doc/Lifespan_Analysis.md
In n-mounier/bGWAS: Bayesian Genome-Wide Association Study
Lifespan Analysis
- Description
- Risk factors (Prior GWASs) used
- Results - BF
- Results - Posterior Effects
- Results - Direct Effects
Description
In this example, we will use the data from Timmers et
al to apply our Bayesian GWAS
approach to study lifespan.
Here, we assume that the bGWAS package is already installed, that the
Z-matrix files have already been downloaded and stored in
"~/ZMatrices". If that is not the case, please follow the steps
described here.
library(bGWAS) # bGWAS github version: v.1.0.2
# Download data to working directory (~460 MB) if not already here
if(!file.exists("lifegen_phase2_bothpl_alldr_2017_09_18.tsv.gz")) download.file(url = "https://datashare.is.ed.ac.uk/bitstream/handle/10283/3209/lifegen_phase2_bothpl_alldr_2017_09_18.tsv.gz?sequence=1&isAllowed=y", destfile = "lifegen_phase2_bothpl_alldr_2017_09_18.tsv.gz")
Now that we have the data in our working directory, we can launch the
analysis (with default parameters):
Lifespan_bGWAS = bGWAS(name = "Lifespan_Timmers2019",
GWAS = "lifegen_phase2_bothpl_alldr_2017_09_18.tsv.gz")
See log
Lifespan_bGWAS = bGWAS(name = "Lifespan_Timmers2019",
GWAS = "lifegen_phase2_bothpl_alldr_2017_09_18.tsv.gz")
## <<< Preparation of analysis >>>
## > Checking parameters
## The name of your analysis is: "Lifespan_Timmers2019".
## The Z-Matrix files are stored in "/Users/nmounier/ZMatrices".
## # Preparation of the data...
## The conventional GWAS used as input is: "lifegen_phase2_bothpl_alldr_2017_09_18.tsv.gz".
## SNPID column, ok - ALT column, ok - REF column, ok - BETA column, ok - SE column, ok
## Posterior effects will be rescaled using BETA and SE.The analysis will be run in the folder: "/Users/nmounier/Documents/SGG/Projects/Packaging/bGWAS/doc".
## The p-value threshold used for selecting MR instruments is: 1e-06.
## The minimum number instruments required for each trait is: 3.
## The distance used for pruning MR instruments is: 500Kb.
## Distance-based pruning will be used for MR instruments.
## No shrinkage applied before performing MR.The p-value threshold used for stepwise selection will be derived according to the number of Prior GWASs used.
## Using MR_shrinkage as default for prior_shrinkage:No shrinkage applied before performing calculating the prior.The p-value threshold used for stepwise selection will be derived according to the number of Prior GWASs used.
## Significant SNPs will be identified according to p-value. The threshold used is :5e-08.
## The distance used for pruning results is: 500Kb.
## Distance-based pruning will be used for results.
## <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
## <<< Identification of significant prior GWASs for MR >>>
## > Creating the Z-Matrix of strong instruments
## # Loading the ZMatrix...
## Selecting studies :
## 38 studies
## 209,840 SNPs
## # Adding data from the conventional GWAS :
## "lifegen_phase2_bothpl_alldr_2017_09_18.tsv.gz"
## Done!
## 197,681 SNPs in common between prior studies and the conventional GWAS
## # Thresholding...
## 134,807 SNPs left after thresholding
## Neuroticism (GPC) - Smoking - ever smoked (TAG) - Smoking - age of onset (TAG) : removed (less than 3 instrument after thresholding)
## 35 studies left after thresholding
## Pruning MR instruments...
## distance : 500Kb
## 1,934 SNPs left after pruning
## Anorexia (GCAN) - Openness to Experience (GPC) - Extraversion (GPC) - Insulin (MAGIC) - 2010 - HOMA-IR (MAGIC) - Depression (PGC) - Autism (PGC) - Smoking - cigarettes per day (TAG) - Smoking - former smoker (TAG) : removed (less than 3 strong instrument after pruning)
## 26 studies left after thresholding+pruning
## 1,927 SNPs left after removing studies with only one strong instrument
## > Performing MR
## #Preparation of the MR analyses to identify significant studies...
## Conventionnal GWAS of interest : lifegen_phase2_bothpl_alldr_2017_09_18.tsv.gz
## # Univariate regressions for each trait...
## Number of trait-specific instruments per univariate regression:
## . Body Mass Index (GIANT) : 89
## . Schizophrenia (PGC) - 2014 : 6
## . Coronary Artery Disease (CARDIoGRAM) : 12
## . Type 2 Diabetes (DIAGRAM) : 12
## . Years of Schooling (SSGAC) : 80
## . Glucose (ENGAGE) : 11
## . Crohns Disease (IBD) : 43
## . Ulcerative Colitis (IBD) : 75
## . HDL Cholesterol (GLGC) : 72
## . LDL Cholesterol (GLGC) : 57
## . Total Cholesterol (GLGC) : 73
## . Triglycerides (GLGC) : 51
## . Glucose (MAGIC) - 2010 : 10
## . HOMA-B (MAGIC) : 6
## . Glucose (MAGIC) : 17
## . Insulin (MAGIC) : 5
## . Heart Rate (HRgene) : 14
## . Height (GIANT) : 522
## . Parkinsons : 382
## . Neuroblastoma : 353
## . Multiple Sclerosis : 153
## . Systolic Blood Pressure (ICBP) : 8
## . Diastolic Blood Pressure (ICBP) : 9
## . Schizophrenia (PGC) - 2013 : 19
## . Schizophrenia (PGC) : 107
## . College Completion (SSGAC) : 3
## Done!
## # Stepwise selection (all traits)...
## The p-value threshold used for stepwise selection is 0.0019 (26 Prior GWASs tested).
## Studies tested (reaching p<0.05 in univariate models) :
## Years of Schooling (SSGAC)
## Body Mass Index (GIANT)
## Coronary Artery Disease (CARDIoGRAM)
## Ulcerative Colitis (IBD)
## HDL Cholesterol (GLGC)
## LDL Cholesterol (GLGC)
## Total Cholesterol (GLGC)
## Triglycerides (GLGC)
## Glucose (MAGIC) - 2010
## Glucose (MAGIC)
## Systolic Blood Pressure (ICBP)
## Diastolic Blood Pressure (ICBP)
## Adding the first study :Years of Schooling (SSGAC)
## iteration 1: 1 studies
## #Run model
## #Test if any study can be added with p<0.0019
## Adding one study :LDL Cholesterol (GLGC)
## Done!
## #Update model
## #Test if any study has p>0.0019 now
## iteration 2: 2 studies
## #Run model
## #Test if any study can be added with p<0.0019
## Adding one study :Body Mass Index (GIANT)
## Done!
## #Update model
## #Test if any study has p>0.0019 now
## iteration 3: 3 studies
## #Run model
## #Test if any study can be added with p<0.0019
## Adding one study :Coronary Artery Disease (CARDIoGRAM)
## Done!
## #Update model
## #Test if any study has p>0.0019 now
## iteration 4: 4 studies
## #Run model
## #Test if any study can be added with p<0.0019
## Adding one study :Diastolic Blood Pressure (ICBP)
## Done!
## #Update model
## #Test if any study has p>0.0019 now
## iteration 5: 5 studies
## #Run model
## #Test if any study can be added with p<0.0019
## #Update model
## #Test if any study has p>0.0019 now
## It converged!
## # Final regression...
## The studies used are:
## - Years of Schooling (SSGAC)
## - LDL Cholesterol (GLGC)
## - Body Mass Index (GIANT)
## - Coronary Artery Disease (CARDIoGRAM)
## - Diastolic Blood Pressure (ICBP)
## Estimating adjusted R-squared:
## - in-sample adjusted R-squared for the all-chromosomes multivariate regression is 0.5435
## - out-of-sample R-squared (masking one chromosome at a time), for the multivariate regression will be estimated when calculating the prior.
## <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
## <<< Estimation of the prior >>>
## > Creating the full Z-Matrix
## # Loading the ZMatrix...
## Selecting studies :
## 5 studies
## 6,811,310 SNPs
## # Adding data from the conventional GWAS :
## "lifegen_phase2_bothpl_alldr_2017_09_18.tsv.gz"
## Done!
## 6,513,704 SNPs in common between prior studies and the conventional GWAS
## > Computing prior
## # Calculating the prior chromosome by chromosome...
## Chromosome 1
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## Chromosome 2
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## Chromosome 3
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## Chromosome 4
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## Chromosome 5
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## Chromosome 6
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## Chromosome 7
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## Chromosome 8
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## Chromosome 9
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## Chromosome 10
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## Chromosome 11
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## Chromosome 12
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## Chromosome 13
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## Chromosome 14
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## Chromosome 15
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## Chromosome 16
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## Chromosome 17
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## Chromosome 18
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## Chromosome 19
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## Chromosome 20
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## Chromosome 21
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## Chromosome 22
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## ## Out-of-sample R-squared for MR instruments across all chromosomes is 0.5298
## ## Out-of-sample squared correlation for MR instruments across all chromosome is 0.53
## ## Correlation between prior and observed effects for all SNPs is 0.1953
## ## Correlation between prior and observed effects for SNPs with GWAS p-value < 0.001 is 0.6141
## Done!
## <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
## <<< Calculation of Bayes Factors and p-values >>>
## > Calculating them for all SNPs
## # Computing observed Bayes Factor for all SNPs...
## Done!
## # Computing BF p-values...
## using a distribution approach:
## ... getting approximated p-values using non-linear quantiles
## ... checking p-values near significance threshold
## 9 p-values have been re-estimated using the exact formula.
## # Estimating p-values for posterior effects...
## Done!
## # Estimating p-values for direct effects...
## Done!
## > Pruning and identifying significant SNPs
## Identification based on BFs
## Starting with 6,513,704 SNPs
## # Selecting significant SNPs according to p-values...
## 871 SNPs left
## Done!
## # Pruning significant SNPs...
## distance : 500Kb
## 28 SNPs left
## Done!
## Identification based on posterior effects
## Starting with 6,513,704 SNPs
## # Selecting significant SNPs according to p-values...
## 975 SNPs left
## Done!
## # Pruning significant SNPs...
## distance : 500Kb
## 28 SNPs left
## Done!
## Identification based on direct effects
## Starting with 6,513,704 SNPs
## # Selecting significant SNPs according to p-values...
## 166 SNPs left
## Done!
## # Pruning significant SNPs...
## distance : 500Kb
## 4 SNPs left
## Done!
## <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
## Time of the analysis: 94 minute(s) and 15 second(s).
We can now look at the results more in details.
Risk factors (Prior GWASs) used
coefficients_plot_bGWAS(Lifespan_bGWAS)
5 risk factors are used to create the prior, the multivariate causal
effect estimates are consistent with what we would expect. On this
figure, the multivariate causal effect estimate and the 95% interval
from the multivariate MR model using all chromosomes (black dot and
bars) as well as the 22 per-chromosome estimates (grey bars) are
represented for each prior GWASs. Coronary Artery Disease (CAD) has the
strongest negative effect on lifespan. High Diastolic Blood Pressure
(DBP) and Body Mass Index (BMI) also decreases lifespan. We can also see
that education, in this case the number of years of schooling, has a
positive effect on lifespan.
Overall, the squared correlation between prior and observed effects is
about 0.038 and goes up to 0.377 when we consider only SNPs having at
least a moderate effect on lifespan (observed p-value \< 0.001).
Using the previous version (Timmers et al), squared correlation was
around 0.003 when considering all SNPs and around 0.082 for SNPs having
a moderate effect.
Results - BF
All significant hits
With this approach, we identified 28 SNPs affecting lifespan through the
selected risk factors:
# all hits
extract_results_bGWAS(Lifespan_bGWAS) %>%
mutate(BF = as.character(format(BF, scientific=T, digits=3)), BF_p = as.character(format(BF_p, scientific=T, digits=3))) %>%
arrange(chrm_UK10K, pos_UK10K) -> Hits
knitr::kable(Hits, digits=3)
| rsid | chrm_UK10K | pos_UK10K | alt | ref | beta | se | z_obs | mu_prior_estimate | mu_prior_std_error | beta_prior_estimate | beta_prior_std_error | BF | BF_p |
| :--------- | ----------: | ---------: | :-- | :-- | ------: | ----: | ------: | ------------------: | --------------------: | --------------------: | ----------------------: | :------- | :------- |
| rs646776 | 1 | 109818530 | T | C | -0.023 | 0.005 | -4.908 | -4.822 | 0.748 | -0.022 | 0.003 | 1.36e+05 | 1.21e-11 |
| rs1230666 | 1 | 114173410 | A | G | -0.032 | 0.006 | -5.805 | -1.433 | 0.527 | -0.008 | 0.003 | 1.03e+04 | 6.99e-10 |
| rs7536152 | 1 | 154423909 | A | G | -0.018 | 0.004 | -4.627 | -1.636 | 0.544 | -0.006 | 0.002 | 1.24e+03 | 2.86e-08 |
| rs6719980 | 2 | 651507 | T | C | -0.028 | 0.005 | -5.407 | -1.906 | 0.581 | -0.010 | 0.003 | 1.97e+04 | 2.45e-10 |
| rs1275922 | 2 | 26932887 | A | G | -0.026 | 0.004 | -5.817 | -0.850 | 0.515 | -0.004 | 0.002 | 1.15e+03 | 3.31e-08 |
| rs7599488 | 2 | 60718347 | T | C | -0.018 | 0.004 | -4.663 | -1.956 | 0.521 | -0.008 | 0.002 | 2.62e+03 | 7.32e-09 |
| rs13082711 | 3 | 27537909 | T | C | 0.020 | 0.005 | 4.428 | 1.954 | 0.616 | 0.009 | 0.003 | 1.67e+03 | 1.65e-08 |
| rs2271961 | 3 | 49878113 | T | C | 0.015 | 0.004 | 3.824 | 3.085 | 0.572 | 0.012 | 0.002 | 1.06e+03 | 3.87e-08 |
| rs61348208 | 4 | 3089564 | T | C | 0.023 | 0.004 | 5.823 | 1.479 | 0.502 | 0.006 | 0.002 | 1.10e+04 | 6.32e-10 |
| rs9393691 | 6 | 26272829 | T | C | -0.022 | 0.004 | -5.570 | -1.565 | 0.505 | -0.006 | 0.002 | 8.18e+03 | 1.03e-09 |
| rs4580876 | 6 | 98322872 | A | G | 0.016 | 0.004 | 4.112 | 2.915 | 0.526 | 0.011 | 0.002 | 2.37e+03 | 8.76e-09 |
| rs10455872 | 6 | 161010118 | A | G | 0.076 | 0.007 | 10.282 | 2.150 | 0.606 | 0.016 | 0.005 | 2.42e+12 | 2.85e-22 |
| rs11556924 | 7 | 129663496 | T | C | 0.020 | 0.004 | 5.062 | 3.276 | 0.648 | 0.013 | 0.003 | 1.00e+05 | 1.93e-11 |
| rs10104032 | 8 | 9616664 | A | C | -0.017 | 0.004 | -4.232 | -2.169 | 0.533 | -0.009 | 0.002 | 1.31e+03 | 2.61e-08 |
| rs11986845 | 8 | 10691318 | T | C | -0.017 | 0.004 | -4.237 | -2.428 | 0.544 | -0.010 | 0.002 | 1.96e+03 | 1.23e-08 |
| rs59234174 | 9 | 16730258 | T | C | -0.028 | 0.005 | -5.127 | -1.492 | 0.508 | -0.008 | 0.003 | 2.38e+03 | 8.70e-09 |
| rs1333045 | 9 | 22119195 | T | C | 0.024 | 0.004 | 6.256 | 2.981 | 0.844 | 0.012 | 0.003 | 1.05e+07 | 1.62e-14 |
| rs2519093 | 9 | 136141870 | T | C | -0.022 | 0.005 | -4.517 | -2.275 | 0.596 | -0.011 | 0.003 | 3.62e+03 | 4.13e-09 |
| rs10841520 | 12 | 20586395 | T | C | 0.024 | 0.005 | 4.944 | 1.334 | 0.522 | 0.006 | 0.003 | 1.08e+03 | 3.75e-08 |
| rs10849925 | 12 | 111495518 | A | G | 0.021 | 0.004 | 5.193 | 1.881 | 0.605 | 0.008 | 0.002 | 1.11e+04 | 6.24e-10 |
| rs11065979 | 12 | 112059557 | T | C | -0.028 | 0.004 | -7.128 | -2.227 | 0.719 | -0.009 | 0.003 | 3.19e+07 | 3.06e-15 |
| rs17630235 | 12 | 112591686 | A | G | -0.026 | 0.004 | -6.503 | -1.884 | 0.713 | -0.007 | 0.003 | 1.06e+06 | 5.27e-13 |
| rs8042849 | 15 | 78817929 | T | C | 0.044 | 0.004 | 10.659 | 0.265 | 0.499 | 0.001 | 0.002 | 6.99e+05 | 9.90e-13 |
| rs8039305 | 15 | 91422543 | T | C | 0.025 | 0.004 | 6.414 | 1.473 | 0.550 | 0.006 | 0.002 | 6.40e+04 | 3.86e-11 |
| rs12924886 | 16 | 72075593 | A | T | 0.028 | 0.005 | 5.679 | 2.455 | 0.522 | 0.012 | 0.003 | 1.51e+05 | 1.03e-11 |
| rs6511720 | 19 | 11202306 | T | G | 0.034 | 0.006 | 5.631 | 3.787 | 0.754 | 0.023 | 0.005 | 2.07e+06 | 1.89e-13 |
| rs12459965 | 19 | 18452195 | T | C | 0.020 | 0.004 | 4.426 | 2.781 | 0.533 | 0.012 | 0.002 | 5.51e+03 | 2.00e-09 |
| rs429358 | 19 | 45411941 | T | C | 0.106 | 0.005 | 19.328 | 1.854 | 0.712 | 0.010 | 0.004 | 1.11e+37 | 7.06e-70 |
New hits
# new hits (compared to conventional GWAS)
# look at SNPs in a 100kb window around (to assess significance in conventional GWAS)
Hits$NewHits = NA
dist=100000
for(snp in 1:nrow(Hits)){
Hits %>% dplyr::slice(snp) %>% pull(chrm_UK10K) -> chr
Hits %>% dplyr::slice(snp) %>% pull(pos_UK10K) -> pos
extract_results_bGWAS(Lifespan_bGWAS, SNPs = "all") %>%
filter(chrm_UK10K == chr,
pos_UK10K > pos - dist,
pos_UK10K < pos + dist) %>%
mutate(p_obs = 2*pnorm(-abs(z_obs))) %>%
pull(p_obs) %>% min() -> minP_region
Hits$NewHits[snp] = ifelse(minP_region<5e-8,FALSE,TRUE)
}
Hits %>%
filter(NewHits == TRUE) %>%
mutate(NewHits = NULL)-> New_Hits
# also add gene names using annovar
suppressWarnings(suppressMessages(source(system.file("Scripts/Get_GenesAndTraits.R", package="bGWAS"))))
Gene_Info <- do.call(rbind.data.frame,
apply(New_Hits, 1, function(x) get_geneInfo(as.numeric(x[2]), as.numeric(x[3]), x[4], x[5])))
# clean some names for intergenic regions
knitr::kable(Gene_Info)
| Function | Gene | Distance |
| :--------- | :------------------- | :------------ |
| downstream | CELSR2 | 157 |
| intronic | IL6R | 0 |
| intergenic | LOC105373352/TMEM18 | 89415/12371 |
| intronic | BCL11A | 0 |
| intergenic | SLC4A7/EOMES | 12034/219531 |
| intronic | TRAIP | 0 |
| intergenic | LOC101927314/MIR2113 | 166079/149535 |
| exonic | ZC3HC1 | 0 |
| intronic | TNKS | 0 |
| intronic | PINX1 | 0 |
| intronic | BNC2 | 0 |
| intronic | ABO | 0 |
| intronic | PDE3A | 0 |
| intronic | CUX2 | 0 |
| intronic | PGPEP1 | 0 |
# intergenic LOC105373352/TMEM18 89415/12371 -> TMEM18
Gene_Info[3,2:3] = c("TMEM18", "12371")
# intergenic SLC4A7/EOMES 12034/219531
Gene_Info[5,2:3] = c("SLC4A7", "12034")
# intergenic LOC101927314/MIR2113 166079/149535 -> MIR2113
Gene_Info[7,2:3] = c("MIR2113", "149535")
Gene_Info %>%
bind_cols(New_Hits) -> New_Hits
knitr::kable(New_Hits, digits=3)
| Function | Gene | Distance | rsid | chrm_UK10K | pos_UK10K | alt | ref | beta | se | z_obs | mu_prior_estimate | mu_prior_std_error | beta_prior_estimate | beta_prior_std_error | BF | BF_p |
| :--------- | :------ | :------- | :--------- | ----------: | ---------: | :-- | :-- | ------: | ----: | ------: | ------------------: | --------------------: | --------------------: | ----------------------: | :------- | :------- |
| downstream | CELSR2 | 157 | rs646776 | 1 | 109818530 | T | C | -0.023 | 0.005 | -4.908 | -4.822 | 0.748 | -0.022 | 0.003 | 1.36e+05 | 1.21e-11 |
| intronic | IL6R | 0 | rs7536152 | 1 | 154423909 | A | G | -0.018 | 0.004 | -4.627 | -1.636 | 0.544 | -0.006 | 0.002 | 1.24e+03 | 2.86e-08 |
| intergenic | TMEM18 | 12371 | rs6719980 | 2 | 651507 | T | C | -0.028 | 0.005 | -5.407 | -1.906 | 0.581 | -0.010 | 0.003 | 1.97e+04 | 2.45e-10 |
| intronic | BCL11A | 0 | rs7599488 | 2 | 60718347 | T | C | -0.018 | 0.004 | -4.663 | -1.956 | 0.521 | -0.008 | 0.002 | 2.62e+03 | 7.32e-09 |
| intergenic | SLC4A7 | 12034 | rs13082711 | 3 | 27537909 | T | C | 0.020 | 0.005 | 4.428 | 1.954 | 0.616 | 0.009 | 0.003 | 1.67e+03 | 1.65e-08 |
| intronic | TRAIP | 0 | rs2271961 | 3 | 49878113 | T | C | 0.015 | 0.004 | 3.824 | 3.085 | 0.572 | 0.012 | 0.002 | 1.06e+03 | 3.87e-08 |
| intergenic | MIR2113 | 149535 | rs4580876 | 6 | 98322872 | A | G | 0.016 | 0.004 | 4.112 | 2.915 | 0.526 | 0.011 | 0.002 | 2.37e+03 | 8.76e-09 |
| exonic | ZC3HC1 | 0 | rs11556924 | 7 | 129663496 | T | C | 0.020 | 0.004 | 5.062 | 3.276 | 0.648 | 0.013 | 0.003 | 1.00e+05 | 1.93e-11 |
| intronic | TNKS | 0 | rs10104032 | 8 | 9616664 | A | C | -0.017 | 0.004 | -4.232 | -2.169 | 0.533 | -0.009 | 0.002 | 1.31e+03 | 2.61e-08 |
| intronic | PINX1 | 0 | rs11986845 | 8 | 10691318 | T | C | -0.017 | 0.004 | -4.237 | -2.428 | 0.544 | -0.010 | 0.002 | 1.96e+03 | 1.23e-08 |
| intronic | BNC2 | 0 | rs59234174 | 9 | 16730258 | T | C | -0.028 | 0.005 | -5.127 | -1.492 | 0.508 | -0.008 | 0.003 | 2.38e+03 | 8.70e-09 |
| intronic | ABO | 0 | rs2519093 | 9 | 136141870 | T | C | -0.022 | 0.005 | -4.517 | -2.275 | 0.596 | -0.011 | 0.003 | 3.62e+03 | 4.13e-09 |
| intronic | PDE3A | 0 | rs10841520 | 12 | 20586395 | T | C | 0.024 | 0.005 | 4.944 | 1.334 | 0.522 | 0.006 | 0.003 | 1.08e+03 | 3.75e-08 |
| intronic | CUX2 | 0 | rs10849925 | 12 | 111495518 | A | G | 0.021 | 0.004 | 5.193 | 1.881 | 0.605 | 0.008 | 0.002 | 1.11e+04 | 6.24e-10 |
| intronic | PGPEP1 | 0 | rs12459965 | 19 | 18452195 | T | C | 0.020 | 0.004 | 4.426 | 2.781 | 0.533 | 0.012 | 0.002 | 5.51e+03 | 2.00e-09 |
15 of the 28 genome-wide significant loci are missed by the conventional
GWAS (using same p-value threshold of 5e-8 to assess significance).
Using the previous version (Timmers et al), we identified 7 new loci
(using a threshold of 2.5e-8 for both GWAS and bGWAS results), 4 of them
are also significant in this analysis (near CELSR2, TMEM18, ZC3HC1 and
ABO). The 11 other variants identified in this analysis (near IL6R,
BCL11A, SLC4A7, TRAIP, MIR2113, PINX1, TNKS, BNC2, CUX2, PDE3A and
PGPEP1) are reported to be associated with lifespan for the first time.
# For the plots, we will use only the new hits
New_Hits %>%
transmute(rs=rsid,
gene = Gene,
color="#932735") -> my_SNPs
manhattan_plot_bGWAS(Lifespan_bGWAS, SNPs=my_SNPs)
New hits, association with risk factors
my_SNPs %>%
mutate(color=NULL) -> my_SNPs
heatmap_bGWAS(Lifespan_bGWAS, SNPs = my_SNPs)
On this figure, the contribution of each risk factor to the prior
effects of new hits (alleles aligned to be life-lengthening) is
represented as a heatmap. Overall, we observe a lot of red, as expected
since alleles are aligned to be life-lengthening.
Among these 15 new variants, 8 were known to be associated with at least
one of the RFs (indicated with a star on the heatmap - variant near
CELSR2 associated with LDL cholesterol, variants near TMEM18 and PGPEP1
associated with Body Mass Index, variants near BCL11A, TRAIP and MIR2113
associated with Years of Schooling, variant near ZC3HC1 associated with
Coronary Artery Disease and variant near ABO associated with both LDL
cholesterol and Coronary Artery Disease). 7 variants (near IL6R, SLC4A7,
PINX1, TNKS, BNC2, CUX2 and PDE3A) are not associated with any of the
RFs (at least not in the summary statistics used to create the prior),
suggesting that they could be acting on lifespan through smaller
pleiotropic effects on several RFs.
These variants (and the ones in a 100kb window) can be further
investigated using the GWAS Catalog. R2
estimates in EUR population from LDlink
are used to keep only SNPs in LD (R2>0.1) with the variant identified.
SNP-trait associations p-values below 5e-8 are reported below (when
LD-friends are used, p-values adjusted for the correlation between the
variants).
Reported Associations
##
## * SNP - rs7536152 (IL6R):
##
##
## snp chrm posh19 LD_R2 LD_alleles trait p adjusted_p effect gene url
## -------------- ----- ---------- ------ ---------------------------- --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ------ ----------- ---------------------------- ----- -------------------------------------
## rs6689306-A 1 154395946 0.902 rs6689306(G)/rs7536152(G) Atrial fibrillation 1e-18 4.76e-17 [1.05-1.08] IL6R www.ncbi.nlm.nih.gov/pubmed/30061737
## rs146402667-G 1 154398553 0.616 rs34280647(AC)/rs7536152(G) Blood protein levels 0e+00 0.00e+00 [0.91-0.99] unit increase IL6R www.ncbi.nlm.nih.gov/pubmed/29875488
## rs4129267-C 1 154426264 0.462 rs4129267(T)/rs7536152(G) C-reactive protein levels 2e-48 2.72e-23 [0.07-0.09] unit increase IL6R www.ncbi.nlm.nih.gov/pubmed/21300955
## rs2228145-C 1 154426970 0.462 rs2228145(C)/rs7536152(G) Cerebrospinal fluid biomarker levels 7e-29 3.39e-14 IL6R www.ncbi.nlm.nih.gov/pubmed/28031287
## rs61812598-G 1 154420087 0.464 rs61812598(G)/rs7536152(A) Cerebrospinal fluid levels of Alzheimer's disease-related proteins 6e-63 3.68e-30 IL6R www.ncbi.nlm.nih.gov/pubmed/25340798
## rs4129267-? 1 154426264 0.462 rs4129267(T)/rs7536152(G) Chronic inflammatory diseases (ankylosing spondylitis, Crohn's disease, psoriasis, primary sclerosing cholangitis, ulcerative colitis) (pleiotropy) 9e-18 5.29e-09 IL6R www.ncbi.nlm.nih.gov/pubmed/26974007
## rs4845625-T 1 154422067 1.000 rs4845625(T)/rs7536152(A) Coronary artery disease 6e-16 6.00e-16 [0.034-0.056] unit increase IL6R www.ncbi.nlm.nih.gov/pubmed/29212778
## rs6689306-A 1 154395946 0.902 rs6689306(G)/rs7536152(G) Coronary artery disease (myocardial infarction, percutaneous transluminal coronary angioplasty, coronary artery bypass grafting, angina or chromic ischemic heart disease) 2e-09 1.22e-08 [1.03-1.07] IL6R www.ncbi.nlm.nih.gov/pubmed/28714975
## rs4129267-T 1 154426264 0.462 rs4129267(T)/rs7536152(G) Fibrinogen 6e-27 2.70e-13 [0.009-0.013] unit decrease IL6R www.ncbi.nlm.nih.gov/pubmed/23969696
## rs61812598-A 1 154420087 0.464 rs61812598(G)/rs7536152(A) Fibrinogen levels 3e-36 1.07e-17 NR unit decrease IL6R www.ncbi.nlm.nih.gov/pubmed/26561523
## rs4129267-? 1 154426264 0.462 rs4129267(T)/rs7536152(G) Protein quantitative trait loci 2e-57 1.79e-27 IL6R www.ncbi.nlm.nih.gov/pubmed/18464913
##
##
## * SNP - rs13082711 (SLC4A7):
##
##
## snp chrm posh19 LD_R2 LD_alleles trait p adjusted_p effect gene url
## ------------- ----- --------- ------ --------------------------- --------------------------------------------------------------------- ------ ----------- -------------------------- ---------------------- -------------------------------------
## rs13082711-T 3 27537909 NA NA Blood pressure 5e-09 NA [0.22-0.45] mmHg decrease AC099535.1 - RNU1-96P www.ncbi.nlm.nih.gov/pubmed/21909110
## rs13082711-C 3 27537909 NA NA Diastolic blood pressure 3e-10 NA [0.23-0.43] unit increase AC099535.1 - RNU1-96P www.ncbi.nlm.nih.gov/pubmed/28739976
## rs13082711-? 3 27537909 NA NA Diastolic blood pressure (cigarette smoking interaction) 7e-12 NA AC099535.1 - RNU1-96P www.ncbi.nlm.nih.gov/pubmed/29455858
## rs13082711-? 3 27537909 NA NA Systolic blood pressure (cigarette smoking interaction) 3e-11 NA AC099535.1 - RNU1-96P www.ncbi.nlm.nih.gov/pubmed/29455858
## rs2643826-T 3 27562988 0.309 rs2643826(T)/rs13082711(T) Systolic blood pressure x alcohol consumption interaction (2df test) 6e-32 6.14e-11 RNU1-96P - AC137675.1 www.ncbi.nlm.nih.gov/pubmed/29912962
##
##
## * SNP - rs10104032 (TNKS):
##
##
## snp chrm posh19 LD_R2 LD_alleles trait p adjusted_p effect gene url
## ------------ ----- -------- ------ --------------------------- ----------------------------------------------------------------------------------------- ------ ----------- ---------------------------- ----------------- -------------------------------------
## rs9286060-A 8 9653145 0.702 rs9286060(C)/rs10104032(C) Diastolic blood pressure x alcohol consumption interaction (2df test) 2e-13 7.46e-10 TNKS - LINC00599 www.ncbi.nlm.nih.gov/pubmed/29912962
## rs4383974-? 8 9619348 0.704 rs4383974(G)/rs10104032(A) Heel bone mineral density 2e-17 1.02e-12 [0.014-0.023] unit decrease TNKS www.ncbi.nlm.nih.gov/pubmed/30048462
## rs1976671-A 8 9679634 0.642 rs1976671(G)/rs10104032(C) Systolic blood pressure x alcohol consumption interaction (2df test) 7e-18 5.14e-12 TNKS - LINC00599 www.ncbi.nlm.nih.gov/pubmed/29912962
## rs4841235-? 8 9683358 0.538 rs4841235(G)/rs10104032(C) Systolic blood pressure x smoking status (current vs non-current) interaction (2df test) 5e-15 9.54e-09 TNKS - LINC00599 www.ncbi.nlm.nih.gov/pubmed/29455858
## rs4841235-? 8 9683358 0.538 rs4841235(G)/rs10104032(C) Systolic blood pressure x smoking status (ever vs never) interaction (2df test) 4e-14 2.96e-08 TNKS - LINC00599 www.ncbi.nlm.nih.gov/pubmed/29455858
##
##
## * SNP - rs11986845 (PINX1):
##
##
## snp chrm posh19 LD_R2 LD_alleles trait p adjusted_p effect gene url
## ------------ ----- --------- ------ --------------------------- ---------------------------------------------------------------------- ------ ----------- -------------------------- ------------- -------------------------------------
## rs4551304-A 8 10665069 0.723 rs4551304(G)/rs11986845(T) Diastolic blood pressure x alcohol consumption interaction (2df test) 2e-14 7.82e-11 PINX1, PINX1 www.ncbi.nlm.nih.gov/pubmed/29912962
## rs1821002-G 8 10640065 0.660 rs1821002(G)/rs11986845(T) Systolic blood pressure 4e-19 3.83e-13 [0.33-0.52] unit decrease PINX1, PINX1 www.ncbi.nlm.nih.gov/pubmed/28135244
## rs7814757-T 8 10675188 0.716 rs7814757(T)/rs11986845(T) Systolic blood pressure x alcohol consumption interaction (2df test) 3e-22 2.28e-16 PINX1, PINX1 www.ncbi.nlm.nih.gov/pubmed/29912962
##
##
## * SNP - rs59234174 (BNC2):
##
## No association reported
##
## * SNP - rs10841520 (PDE3A):
##
## No association reported
##
## * SNP - rs10849925 (CUX2):
##
## No association reported
Interestingly, we can see that a few loci identified have been
associated with some of the risk factors used to create the prior in
more recent studies. Variants in LD with the variant identified near
IL6R have been associated with Coronary Artery Disease. Variants in LD
with the variants identified near SLC4A7, PINX1 and TNKS have been
associated with Diastolic Blood Pressure. The other loci have not been
associated with any of the risk factors, and are likely acting on
lifespan through moderate effects on several risk factors (pleiotropic
effects).
Comparison with Timmers et al results
# Download data to working directory (~230 MB) if not already here
if(!file.exists("bGWAS_Timmers2019/bGWAS_Timmers2019.csv.gz")){
download.file(url = "https://drive.switch.ch/index.php/s/zlNLUSCUyfgovcp/download", destfile = "bGWAS_Timmers2019.tar.gz")
system("tar -xzvf bGWAS_Timmers2019.tar.gz") }
Data_Timmers = data.table::fread("bGWAS_Timmers2019/bGWAS_Timmers2019.csv.gz")
All_Results = extract_results_bGWAS(Lifespan_bGWAS, SNPs = "all")
# combine results
All_Results %>%
inner_join(Data_Timmers, by="rsid", suffix=c("", "_Timmers")) -> All_Results
# keep SNPs significant in one of the two analyses
# (note that in Timmers et al paper a different threshold was used because of multiple analyses)
All_Results %>%
filter(BF_p < 5e-8 | pvalue < 2.5e-8) -> Combined_Results
# identify lead SNP in each region (distance pruning)
prune_byDistance = getFromNamespace("prune_byDistance", "bGWAS")
Combined_Results %>%
transmute(SNP = rsid,
chr_name = chrm_UK10K,
chr_start = pos_UK10K,
pval.exposure = pmin(pvalue, BF_p)) -> ToPrune
SNPsToKeep = prune_byDistance(ToPrune, prune.dist=500, byP=T)
Combined_Results %>%
filter(rsid %in% SNPsToKeep) %>%
transmute(rsid,
chr= chrm_UK10K,
pos = pos_UK10K,
obs = z_obs,
# align alleles to be able to compare directions
prior_new = mu_prior_estimate,
prior_Timmers = case_when(
a1 == alt ~ prior_estimate,
TRUE ~ -prior_estimate),
BF_new = BF,
BF_Timmers,
p_new = BF_p,
p_Timmers = pvalue,
Significance = case_when(
p_new > 5e-8 ~ "Timmers et al only",
p_Timmers > 2.5e-8 ~ "new signal",
TRUE ~ "significant in both analyses"
)) -> Combined_Results
nrow(Combined_Results %>% filter(p_new<5e-8))
## [1] 23
# here we do have less new hits
Hits %>% filter(!rsid %in% Combined_Results$rsid) %>% pull(rsid) -> Missing_SNPs
Missing_SNPs %in% Data_Timmers$rsid
## [1] FALSE FALSE FALSE FALSE FALSE
# this is because we are only looking at variants present in both analyses
# these variants where not included in Timmers et al (i.e no prior effect could
# be estimated) because when creating the Z-Matrices, variants with low imputation
# quality for any RF were excluded instead of set to 0 as they are now
knitr::kable(Combined_Results, digits=3)
| rsid | chr | pos | obs | prior_new | prior_Timmers | BF_new | BF_Timmers | p_new | p_Timmers | Significance |
| :---------- | --: | --------: | ------: | ---------: | -------------: | -----------: | :------------------- | -----: | ---------: | :--------------------------- |
| rs429358 | 19 | 45411941 | 19.328 | 1.854 | 4.026 | 1.110463e+37 | 29171248738146925e45 | 0 | 0 | significant in both analyses |
| rs10455872 | 6 | 161010118 | 10.282 | 2.150 | 2.534 | 2.424978e+12 | 492671224373715e3 | 0 | 0 | significant in both analyses |
| rs1333045 | 9 | 22119195 | 6.256 | 2.981 | 2.743 | 1.050057e+07 | 20029321.026961345 | 0 | 0 | significant in both analyses |
| rs6511720 | 19 | 11202306 | 5.631 | 3.787 | 3.804 | 2.071058e+06 | 2507806.256755927 | 0 | 0 | significant in both analyses |
| rs8042849 | 15 | 78817929 | 10.659 | 0.265 | 1.175 | 6.987773e+05 | 148475882457531e5 | 0 | 0 | significant in both analyses |
| rs12924886 | 16 | 72075593 | 5.679 | 2.455 | 1.477 | 1.507003e+05 | 204801.97853190443 | 0 | 0 | significant in both analyses |
| rs646776 | 1 | 109818530 | -4.908 | -4.822 | -6.902 | 1.357283e+05 | 49399.20478889484 | 0 | 0 | significant in both analyses |
| rs11556924 | 7 | 129663496 | 5.062 | 3.276 | 1.446 | 1.001113e+05 | 17649.28405521511 | 0 | 0 | significant in both analyses |
| rs8039305 | 15 | 91422543 | 6.414 | 1.473 | -0.194 | 6.395981e+04 | 50550.771535476524 | 0 | 0 | significant in both analyses |
| rs6719980 | 2 | 651507 | -5.407 | -1.906 | -2.107 | 1.973024e+04 | 173370.50556369557 | 0 | 0 | significant in both analyses |
| rs61348208 | 4 | 3089564 | 5.823 | 1.479 | 0.972 | 1.099280e+04 | 142313.10294079248 | 0 | 0 | significant in both analyses |
| rs1230666 | 1 | 114173410 | -5.805 | -1.433 | 0.000 | 1.033311e+04 | 18404.921566568224 | 0 | 0 | significant in both analyses |
| rs12459965 | 19 | 18452195 | 4.426 | 2.781 | 0.857 | 5.514180e+03 | 920.439727029316 | 0 | 0 | new signal |
| rs2519093 | 9 | 136141870 | -4.517 | -2.275 | -3.020 | 3.620905e+03 | 10857.171015204418 | 0 | 0 | significant in both analyses |
| rs7599488 | 2 | 60718347 | -4.663 | -1.956 | -1.290 | 2.618682e+03 | 3659.2921681571866 | 0 | 0 | new signal |
| rs59234174 | 9 | 16730258 | -5.127 | -1.492 | 0.000 | 2.378208e+03 | 1940.52607021053 | 0 | 0 | new signal |
| rs4580876 | 6 | 98322872 | 4.112 | 2.915 | 1.792 | 2.368247e+03 | 1017.410230589991 | 0 | 0 | new signal |
| rs11986845 | 8 | 10691318 | -4.237 | -2.428 | 0.000 | 1.964372e+03 | 163.1002364768018 | 0 | 0 | new signal |
| rs10104032 | 8 | 9616664 | -4.232 | -2.169 | -0.124 | 1.305137e+03 | 196.65685527856158 | 0 | 0 | new signal |
| rs7536152 | 1 | 154423909 | -4.627 | -1.636 | 0.000 | 1.240998e+03 | 431.52332466696873 | 0 | 0 | new signal |
| rs1275922 | 2 | 26932887 | -5.817 | -0.850 | -0.816 | 1.148873e+03 | 111238.35721435159 | 0 | 0 | significant in both analyses |
| rs10841520 | 12 | 20586395 | 4.944 | 1.334 | 0.000 | 1.075240e+03 | 1081.8164977967003 | 0 | 0 | new signal |
| rs2271961 | 3 | 49878113 | 3.824 | 3.085 | 1.757 | 1.058031e+03 | 406.50704370647395 | 0 | 0 | new signal |
| rs113160991 | 7 | 75094329 | -5.137 | -0.963 | -1.276 | 4.765290e+02 | 19355.298796056322 | 0 | 0 | Timmers et al only |
| rs10211471 | 2 | 237081854 | -4.871 | -0.862 | -1.433 | 2.151440e+02 | 9263.378759875068 | 0 | 0 | Timmers et al only |
| rs111333005 | 6 | 160487196 | -5.496 | -0.183 | -0.614 | 3.618300e+01 | 20202.541832372604 | 0 | 0 | Timmers et al only |
There are 3 variants from Timmers et al that are not significant in
the new analysis. Details about these variants and their association
with the RFs used to create the prior in the Timmers et al analysis
can be found here. The first
(rs113160991, near POM121C) and the second (rs113160991, near
GBX2/ASB18) variants were not significantly associated with any of the
risk factors used to create the prior in Timmers et al and they were
likely acting through small effects on various RFs. In our new analysis,
a different set of RFs is used, leading to a much smaller prior effect.
The third variant (rs111333005, LPA/IGF2R) is known to be significantly
associated with LDL, a RF that was selected to create the prior in both
analyses. However, the multivariate causal effect estimate of HDL on
lifespan is slighlty smaller in our new analysis (-0.09 vs -0.13),
reducing the prior effect. Additionnaly, this variant was also having
small effects on some additional RFs included in Timmers et al
analyses, explaining why the prior effect estimated in our new analysis
is smaller.
Prior Effects Estimates
# abs(prior effects):
p=ggplot2::ggplot(Combined_Results, ggplot2::aes(x=prior_Timmers, y=prior_new, shape=Significance)) +
geom_abline(slope=1, intercept=0, col="darkgrey", lty=2) +
geom_hline(yintercept = 0, size=0.4) +
geom_vline(xintercept = 0, size=0.4) +
ggplot2::geom_point(size=2, alpha=0.6) +
labs(title = "Prior Effects Estimates") +
ylab("bGWAS v.1.0.2") + xlab("Timmers et al") +
apatheme
p
The prior effects are quite consistent (all except 1 agree in sign).
This variant (rs8039305) is still identified as significantly associated
with lifespan by both analyses because of its strong observed effect
(z-score of 6.413708).
The differences that are observed likely come from the fact that
different RFs are selected (update of the stepwise selection approach).
It is also important to note that in the new analysis no shrinkage is
applied before the prior estimation. This also creates some differences
and this is why some of the prior effects from Timmers
Bayes Factors
# abs(prior effects):
p=ggplot2::ggplot(Combined_Results, ggplot2::aes(x=log(as.numeric(BF_Timmers)), y=log(as.numeric(BF_new)), shape=Significance)) +
geom_abline(slope=1, intercept=0, col="darkgrey", lty=2) +
ggplot2::geom_point(size=2, alpha=0.6) +
labs(title = "Bayes Factors (log scale)") +
ylab("bGWAS v.1.0.2") + xlab("Timmers et al") +
apatheme
p
The BFs are also quite consistent. Differences in BFs are directly
correlated to the differences in prior effects, since the observed
effects are the same for both analyses.
P-values
# abs(prior effects):
p=ggplot2::ggplot(Combined_Results, ggplot2::aes(x=-log10(p_Timmers), y=-log10(p_new), shape=Significance)) +
geom_abline(slope=1, intercept=0, col="darkgrey", lty=2) +
ggplot2::geom_point(size=2, alpha=0.6) +
labs(title = "P-values (-log10 scale)") +
ylab("bGWAS v.1.0.2") + xlab("Timmers et al") +
apatheme
p
The
p-values are also quite similar. Differences in p-values can be
explained by the differences in BFs (because each BF value might differ,
but also because the distribution of BFs is different in each analysis,
meaning that for a specific BF value, the respective p-value will
differ). In addition, this is important to note that since the p-values
from Timmers et al were estimated using a permutation approach, the
minimal p-value that could be estimated (1.4e-10) was dependent on the
number of permutations performed.
Results - Posterior Effects
All significant hits
With this approach, we identified 28 SNPs having significant posterior
effects:
# all posterior hits
extract_results_bGWAS(Lifespan_bGWAS, results="posterior") %>%
mutate(p_posterior = as.character(format(p_posterior, scientific=T, digits=3))) %>%
arrange(chrm_UK10K, pos_UK10K) -> Posterior_Hits
knitr::kable(Posterior_Hits, digits=3)
| rsid | chrm_UK10K | pos_UK10K | alt | ref | beta | se | z_obs | mu_posterior_estimate | mu_posterior_std_error | beta_posterior_estimate | beta_posterior_std_error | z_posterior | p_posterior |
| :---------- | ----------: | ---------: | :-- | :-- | ------: | ----: | -------: | ----------------------: | ------------------------: | ------------------------: | --------------------------: | -----------: | :----------- |
| rs629301 | 1 | 109818306 | T | G | -0.022 | 0.005 | -4.699 | -4.824 | 0.589 | -0.022 | 0.003 | -8.196 | 2.48e-16 |
| rs13386964 | 2 | 650828 | A | G | 0.027 | 0.005 | 5.314 | 2.837 | 0.504 | 0.015 | 0.003 | 5.626 | 1.84e-08 |
| rs7599488 | 2 | 60718347 | T | C | -0.018 | 0.004 | -4.663 | -2.534 | 0.462 | -0.010 | 0.002 | -5.484 | 4.15e-08 |
| rs13086611 | 3 | 49385417 | A | T | -0.013 | 0.004 | -3.019 | -3.043 | 0.503 | -0.013 | 0.002 | -6.049 | 1.46e-09 |
| rs2681781 | 3 | 49898273 | A | G | 0.013 | 0.004 | 3.316 | 3.577 | 0.511 | 0.014 | 0.002 | 6.999 | 2.58e-12 |
| rs3135169 | 4 | 3262374 | T | C | 0.017 | 0.004 | 4.398 | 2.512 | 0.451 | 0.010 | 0.002 | 5.563 | 2.65e-08 |
| rs13130484 | 4 | 45175691 | T | C | -0.012 | 0.004 | -3.161 | -3.270 | 0.487 | -0.013 | 0.002 | -6.716 | 1.86e-11 |
| rs3004179 | 6 | 98513032 | A | G | 0.015 | 0.004 | 3.930 | 3.461 | 0.479 | 0.014 | 0.002 | 7.220 | 5.20e-13 |
| rs118039278 | 6 | 160985526 | A | G | -0.076 | 0.007 | -10.275 | -4.283 | 0.512 | -0.032 | 0.004 | -8.366 | 5.97e-17 |
| rs34809719 | 7 | 2028968 | T | G | 0.016 | 0.005 | 3.227 | 2.774 | 0.483 | 0.014 | 0.002 | 5.749 | 8.97e-09 |
| rs11556924 | 7 | 129663496 | T | C | 0.020 | 0.004 | 5.062 | 3.804 | 0.544 | 0.015 | 0.002 | 6.998 | 2.60e-12 |
| rs10104032 | 8 | 9616664 | A | C | -0.017 | 0.004 | -4.232 | -2.626 | 0.470 | -0.011 | 0.002 | -5.583 | 2.37e-08 |
| rs2271355 | 8 | 10692158 | T | C | 0.015 | 0.004 | 3.911 | 2.866 | 0.479 | 0.011 | 0.002 | 5.982 | 2.20e-09 |
| rs1333045 | 9 | 22119195 | T | C | 0.024 | 0.004 | 6.256 | 4.344 | 0.645 | 0.017 | 0.003 | 6.733 | 1.66e-11 |
| rs2519093 | 9 | 136141870 | T | C | -0.022 | 0.005 | -4.517 | -2.862 | 0.512 | -0.014 | 0.003 | -5.593 | 2.23e-08 |
| rs964184 | 11 | 116648917 | C | G | 0.021 | 0.006 | 3.791 | 3.165 | 0.497 | 0.018 | 0.003 | 6.364 | 1.96e-10 |
| rs11065979 | 12 | 112059557 | T | C | -0.028 | 0.004 | -7.128 | -3.897 | 0.584 | -0.015 | 0.002 | -6.677 | 2.45e-11 |
| rs11066309 | 12 | 112883476 | A | G | -0.025 | 0.004 | -6.330 | -3.444 | 0.578 | -0.014 | 0.002 | -5.958 | 2.55e-09 |
| rs1183910 | 12 | 121420807 | A | G | -0.015 | 0.004 | -3.676 | -2.915 | 0.481 | -0.012 | 0.002 | -6.064 | 1.33e-09 |
| rs1029420 | 15 | 91441086 | T | C | 0.023 | 0.004 | 5.738 | 2.848 | 0.499 | 0.011 | 0.002 | 5.701 | 1.19e-08 |
| rs8049439 | 16 | 28837515 | T | C | 0.011 | 0.004 | 2.809 | 2.949 | 0.480 | 0.012 | 0.002 | 6.144 | 8.05e-10 |
| rs1421085 | 16 | 53800954 | T | C | 0.014 | 0.004 | 3.550 | 3.760 | 0.612 | 0.015 | 0.002 | 6.147 | 7.89e-10 |
| rs12924886 | 16 | 72075593 | A | T | 0.028 | 0.005 | 5.679 | 3.146 | 0.463 | 0.015 | 0.002 | 6.798 | 1.06e-11 |
| rs999474 | 17 | 46987665 | A | G | 0.014 | 0.004 | 3.502 | 2.674 | 0.469 | 0.010 | 0.002 | 5.697 | 1.22e-08 |
| rs303757 | 18 | 21078716 | T | G | 0.011 | 0.004 | 2.793 | 2.983 | 0.478 | 0.012 | 0.002 | 6.240 | 4.37e-10 |
| rs6511720 | 19 | 11202306 | T | G | 0.034 | 0.006 | 5.631 | 4.455 | 0.602 | 0.027 | 0.004 | 7.400 | 1.36e-13 |
| rs17724992 | 19 | 18454825 | A | G | -0.019 | 0.004 | -4.402 | -3.170 | 0.471 | -0.014 | 0.002 | -6.725 | 1.76e-11 |
| rs429358 | 19 | 45411941 | T | C | 0.106 | 0.005 | 19.328 | 7.736 | 0.580 | 0.042 | 0.003 | 13.334 | 1.47e-40 |
New hits
# new hits (compared to conventional GWAS & BF p-values)
# look at SNPs in a 100kb window around (to assess significance in conventional GWAS / bGWAS results)
Posterior_Hits$NewHits = NA
dist=100000
for(snp in 1:nrow(Posterior_Hits)){
Posterior_Hits %>% dplyr::slice(snp) %>% pull(chrm_UK10K) -> chr
Posterior_Hits %>% dplyr::slice(snp) %>% pull(pos_UK10K) -> pos
extract_results_bGWAS(Lifespan_bGWAS, SNPs = "all") %>%
filter(chrm_UK10K == chr,
pos_UK10K > pos - dist,
pos_UK10K < pos + dist) %>%
mutate(p_obs = 2*pnorm(-abs(z_obs))) %>%
dplyr::select(p_obs, BF_p) %>% min() -> minP_region
Posterior_Hits$NewHits[snp] = ifelse(minP_region<5e-8,FALSE,TRUE)
}
Posterior_Hits %>%
filter(NewHits == TRUE) %>%
mutate(NewHits = NULL)-> NewPosterior_Hits
# also add gene names using annovar
Gene_Info_Posterior <- do.call(rbind.data.frame,
apply(NewPosterior_Hits, 1, function(x) get_geneInfo(as.numeric(x[2]), as.numeric(x[3]), x[4], x[5])))
# clean some names for intergenic regions
knitr::kable(Gene_Info_Posterior)
| Function | Gene | Distance |
| :--------- | :------------ | :------------ |
| intergenic | USP4/GPX1 | 7931/9187 |
| intergenic | GNPDA2/GABRG1 | 447040/862095 |
| intronic | MAD1L1 | 0 |
| UTR3 | ZPR1 | 0 |
| intronic | HNF1A | 0 |
| intronic | ATXN2L | 0 |
| intronic | FTO | 0 |
| intronic | UBE2Z | 0 |
| intergenic | RIOK3/RMC1 | 15612/4718 |
# intergenic USP4/GPX1 7931/9187 -> USP4
Gene_Info_Posterior[1,2:3] = c("USP4", "7931")
# intergenic GNPDA2/GABRG1 447040/862095 -> GNPDA2 (super far away though ... )
Gene_Info_Posterior[2,2:3] = c("GNPDA2", "447040")
# intergenic RIOK3/RMC1 15612/4718 -> RMC1
Gene_Info_Posterior[9,2:3] = c("RMC1", "4718")
Gene_Info_Posterior %>%
bind_cols(NewPosterior_Hits) -> NewPosterior_Hits
knitr::kable(NewPosterior_Hits, digits=3)
| Function | Gene | Distance | rsid | chrm_UK10K | pos_UK10K | alt | ref | beta | se | z_obs | mu_posterior_estimate | mu_posterior_std_error | beta_posterior_estimate | beta_posterior_std_error | z_posterior | p_posterior |
| :--------- | :----- | :------- | :--------- | ----------: | ---------: | :-- | :-- | ------: | ----: | ------: | ----------------------: | ------------------------: | ------------------------: | --------------------------: | -----------: | :----------- |
| intergenic | USP4 | 7931 | rs13086611 | 3 | 49385417 | A | T | -0.013 | 0.004 | -3.019 | -3.043 | 0.503 | -0.013 | 0.002 | -6.049 | 1.46e-09 |
| intergenic | GNPDA2 | 447040 | rs13130484 | 4 | 45175691 | T | C | -0.012 | 0.004 | -3.161 | -3.270 | 0.487 | -0.013 | 0.002 | -6.716 | 1.86e-11 |
| intronic | MAD1L1 | 0 | rs34809719 | 7 | 2028968 | T | G | 0.016 | 0.005 | 3.227 | 2.774 | 0.483 | 0.014 | 0.002 | 5.749 | 8.97e-09 |
| UTR3 | ZPR1 | 0 | rs964184 | 11 | 116648917 | C | G | 0.021 | 0.006 | 3.791 | 3.165 | 0.497 | 0.018 | 0.003 | 6.364 | 1.96e-10 |
| intronic | HNF1A | 0 | rs1183910 | 12 | 121420807 | A | G | -0.015 | 0.004 | -3.676 | -2.915 | 0.481 | -0.012 | 0.002 | -6.064 | 1.33e-09 |
| intronic | ATXN2L | 0 | rs8049439 | 16 | 28837515 | T | C | 0.011 | 0.004 | 2.809 | 2.949 | 0.480 | 0.012 | 0.002 | 6.144 | 8.05e-10 |
| intronic | FTO | 0 | rs1421085 | 16 | 53800954 | T | C | 0.014 | 0.004 | 3.550 | 3.760 | 0.612 | 0.015 | 0.002 | 6.147 | 7.89e-10 |
| intronic | UBE2Z | 0 | rs999474 | 17 | 46987665 | A | G | 0.014 | 0.004 | 3.502 | 2.674 | 0.469 | 0.010 | 0.002 | 5.697 | 1.22e-08 |
| intergenic | RMC1 | 4718 | rs303757 | 18 | 21078716 | T | G | 0.011 | 0.004 | 2.793 | 2.983 | 0.478 | 0.012 | 0.002 | 6.240 | 4.37e-10 |
9 of the 28 genome-wide significant loci are missed by the conventional
GWAS and by the identification based on BFs (using same p-value
threshold of 5e-8 to assess significance).
# For the plots, we will use only the new posterior hits
NewPosterior_Hits %>%
transmute(rs=rsid,
gene = Gene,
color="#932735") -> my_SNPsPosterior
manhattan_plot_bGWAS(Lifespan_bGWAS, SNPs=my_SNPsPosterior, results = "posterior")
New hits, association with risk factors
These variants (and the ones in a 100kb window) can be further
investigated using the GWAS Catalog. R2
estimates in EUR population from LDlink
are used to keep only SNPs in LD (R2>0.1) with the variant identified.
SNP-trait associations p-values below 5e-8 are reported below (when
LD-friends are used, p-values adjusted for the correlation between the
variants).
Reported Associations for posterior Hits
##
## * SNP - rs13086611 (USP4):
##
##
## snp chrm posh19 LD_R2 LD_alleles trait p adjusted_p effect gene url
## -------------- ----- --------- ------ ----------------------------- -------------------------------------------- ------ ----------- ----------------------------- ------------------ -------------------------------------
## rs13086611-? 3 49385417 NA NA Educational attainment 2e-14 NA [NR] unit increase USP4 - GPX1 www.ncbi.nlm.nih.gov/pubmed/27046643
## rs7623659-T 3 49414791 0.991 rs7623659(T)/rs13086611(A) Cognitive performance (MTAG) 4e-57 1.24e-56 [0.035-0.044] unit increase RHOA www.ncbi.nlm.nih.gov/pubmed/30038396
## rs148734725-A 3 49406708 0.991 rs148734725(G)/rs13086611(T) Educational attainment (college completion) 5e-12 6.21e-12 [NR] RHOA www.ncbi.nlm.nih.gov/pubmed/27225129
## rs9859556-T 3 49455986 0.991 rs9859556(T)/rs13086611(A) Educational attainment (years of education) 5e-82 2.58e-81 [0.026-0.032] unit increase AC104452.1, AMT www.ncbi.nlm.nih.gov/pubmed/30038396
## rs1987628-A 3 49399259 0.991 rs1987628(G)/rs13086611(T) Extremely high intelligence 4e-08 4.59e-08 NR z score increase increase RHOA www.ncbi.nlm.nih.gov/pubmed/29520040
## rs7646366-A 3 49470668 0.991 rs7646366(G)/rs13086611(T) General cognitive ability 2e-19 2.88e-19 z-score increase NICN1 - RNA5SP130 www.ncbi.nlm.nih.gov/pubmed/29844566
## rs7623659-T 3 49414791 0.991 rs7623659(T)/rs13086611(A) Intelligence 1e-27 1.71e-27 z-score increase RHOA www.ncbi.nlm.nih.gov/pubmed/29942086
##
##
## * SNP - rs13130484 (GNPDA2):
##
##
## snp chrm posh19 LD_R2 LD_alleles trait p adjusted_p effect gene url
## ------------- ----- --------- ------ ---------------------------- ---------------------------------------------------------------------- ------ ----------- ------------------------------ ---------------------- -------------------------------------
## rs13130484-? 4 45175691 NA NA Body mass index 2e-13 NA [0.036-0.063] unit increase AC108467.1 - THAP12P9 www.ncbi.nlm.nih.gov/pubmed/31217584
## rs13130484-T 4 45175691 NA NA Childhood body mass index 2e-23 NA [0.053-0.081] unit increase AC108467.1 - THAP12P9 www.ncbi.nlm.nih.gov/pubmed/26604143
## rs13130484-T 4 45175691 NA NA Hand grip strength 1e-12 NA [0.0014-0.0026] unit decrease AC108467.1 - THAP12P9 www.ncbi.nlm.nih.gov/pubmed/29691431
## rs13130484-T 4 45175691 NA NA Obesity 4e-28 NA [NR] AC108467.1 - THAP12P9 www.ncbi.nlm.nih.gov/pubmed/23563607
## rs10938397-A 4 45182527 0.992 rs10938397(G)/rs13130484(T) BMI (adjusted for smoking behaviour) 2e-23 3.00e-23 [0.026-0.039] kg/m2 decrease AC108467.1 - THAP12P9 www.ncbi.nlm.nih.gov/pubmed/28443625
## rs10938397-A 4 45182527 0.992 rs10938397(G)/rs13130484(T) BMI in non-smokers 4e-18 5.45e-18 [0.025-0.04] kg/m2 decrease AC108467.1 - THAP12P9 www.ncbi.nlm.nih.gov/pubmed/28443625
## rs10938397-G 4 45182527 0.992 rs10938397(G)/rs13130484(T) Body mass index (adult) 6e-46 1.37e-45 [0.028-0.06] unit increase AC108467.1 - THAP12P9 www.ncbi.nlm.nih.gov/pubmed/28430825
## rs10938397-A 4 45182527 0.992 rs10938397(G)/rs13130484(T) Body mass index (joint analysis main effects and smoking interaction) 7e-23 1.04e-22 AC108467.1 - THAP12P9 www.ncbi.nlm.nih.gov/pubmed/28443625
## rs10938397-A 4 45182527 0.992 rs10938397(G)/rs13130484(T) Hip circumference 9e-17 1.20e-16 [0.023-0.038] unit decrease AC108467.1 - THAP12P9 www.ncbi.nlm.nih.gov/pubmed/25673412
## rs10938397-A 4 45182527 0.992 rs10938397(G)/rs13130484(T) Menarche (age at onset) 4e-13 4.97e-13 [0.03-0.05] unit increase AC108467.1 - THAP12P9 www.ncbi.nlm.nih.gov/pubmed/25231870
## rs12507026-T 4 45181334 0.996 rs12507026(T)/rs13130484(T) Obese vs. thin 6e-15 6.81e-15 [1.13-1.23] AC108467.1 - THAP12P9 www.ncbi.nlm.nih.gov/pubmed/30677029
## rs10938398-A 4 45186139 0.992 rs10938398(G)/rs13130484(T) Type 2 diabetes 2e-09 2.32e-09 1.04-1.08 AC108467.1 - THAP12P9 www.ncbi.nlm.nih.gov/pubmed/30718926
## rs10938397-A 4 45182527 0.992 rs10938397(G)/rs13130484(T) Waist circumference 6e-20 8.45e-20 [0.025-0.038] unit decrease AC108467.1 - THAP12P9 www.ncbi.nlm.nih.gov/pubmed/25673412
##
##
## * SNP - rs34809719 (MAD1L1):
##
##
## snp chrm posh19 LD_R2 LD_alleles trait p adjusted_p effect gene url
## ------------- ----- -------- ------ ---------------------------- -------------------------------------------- ------ ----------- ------------------------------ ------------------- -------------------------------------
## rs62444881-T 7 2052318 0.942 rs62444881(T)/rs34809719(T) Educational attainment (years of education) 3e-19 3.21e-18 [0.012-0.019] unit increase AC069288.1, MAD1L1 www.ncbi.nlm.nih.gov/pubmed/30038396
## rs11766468-A 7 2082604 0.680 rs11766468(G)/rs34809719(G) Hand grip strength 8e-12 1.72e-08 [0.0022-0.0038] unit increase AC069288.1, MAD1L1 www.ncbi.nlm.nih.gov/pubmed/29691431
## rs62442924-C 7 1989976 0.887 rs62442924(T)/rs34809719(T) Highest math class taken 3e-10 3.00e-09 [0.012-0.023] unit decrease MAD1L1, AC069288.1 www.ncbi.nlm.nih.gov/pubmed/30038396
##
##
## * SNP - rs964184 (ZPR1):
##
##
## snp chrm posh19 LD_R2 LD_alleles trait p adjusted_p effect gene url
## ------------- ----- ---------- ------ -------------------------- -------------------------------------------------------------- ------- ----------- ------------------------------------------- ------ -------------------------------------
## rs964184-? 11 116648917 NA NA Age-related disease endophenotypes 3e-116 NA ZPR1 www.ncbi.nlm.nih.gov/pubmed/27790247
## rs964184-? 11 116648917 NA NA Age-related diseases, mortality and associated endophenotypes 2e-108 NA ZPR1 www.ncbi.nlm.nih.gov/pubmed/27790247
## rs964184-C 11 116648917 NA NA Blood metabolite levels 8e-15 NA [0.019-0.031] unit decrease ZPR1 www.ncbi.nlm.nih.gov/pubmed/24816252
## rs964184-C 11 116648917 NA NA Blood protein levels 8e-24 NA [NR] unit increase ZPR1 www.ncbi.nlm.nih.gov/pubmed/30072576
## rs964184-G 11 116648917 NA NA Cholesterol, total 6e-57 NA [4.11-5.25] mg/dL increase ZPR1 www.ncbi.nlm.nih.gov/pubmed/20686565
## rs964184-C 11 116648917 NA NA Coronary artery disease 8e-15 NA [0.053-0.089] unit decrease ZPR1 www.ncbi.nlm.nih.gov/pubmed/29212778
## rs964184-? 11 116648917 NA NA Coronary artery disease or large artery stroke 9e-10 NA ZPR1 www.ncbi.nlm.nih.gov/pubmed/24262325
## rs964184-G 11 116648917 NA NA Coronary heart disease 1e-17 NA [1.10-1.16] ZPR1 www.ncbi.nlm.nih.gov/pubmed/21378990
## rs964184-C 11 116648917 NA NA HDL cholesterol 6e-48 NA [NR] unit increase ZPR1 www.ncbi.nlm.nih.gov/pubmed/24097068
## rs964184-C 11 116648917 NA NA HDL cholesterol levels 6e-49 NA [0.018-0.024] unit increase ZPR1 www.ncbi.nlm.nih.gov/pubmed/30926973
## rs964184-? 11 116648917 NA NA High density lipoprotein cholesterol levels 2e-19 NA [0.91-1.43] unit increase ZPR1 www.ncbi.nlm.nih.gov/pubmed/31217584
## rs964184-? 11 116648917 NA NA Hypertriglyceridemia 5e-35 NA [1.67-1.87] ZPR1 www.ncbi.nlm.nih.gov/pubmed/23505323
## rs964184-G 11 116648917 NA NA LDL cholesterol 1e-26 NA [2.32-3.38] mg/dL increase ZPR1 www.ncbi.nlm.nih.gov/pubmed/20686565
## rs964184-C 11 116648917 NA NA LDL cholesterol levels 3e-18 NA [0.07-0.101] unit decrease (EA Beta value) ZPR1 www.ncbi.nlm.nih.gov/pubmed/28334899
## rs964184-C 11 116648917 NA NA Lipoprotein-associated phospholipase A2 activity and mass 8e-11 NA [0.022-0.042] unit decrease ZPR1 www.ncbi.nlm.nih.gov/pubmed/22003152
## rs964184-G 11 116648917 NA NA Metabolic syndrome 3e-31 NA [NR] mmol/l increase ZPR1 www.ncbi.nlm.nih.gov/pubmed/22399527
## rs964184-? 11 116648917 NA NA Metabolite levels 8e-20 NA ZPR1 www.ncbi.nlm.nih.gov/pubmed/22916037
## rs964184-C 11 116648917 NA NA Metabolite levels (lipoprotein measures) 8e-66 NA [0.22-0.26] unit decrease ZPR1 www.ncbi.nlm.nih.gov/pubmed/27005778
## rs964184-? 11 116648917 NA NA Phospholipid levels (plasma) 2e-10 NA [NR] % increase ZPR1 www.ncbi.nlm.nih.gov/pubmed/22359512
## rs964184-C 11 116648917 NA NA Postprandial triglyceride response to high fat diet meal 1e-09 NA [0.2-0.4] unit decrease ZPR1 www.ncbi.nlm.nih.gov/pubmed/26256467
## rs964184-G 11 116648917 NA NA Response to Vitamin E supplementation 3e-12 NA [0.050-0.090] mg/L increase ZPR1 www.ncbi.nlm.nih.gov/pubmed/22437554
## rs964184-C 11 116648917 NA NA Total cholesterol levels 2e-55 NA [0.11-0.14] unit decrease (EA Beta values) ZPR1 www.ncbi.nlm.nih.gov/pubmed/28334899
## rs964184-C 11 116648917 NA NA Triacylglyceride levels 1e-08 NA [0.17-0.35] unit decrease ZPR1 www.ncbi.nlm.nih.gov/pubmed/31551469
## rs964184-G 11 116648917 NA NA Triacylglycerol 56:2 levels 8e-10 NA [0.17-0.33] unit increase ZPR1 www.ncbi.nlm.nih.gov/pubmed/31560688
## rs964184-C 11 116648917 NA NA Triglyceride levels 2e-138 NA [0.07-0.082] unit decrease ZPR1 www.ncbi.nlm.nih.gov/pubmed/30926973
## rs964184-G 11 116648917 NA NA Triglycerides 7e-240 NA [16.01-17.89] mg/dL increase ZPR1 www.ncbi.nlm.nih.gov/pubmed/20686565
## rs964184-C 11 116648917 NA NA Triglycerides x physical activity interaction (2df test) 0e+00 NA ZPR1 www.ncbi.nlm.nih.gov/pubmed/30670697
## rs964184-G 11 116648917 NA NA Very low density lipoprotein cholesterol levels 4e-12 NA [0.17-0.31] unit increase ZPR1 www.ncbi.nlm.nih.gov/pubmed/28548082
## rs964184-G 11 116648917 NA NA Vitamin E levels 8e-12 NA [0.02-0.06] unit increase ZPR1 www.ncbi.nlm.nih.gov/pubmed/21729881
## rs10790162-A 11 116639104 0.507 rs10790162(G)/rs964184(G) HDL Cholesterol - Triglycerides (HDLC-TG) 3e-15 1.91e-08 [0.28-0.48] unit increase BUD13 www.ncbi.nlm.nih.gov/pubmed/21386085
## rs651821-? 11 116662579 0.439 rs651821(T)/rs964184(C) Lipid metabolism phenotypes 8e-20 1.58e-09 [0.21-0.33] unit increase APOA5 www.ncbi.nlm.nih.gov/pubmed/22286219
## rs651821-C 11 116662579 0.439 rs651821(T)/rs964184(C) Lipid traits 2e-59 4.90e-27 [0.14-0.20] mmol/L increase APOA5 www.ncbi.nlm.nih.gov/pubmed/24386095
## rs66505542-T 11 116623214 0.849 rs66505542(A)/rs964184(G) Red cell distribution width 2e-14 1.78e-12 [0.032-0.054] unit increase BUD13 www.ncbi.nlm.nih.gov/pubmed/28957414
## rs2075290-C 11 116653296 0.527 rs2075290(T)/rs964184(C) Waist Circumference - Triglycerides (WC-TG) 1e-16 1.66e-09 [0.31-0.51] unit increase ZPR1 www.ncbi.nlm.nih.gov/pubmed/21386085
##
##
## * SNP - rs1183910 (HNF1A):
##
##
## snp chrm posh19 LD_R2 LD_alleles trait p adjusted_p effect gene url
## ------------ ----- ---------- ------ -------------------------- --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ------- ----------- --------------------------------------------- ----------------- -------------------------------------
## rs1183910-T 12 121420807 NA NA C-reactive protein 1e-30 NA [10.9-16.6] % decrease HNF1A www.ncbi.nlm.nih.gov/pubmed/19567438
## rs1183910-G 12 121420807 NA NA C-reactive protein levels 2e-124 NA [0.14-0.16] unit increase HNF1A www.ncbi.nlm.nih.gov/pubmed/21300955
## rs1183910-A 12 121420807 NA NA C-reactive protein levels or LDL-cholesterol levels (pleiotropy) 6e-128 NA HNF1A www.ncbi.nlm.nih.gov/pubmed/27286809
## rs1183910-A 12 121420807 NA NA C-reactive protein levels or total cholesterol levels (pleiotropy) 8e-128 NA HNF1A www.ncbi.nlm.nih.gov/pubmed/27286809
## rs3213545-A 12 121471337 0.663 rs3213545(G)/rs1183910(G) Cardiovascular disease risk factors 4e-15 1.59e-10 [0.092-0.150] units/l decrease OASL www.ncbi.nlm.nih.gov/pubmed/21943158
## rs1169288-C 12 121416650 0.888 rs1169288(C)/rs1183910(A) Cholesterol, total 4e-17 2.25e-15 [NR] unit increase HNF1A-AS1, HNF1A www.ncbi.nlm.nih.gov/pubmed/24097068
## rs2244608-A 12 121416988 0.909 rs2244608(G)/rs1183910(G) Coronary artery disease 7e-19 2.62e-17 [0.04-0.063] unit decrease HNF1A, HNF1A-AS1 www.ncbi.nlm.nih.gov/pubmed/29212778
## rs2244608-G 12 121416988 0.909 rs2244608(G)/rs1183910(G) Coronary artery disease (myocardial infarction, percutaneous transluminal coronary angioplasty, coronary artery bypass grafting, angina or chromic ischemic heart disease) 8e-10 4.65e-09 [1.035-1.070] HNF1A, HNF1A-AS1 www.ncbi.nlm.nih.gov/pubmed/28714975
## rs1169288-? 12 121416650 0.888 rs1169288(C)/rs1183910(A) Gallstone disease 2e-14 5.67e-13 [1.06-1.1] HNF1A-AS1, HNF1A www.ncbi.nlm.nih.gov/pubmed/30504769
## rs1169288-G 12 121416650 0.888 rs1169288(C)/rs1183910(A) Gamma glutamyl transferase levels 2e-18 1.57e-16 [0.10-0.16] unit decrease HNF1A-AS1, HNF1A www.ncbi.nlm.nih.gov/pubmed/22010049
## rs2393791-G 12 121423956 0.692 rs2393791(T)/rs1183910(G) Gamma glutamyl transpeptidase 7e-30 3.46e-21 [0.0062-0.0090] IU/L increase HNF1A www.ncbi.nlm.nih.gov/pubmed/21909109
## rs2244608-G 12 121416988 0.909 rs2244608(G)/rs1183910(G) HDL cholesterol levels x alcohol consumption (drinkers vs non-drinkers) interaction (2df) 4e-09 2.02e-08 HNF1A, HNF1A-AS1 www.ncbi.nlm.nih.gov/pubmed/30698716
## rs2251468-A 12 121405126 0.814 rs2251468(C)/rs1183910(A) Homocysteine levels 1e-12 1.23e-10 [0.037-0.065] unit decrease HNF1A-AS1 www.ncbi.nlm.nih.gov/pubmed/23824729
## rs2650000-A 12 121388962 0.807 rs2650000(C)/rs1183910(G) Insulinogenic index 5e-10 2.30e-08 [0.052-0.1] unit decrease HNF1A-AS1 www.ncbi.nlm.nih.gov/pubmed/23263489
## rs1169288-C 12 121416650 0.888 rs1169288(C)/rs1183910(A) LDL cholesterol 6e-21 8.99e-19 [NR] unit increase HNF1A-AS1, HNF1A www.ncbi.nlm.nih.gov/pubmed/24097068
## rs1169288-A 12 121416650 0.888 rs1169288(C)/rs1183910(A) LDL cholesterol levels 2e-22 4.37e-20 [0.03-0.045] unit decrease (EA Beta value) HNF1A-AS1, HNF1A www.ncbi.nlm.nih.gov/pubmed/28334899
## rs2244608-G 12 121416988 0.909 rs2244608(G)/rs1183910(G) LDL cholesterol levels in current drinkers 1e-19 4.46e-18 NR unit decrease HNF1A, HNF1A-AS1 www.ncbi.nlm.nih.gov/pubmed/30698716
## rs2244608-G 12 121416988 0.909 rs2244608(G)/rs1183910(G) LDL cholesterol levels x alcohol consumption (drinkers vs non-drinkers) interaction (2df) 2e-28 5.40e-26 HNF1A, HNF1A-AS1 www.ncbi.nlm.nih.gov/pubmed/30698716
## rs2244608-G 12 121416988 0.909 rs2244608(G)/rs1183910(G) LDL cholesterol levels x alcohol consumption (regular vs non-regular drinkers) interaction (2df) 3e-26 5.16e-24 HNF1A, HNF1A-AS1 www.ncbi.nlm.nih.gov/pubmed/30698716
## rs6489786-A 12 121397875 0.810 rs6489786(G)/rs1183910(A) LDL cholesterol x physical activity interaction (2df test) 7e-19 1.37e-15 HNF1A-AS1 www.ncbi.nlm.nih.gov/pubmed/30670697
## rs7310409-G 12 121424861 0.688 rs7310409(G)/rs1183910(G) Liver enzyme levels (gamma-glutamyl transferase) 7e-45 2.05e-31 [5.70-7.80] % increase HNF1A www.ncbi.nlm.nih.gov/pubmed/22001757
## rs2650000-A 12 121388962 0.807 rs2650000(C)/rs1183910(G) Metabolic traits 3e-11 2.34e-09 [0.25-0.55] mmol/l decrease HNF1A-AS1 www.ncbi.nlm.nih.gov/pubmed/19060910
## rs1169288-C 12 121416650 0.888 rs1169288(C)/rs1183910(A) Serum alpha1-antitrypsin levels 2e-12 3.41e-11 [1.3-2.28] mg dl-1 increase HNF1A-AS1, HNF1A www.ncbi.nlm.nih.gov/pubmed/26174136
## rs2244608-A 12 121416988 0.909 rs2244608(G)/rs1183910(G) Total cholesterol levels 4e-19 1.57e-17 [0.024-0.039] unit decrease (EA Beta values) HNF1A, HNF1A-AS1 www.ncbi.nlm.nih.gov/pubmed/28334899
## rs1169288-C 12 121416650 0.888 rs1169288(C)/rs1183910(A) Type 2 diabetes 1e-11 1.43e-10 [1.03-1.06] HNF1A-AS1, HNF1A www.ncbi.nlm.nih.gov/pubmed/29632382
## rs1169288-C 12 121416650 0.888 rs1169288(C)/rs1183910(A) Type 2 diabetes (adjusted for BMI) 2e-10 2.05e-09 [1.02-1.06] HNF1A-AS1, HNF1A www.ncbi.nlm.nih.gov/pubmed/29632382
##
##
## * SNP - rs8049439 (ATXN2L):
##
##
## snp chrm posh19 LD_R2 LD_alleles trait p adjusted_p effect gene url
## -------------- ----- --------- ------ ---------------------------- ---------------------------------------------------------------------- ------ ----------- ----------------------------------- ----------------------- -------------------------------------
## rs8049439-T 16 28837515 NA NA Educational attainment (years of education) 7e-11 NA NR unit increase ATXN2L www.ncbi.nlm.nih.gov/pubmed/27225129
## rs8049439-G 16 28837515 NA NA Inflammatory bowel disease (early onset) 2e-09 NA [1.00-1.30] ATXN2L www.ncbi.nlm.nih.gov/pubmed/19915574
## rs113079736-A 16 28860183 0.935 rs113079736(G)/rs8049439(T) Alcohol consumption (drinks per week) 3e-11 1.29e-10 [0.011-0.02] unit decrease SH2B1 www.ncbi.nlm.nih.gov/pubmed/30643258
## rs3888190-A 16 28889486 0.931 rs3888190(C)/rs8049439(T) BMI (adjusted for smoking behaviour) 2e-18 2.92e-17 [0.023-0.035] kg/m2 increase SH2B1 - ATP2A1 www.ncbi.nlm.nih.gov/pubmed/28443625
## rs3888190-A 16 28889486 0.931 rs3888190(C)/rs8049439(T) BMI in non-smokers 4e-15 3.48e-14 [0.022-0.037] kg/m2 increase SH2B1 - ATP2A1 www.ncbi.nlm.nih.gov/pubmed/28443625
## rs4788099-G 16 28855727 0.935 rs4788099(G)/rs8049439(C) Body fat percentage 1e-08 2.98e-08 [0.017-0.037] body fat % increase TUFM www.ncbi.nlm.nih.gov/pubmed/26833246
## rs3888190-A 16 28889486 0.931 rs3888190(C)/rs8049439(T) Body mass index 2e-31 2.26e-29 [0.025-0.037] kg/m2 increase SH2B1 - ATP2A1 www.ncbi.nlm.nih.gov/pubmed/28892062
## rs3888190-A 16 28889486 0.931 rs3888190(C)/rs8049439(T) Body mass index (joint analysis main effects and smoking interaction) 6e-18 8.13e-17 SH2B1 - ATP2A1 www.ncbi.nlm.nih.gov/pubmed/28443625
## rs8055982-? 16 28881202 0.935 rs8055982(C)/rs8049439(C) Cognitive ability 1e-09 3.45e-09 z score increase SH2B1 www.ncbi.nlm.nih.gov/pubmed/29186694
## rs8062405-? 16 28837906 0.931 rs8062405(G)/rs8049439(C) Cognitive ability (MTAG) 4e-14 2.96e-13 z score increase ATXN2L www.ncbi.nlm.nih.gov/pubmed/29186694
## rs12448902-C 16 28871191 0.858 rs12448902(G)/rs8049439(T) Cognitive performance 5e-19 1.54e-16 [0.02-0.032] unit increase SH2B1 www.ncbi.nlm.nih.gov/pubmed/30038396
## rs72793812-A 16 28840381 0.922 rs72793812(G)/rs8049439(T) Cognitive performance (MTAG) 8e-35 2.98e-32 [0.024-0.033] unit increase ATXN2L www.ncbi.nlm.nih.gov/pubmed/30038396
## rs12928404-T 16 28847246 0.974 rs12928404(T)/rs8049439(T) Extremely high intelligence 2e-08 3.06e-08 NR z score increase increase AC133550.1, ATXN2L www.ncbi.nlm.nih.gov/pubmed/29520040
## rs62037363-T 16 28865042 0.935 rs62037363(T)/rs8049439(T) General cognitive ability 7e-19 9.21e-18 z-score increase SH2B1 www.ncbi.nlm.nih.gov/pubmed/29844566
## rs12928404-C 16 28847246 0.974 rs12928404(T)/rs8049439(T) Hand grip strength 1e-25 4.28e-25 [0.0024-0.0036] unit decrease AC133550.1, ATXN2L www.ncbi.nlm.nih.gov/pubmed/29691431
## rs11864750-A 16 28875204 0.935 rs11864750(T)/rs8049439(C) Highest math class taken (MTAG) 7e-37 1.31e-34 [0.018-0.025] unit increase SH2B1 www.ncbi.nlm.nih.gov/pubmed/30038396
## rs3888190-A 16 28889486 0.931 rs3888190(C)/rs8049439(T) Hip circumference 9e-22 2.22e-20 [0.027-0.042] unit increase SH2B1 - ATP2A1 www.ncbi.nlm.nih.gov/pubmed/25673412
## rs7187776-A 16 28857645 0.991 rs7187776(G)/rs8049439(C) Hip circumference adjusted for BMI 4e-08 4.59e-08 [0.013-0.027] unit decrease TUFM www.ncbi.nlm.nih.gov/pubmed/25673412
## rs62037363-? 16 28865042 0.935 rs62037363(T)/rs8049439(T) Inflammatory bowel disease 6e-22 1.24e-20 SH2B1 www.ncbi.nlm.nih.gov/pubmed/26192919
## rs2008514-A 16 28825605 0.927 rs2008514(G)/rs8049439(T) Intelligence 1e-24 4.97e-23 z-score decrease AC145285.1, AC145285.2 www.ncbi.nlm.nih.gov/pubmed/29942086
## rs7498665-G 16 28883241 0.935 rs7498665(G)/rs8049439(C) Obesity 3e-13 1.73e-12 [NR] SH2B1 www.ncbi.nlm.nih.gov/pubmed/23563607
## rs12446589-G 16 28870962 0.927 rs12446589(G)/rs8049439(T) Self-reported math ability 3e-27 2.25e-25 [0.018-0.026] unit increase SH2B1 www.ncbi.nlm.nih.gov/pubmed/30038396
## rs12446589-A 16 28870962 0.927 rs12446589(G)/rs8049439(T) Self-reported math ability (MTAG) 2e-36 6.96e-34 [0.02-0.027] unit decrease SH2B1 www.ncbi.nlm.nih.gov/pubmed/30038396
## rs56163509-? 16 28864471 0.931 rs56163509(G)/rs8049439(C) Tonsillectomy 1e-08 3.20e-08 [0.034-0.07] unit increase SH2B1 www.ncbi.nlm.nih.gov/pubmed/28928442
## rs7498665-A 16 28883241 0.935 rs7498665(G)/rs8049439(C) Waist circumference 1e-22 2.32e-21 [0.027-0.041] unit decrease SH2B1 www.ncbi.nlm.nih.gov/pubmed/25673412
## rs7498665-G 16 28883241 0.935 rs7498665(G)/rs8049439(C) Weight 1e-09 3.45e-09 [2.51-4.87] percentage SD increase SH2B1 www.ncbi.nlm.nih.gov/pubmed/19079260
##
##
## * SNP - rs1421085 (FTO):
##
##
## snp chrm posh19 LD_R2 LD_alleles trait p adjusted_p effect gene url
## ------------- ----- --------- ------ --------------------------- ------------------------------------------------------------------------------------------ ------- ----------- ----------------------------------- ----- -------------------------------------
## rs1421085-T 16 53800954 NA NA Alcohol consumption 9e-13 NA [0.006-0.01] unit decrease FTO www.ncbi.nlm.nih.gov/pubmed/31358974
## rs1421085-C 16 53800954 NA NA Body mass index 2e-75 NA [0.068-0.084] unit increase FTO www.ncbi.nlm.nih.gov/pubmed/26961502
## rs1421085-C 16 53800954 NA NA Childhood body mass index 5e-16 NA [0.045-0.073] unit increase FTO www.ncbi.nlm.nih.gov/pubmed/26604143
## rs1421085-C 16 53800954 NA NA Dietary macronutrient intake 1e-16 NA [0.059-0.099] % increase FTO www.ncbi.nlm.nih.gov/pubmed/29988085
## rs1421085-C 16 53800954 NA NA Hand grip strength 5e-38 NA [0.0034-0.0046] unit decrease FTO www.ncbi.nlm.nih.gov/pubmed/29691431
## rs1421085-C 16 53800954 NA NA Obesity 6e-39 NA [NR] FTO www.ncbi.nlm.nih.gov/pubmed/23563607
## rs1421085-C 16 53800954 NA NA Obesity (early onset extreme) 3e-28 NA [1.35-1.54] FTO www.ncbi.nlm.nih.gov/pubmed/23563609
## rs1421085-C 16 53800954 NA NA Type 2 diabetes 1e-28 NA 1.11-1.15 FTO www.ncbi.nlm.nih.gov/pubmed/30718926
## rs8050136-C 16 53816275 0.918 rs8050136(C)/rs1421085(T) Adiposity 3e-26 3.19e-24 [NR] % decrease FTO www.ncbi.nlm.nih.gov/pubmed/21706003
## rs9939609-? 16 53820527 0.918 rs9939609(T)/rs1421085(T) Age-related disease endophenotypes 2e-17 4.06e-16 FTO www.ncbi.nlm.nih.gov/pubmed/27790247
## rs9939609-? 16 53820527 0.918 rs9939609(T)/rs1421085(T) Age-related diseases, mortality and associated endophenotypes 1e-14 1.23e-13 FTO www.ncbi.nlm.nih.gov/pubmed/27790247
## rs1558902-A 16 53803574 1.000 rs1558902(T)/rs1421085(T) BMI (adjusted for smoking behaviour) 3e-110 3.00e-110 [0.068-0.082] kg/m2 increase FTO www.ncbi.nlm.nih.gov/pubmed/28443625
## rs1558902-A 16 53803574 1.000 rs1558902(T)/rs1421085(T) BMI in non-smokers 1e-79 1.00e-79 [0.065-0.079] kg/m2 increase FTO www.ncbi.nlm.nih.gov/pubmed/28443625
## rs1558902-A 16 53803574 1.000 rs1558902(T)/rs1421085(T) BMI in smokers 8e-39 8.00e-39 [0.079-0.108] kg/m2 increase FTO www.ncbi.nlm.nih.gov/pubmed/28443625
## rs1558902-A 16 53803574 1.000 rs1558902(T)/rs1421085(T) Body fat percentage 4e-27 4.00e-27 [0.041-0.061] body fat % increase FTO www.ncbi.nlm.nih.gov/pubmed/26833246
## rs17817964-T 16 53828066 0.918 rs17817964(T)/rs1421085(T) Body mass index (adult) 1e-146 7.36e-135 [0.049-0.089] unit increase FTO www.ncbi.nlm.nih.gov/pubmed/28430825
## rs1558902-A 16 53803574 1.000 rs1558902(T)/rs1421085(T) Body mass index (age interaction) 1e-26 1.00e-26 FTO www.ncbi.nlm.nih.gov/pubmed/25953783
## rs1558902-A 16 53803574 1.000 rs1558902(T)/rs1421085(T) Body mass index (joint analysis main effects and smoking interaction) 2e-115 2.00e-115 FTO www.ncbi.nlm.nih.gov/pubmed/28443625
## rs12149832-A 16 53842908 0.837 rs12149832(G)/rs1421085(T) Body mass index (SNP x SNP interaction) 5e-22 1.07e-18 [0.057-0.089] unit increase FTO www.ncbi.nlm.nih.gov/pubmed/22344221
## rs17817449-T 16 53813367 0.922 rs17817449(T)/rs1421085(T) Breast cancer 2e-14 2.07e-13 [1.05-1.1] FTO www.ncbi.nlm.nih.gov/pubmed/25751625
## rs17817449-T 16 53813367 0.922 rs17817449(T)/rs1421085(T) Breast cancer (estrogen-receptor negative) 2e-10 1.02e-09 [1.04-1.11] (Oncoarray) FTO www.ncbi.nlm.nih.gov/pubmed/29058716
## rs1558902-A 16 53803574 1.000 rs1558902(T)/rs1421085(T) C-reactive protein levels or HDL-cholesterol levels (pleiotropy) 5e-09 5.00e-09 FTO www.ncbi.nlm.nih.gov/pubmed/27286809
## rs8043757-T 16 53813450 0.918 rs8043757(T)/rs1421085(C) circulating leptin levels 1e-09 4.84e-09 [0.02-0.04] unit increase FTO www.ncbi.nlm.nih.gov/pubmed/26833098
## rs9937053-A 16 53799507 0.949 rs9937053(G)/rs1421085(T) Coffee consumption 2e-21 2.08e-20 [0.011-0.017] unit increase FTO www.ncbi.nlm.nih.gov/pubmed/31046077
## rs11642015-? 16 53802494 1.000 rs11642015(T)/rs1421085(C) Diastolic blood pressure x smoking status (current vs non-current) interaction (2df test) 3e-11 3.00e-11 FTO www.ncbi.nlm.nih.gov/pubmed/29455858
## rs11642015-? 16 53802494 1.000 rs11642015(T)/rs1421085(C) Diastolic blood pressure x smoking status (ever vs never) interaction (2df test) 2e-10 2.00e-10 FTO www.ncbi.nlm.nih.gov/pubmed/29455858
## rs1558902-A 16 53803574 1.000 rs1558902(T)/rs1421085(T) Glycated hemoglobin levels 3e-08 3.00e-08 [0.0066-0.014] unit increase FTO www.ncbi.nlm.nih.gov/pubmed/28898252
## rs1121980-A 16 53809247 0.956 rs1121980(G)/rs1421085(T) HDL cholesterol 7e-09 1.48e-08 [NR] unit decrease FTO www.ncbi.nlm.nih.gov/pubmed/24097068
## rs8063057-T 16 53812433 0.918 rs8063057(T)/rs1421085(T) Heel bone mineral density 5e-26 5.15e-24 [0.016-0.024] unit decrease FTO www.ncbi.nlm.nih.gov/pubmed/30598549
## rs1558902-A 16 53803574 1.000 rs1558902(T)/rs1421085(T) Height adjusted BMI 9e-10 9.00e-10 [0.13-0.27] unit increase FTO www.ncbi.nlm.nih.gov/pubmed/25044758
## rs9939973-A 16 53800568 0.956 rs9939973(G)/rs1421085(T) Hip circumference 2e-86 9.50e-83 [0.063-0.077] unit increase FTO www.ncbi.nlm.nih.gov/pubmed/25673412
## rs9936385-? 16 53819169 0.918 rs9936385(T)/rs1421085(T) Lean body mass 7e-13 6.08e-12 [0.11-0.19] unit decrease FTO www.ncbi.nlm.nih.gov/pubmed/28724990
## rs8050136-C 16 53816275 0.918 rs8050136(C)/rs1421085(T) Menarche (age at onset) 2e-17 4.06e-16 [0.03-0.05] unit increase FTO www.ncbi.nlm.nih.gov/pubmed/25231870
## rs9940128-A 16 53800754 0.956 rs9940128(G)/rs1421085(T) Metabolic syndrome 2e-09 4.47e-09 [NR] cm increase FTO www.ncbi.nlm.nih.gov/pubmed/22399527
## rs9928094-G 16 53799905 0.953 rs9928094(G)/rs1421085(T) Obesity (extreme) 6e-101 2.89e-96 [1.29-1.36] FTO www.ncbi.nlm.nih.gov/pubmed/30677029
## rs9928094-A 16 53799905 0.953 rs9928094(G)/rs1421085(T) Pulse pressure x alcohol consumption interaction (2df test) 3e-15 1.34e-14 FTO www.ncbi.nlm.nih.gov/pubmed/29912962
## rs9972653-G 16 53814363 0.918 rs9972653(T)/rs1421085(C) Pure non-grapefruit juice consumption 1e-09 4.84e-09 [0.0054-0.0104] unit increase FTO www.ncbi.nlm.nih.gov/pubmed/31046077
## rs55872725-? 16 53809123 1.000 rs55872725(T)/rs1421085(C) Strenuous sports or other exercises 3e-13 3.00e-13 FTO www.ncbi.nlm.nih.gov/pubmed/29899525
## rs7185735-A 16 53822651 0.922 rs7185735(G)/rs1421085(C) Subcutaneous adipose tissue 1e-09 4.48e-09 z-score decrease FTO www.ncbi.nlm.nih.gov/pubmed/27918534
## rs55872725-C 16 53809123 1.000 rs55872725(T)/rs1421085(C) Sugar-sweetened beverage consumption 2e-15 2.00e-15 [0.0071-0.0119] unit increase FTO www.ncbi.nlm.nih.gov/pubmed/31046077
## rs55872725-T 16 53809123 1.000 rs55872725(T)/rs1421085(C) Systolic blood pressure x alcohol consumption interaction (2df test) 2e-13 2.00e-13 FTO www.ncbi.nlm.nih.gov/pubmed/29912962
## rs11642015-? 16 53802494 1.000 rs11642015(T)/rs1421085(C) Systolic blood pressure x smoking status (current vs non-current) interaction (2df test) 1e-20 1.00e-20 FTO www.ncbi.nlm.nih.gov/pubmed/29455858
## rs11642015-? 16 53802494 1.000 rs11642015(T)/rs1421085(C) Systolic blood pressure x smoking status (ever vs never) interaction (2df test) 4e-20 4.00e-20 FTO www.ncbi.nlm.nih.gov/pubmed/29455858
## rs56094641-? 16 53806453 0.984 rs56094641(G)/rs1421085(T) Type 2 diabetes nephropathy 8e-10 1.09e-09 [1.14-1.29] FTO www.ncbi.nlm.nih.gov/pubmed/30566433
## rs56094641-G 16 53806453 0.984 rs56094641(G)/rs1421085(T) Type 2 diabetes nephropathy including microalbuminuria 2e-09 2.69e-09 [1.12-1.24] FTO www.ncbi.nlm.nih.gov/pubmed/30566433
## rs11642015-C 16 53802494 1.000 rs11642015(T)/rs1421085(C) Urinary sodium excretion 7e-23 7.00e-23 [0.0099-0.0149] unit decrease FTO www.ncbi.nlm.nih.gov/pubmed/31409800
## rs1558902-A 16 53803574 1.000 rs1558902(T)/rs1421085(T) Waist circumference 4e-101 4.00e-101 [0.067-0.081] unit increase FTO www.ncbi.nlm.nih.gov/pubmed/25673412
## rs1121980-A 16 53809247 0.956 rs1121980(G)/rs1421085(T) Waist-hip ratio 1e-38 4.07e-37 [0.037-0.05] unit increase FTO www.ncbi.nlm.nih.gov/pubmed/25673412
## rs8050136-A 16 53816275 0.918 rs8050136(C)/rs1421085(T) Weight 5e-36 3.34e-33 [5.95-8.15] percentage SD increase FTO www.ncbi.nlm.nih.gov/pubmed/19079260
##
##
## * SNP - rs999474 (UBE2Z):
##
##
## snp chrm posh19 LD_R2 LD_alleles trait p adjusted_p effect gene url
## ------------ ----- --------- ------ ------------------------- -------------------------------------------- ------ ----------- ----------------------------- --------- -------------------------------------
## rs1058018-T 17 47000251 0.984 rs1058018(T)/rs999474(G) Educational attainment (years of education) 9e-10 1.24e-09 [0.006-0.0114] unit decrease UBE2Z www.ncbi.nlm.nih.gov/pubmed/30038396
## rs318095-T 17 46974734 0.815 rs318095(T)/rs999474(G) Height 2e-16 1.15e-13 [0.018-0.03] unit increase SUMO2P17 www.ncbi.nlm.nih.gov/pubmed/25282103
## rs9894220-G 17 46989154 1.000 rs9894220(G)/rs999474(G) Type 2 diabetes 2e-13 2.00e-13 [0.043-0.074] unit decrease UBE2Z www.ncbi.nlm.nih.gov/pubmed/30054458
##
##
## * SNP - rs303757 (RMC1):
##
##
## snp chrm posh19 LD_R2 LD_alleles trait p adjusted_p effect gene url
## ------------ ----- --------- ------ ------------------------- -------------------------------------------- ------ ----------- ------------------------------ ------------- -------------------------------------
## rs303753-A 18 21074922 0.991 rs303753(G)/rs303757(T) Alcohol consumption (drinks per week) 4e-08 4.59e-08 [0.0083-0.0175] unit decrease RIOK3 - RMC1 www.ncbi.nlm.nih.gov/pubmed/30643258
## rs1788820-G 18 21101944 0.965 rs1788820(G)/rs303757(T) Bitter alcoholic beverage consumption 2e-10 4.11e-10 [0.0045-0.0089] unit increase NPC1, RMC1 www.ncbi.nlm.nih.gov/pubmed/31046077
## rs1788799-C 18 21124945 0.965 .(G)/rs303757(T) Body mass index 1e-17 3.66e-17 [0.012-0.02] unit increase NPC1 www.ncbi.nlm.nih.gov/pubmed/29273807
## rs303752-A 18 21074255 0.696 rs303752(G)/rs303757(T) Educational attainment (MTAG) 3e-12 5.87e-09 [0.0068-0.0118] unit decrease RIOK3 - RMC1 www.ncbi.nlm.nih.gov/pubmed/30038396
## rs1623003-T 18 21165163 0.893 rs1623003(T)/rs303757(T) Educational attainment (years of education) 4e-12 5.50e-11 [0.0074-0.0132] unit increase NPC1 www.ncbi.nlm.nih.gov/pubmed/30038396
## rs303760-T 18 21083738 0.978 rs303760(T)/rs303757(G) Hand grip strength 2e-08 2.85e-08 [0.0014-0.0026] unit decrease RMC1 www.ncbi.nlm.nih.gov/pubmed/29691431
Interestingly, we can see that all these loci have been associated with
some of the risk factors used to create the prior. The variants near
USP4 and MAD1L1 have been associated with Educational Attainment. The
variants near GNPDA2 and FTO have been associated with Body Mass Index.
The variant near ZPR1 has been associated with Coronary Artery Disease
and LDL Cholesterol. Variants in LD with the variant identified near
HNF1A have been associated with Coronary Artery Disease and LDL
cholesterol. The variant near ATXN2L has been associated with
Educational Attainment, and variants in LD have been associated with
Body Mass Index. A variant in LD with the variant identified near UBE2Z
has been associated with Educational Attainment. Variants in LD with the
variant identified near RMC1 have been associated with Body Mass Index
and Educational Attainment.
Results - Direct Effects
With this approach, we identified 4 SNPs having significant direct
effects. Since there are only a small number of hits here, look at all
of them in details:
# all direct hits
extract_results_bGWAS(Lifespan_bGWAS, results="direct") %>%
mutate(p_direct = as.character(format(p_direct, scientific=T, digits=3))) %>%
arrange(chrm_UK10K, pos_UK10K) -> Direct_Hits
Gene_Info_Direct <- do.call(rbind.data.frame,
apply(Direct_Hits, 1, function(x) get_geneInfo(as.numeric(x[2]), as.numeric(x[3]), x[4], x[5])))
Gene_Info_Direct %>%
bind_cols(Direct_Hits) -> Direct_Hits
knitr::kable(Direct_Hits, digits=3)
| Function | Gene | Distance | rsid | chrm_UK10K | pos_UK10K | alt | ref | beta | se | z_obs | mu_direct_estimate | mu_direct_std_error | beta_direct_estimate | beta_direct_std_error | z_direct | p_direct | CRR |
| :------- | :---- | -------: | :--------- | ----------: | ---------: | :-- | :-- | ------: | ----: | -------: | -------------------: | ---------------------: | ---------------------: | -----------------------: | --------: | :-------- | ----: |
| intronic | LPA | 0 | rs55730499 | 6 | 161005610 | T | C | -0.076 | 0.007 | -10.258 | -8.295 | 1.166 | -0.061 | 0.009 | -7.114 | 1.13e-12 | 0.809 |
| intronic | RAD52 | 0 | rs7307680 | 12 | 1052488 | A | G | -0.026 | 0.005 | -5.286 | -6.196 | 1.134 | -0.031 | 0.006 | -5.465 | 4.64e-08 | 1.172 |
| intronic | HYKK | 0 | rs8042849 | 15 | 78817929 | T | C | 0.044 | 0.004 | 10.659 | 10.395 | 1.118 | 0.043 | 0.005 | 9.300 | 1.41e-20 | 0.975 |
| exonic | APOE | 0 | rs429358 | 19 | 45411941 | T | C | 0.106 | 0.005 | 19.328 | 17.473 | 1.228 | 0.095 | 0.007 | 14.232 | 5.79e-46 | 0.904 |
Direct_Hits %>%
transmute(rs=rsid,
gene = Gene,
color="#932735") -> my_SNPsDirect
manhattan_plot_bGWAS(Lifespan_bGWAS, SNPs=my_SNPsDirect, results = "direct")
variant near LPA
# look if significant in conventional GWAS / using BF or posterior p-values)
# look at SNPs in a 100kb window around (to assess significance in conventional GWAS / bGWAS results)
dist=100000
Direct_Hits %>% dplyr::slice(1) %>% pull(chrm_UK10K) -> chr_LPA
Direct_Hits %>% dplyr::slice(1) %>% pull(pos_UK10K) -> pos_LPA
extract_results_bGWAS(Lifespan_bGWAS, SNPs = "all", results="everything") %>%
filter(chrm_UK10K == chr_LPA,
pos_UK10K > pos_LPA - dist,
pos_UK10K < pos_LPA + dist) %>%
mutate(p_obs = 2*pnorm(-abs(z_obs))) %>%
dplyr::select(p_obs, BF_p, p_posterior) -> res_LPA
# significant in conventional GWAS
any(res_LPA$p_obs<5e-8)
[1] TRUE
# significant in bGWAS
any(res_LPA$BF_p<5e-8)
[1] TRUE
# significant using posterior effects
any(res_LPA$p_posterior<5e-8)
[1] TRUE
# Association with other traits
LPA = Direct_Hits[1,]
knitr::kable(extract_results_bGWAS(Lifespan_bGWAS, SNPs = "all", results="everything") %>%
filter(rsid == LPA$rsid) %>%
mutate(BF = as.character(format(BF, scientific=T, digits=3)),
BF_p = as.character(format(BF_p, scientific=T, digits=3)),
p_direct = as.character(format(p_direct, scientific=T, digits=3)),
p_posterior = as.character(format(p_posterior, scientific=T, digits=3))),
digits=3)
| chrm_UK10K | pos_UK10K | rsid | alt | ref | beta | se | z_obs | mu_prior_estimate | mu_prior_std_error | mu_posterior_estimate | mu_posterior_std_error | z_posterior | mu_direct_estimate | mu_direct_std_error | z_direct | beta_prior_estimate | beta_prior_std_error | beta_posterior_estimate | beta_posterior_std_error | beta_direct_estimate | beta_direct_std_error | CRR | BF | BF_p | p_posterior | p_direct |
| ----------: | ---------: | :--------- | :-- | :-- | ------: | ----: | -------: | ------------------: | --------------------: | ----------------------: | ------------------------: | -----------: | -------------------: | ---------------------: | --------: | --------------------: | ----------------------: | ------------------------: | --------------------------: | ---------------------: | -----------------------: | ----: | :------- | :------- | :----------- | :-------- |
| 6 | 161005610 | rs55730499 | T | C | -0.076 | 0.007 | -10.258 | -1.963 | 0.6 | -4.157 | 0.514 | -8.083 | -8.295 | 1.166 | -7.114 | -0.015 | 0.004 | -0.031 | 0.004 | -0.061 | 0.009 | 0.809 | 6.21e+11 | 2.11e-21 | 6.33e-16 | 1.13e-12 |
Info_LPA = get_associatedTraits(LPA$rsid, LPA$chrm_UK10K, LPA$pos_UK10K,
LD=0.1, distance=100000, P=5e-8,
gwascatdata = my_ebicat37)
# format for nice kable output
Info_LPA %>%
mutate(p=as.character(format(p, scientific=T, digits=3)),
adjusted_p = as.character(format(adjusted_p, scientific=T, digits=3))) -> Info_LPA
Reported Associations for variant near LPA
##
##
## snp chrm posh19 LD_R2 LD_alleles trait p adjusted_p effect gene url
## -------------- ----- ---------- ------ ----------------------------- --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ------- ----------- ----------------------------------- ----- -------------------------------------
## rs55730499-T 6 161005610 NA NA Coronary artery disease 3e-154 NA [0.31-0.36] unit increase LPA www.ncbi.nlm.nih.gov/pubmed/29212778
## rs55730499-T 6 161005610 NA NA Lipoprotein(a) levels adjusted for apolipoprotein(a) isoforms 4e-163 NA [15.63-17.91] unit increase LPA www.ncbi.nlm.nih.gov/pubmed/28512139
## rs55730499-T 6 161005610 NA NA Parental lifespan 9e-11 NA years decrease LPA www.ncbi.nlm.nih.gov/pubmed/29030599
## rs10455872-G 6 161010118 0.972 rs10455872(G)/rs55730499(T) Aortic valve stenosis 2e-31 1.40e-30 [1.37-1.56] LPA www.ncbi.nlm.nih.gov/pubmed/29511194
## rs10455872-G 6 161010118 0.972 rs10455872(G)/rs55730499(T) Aortic-valve calcification 3e-11 5.70e-11 [1.66-2.53] LPA www.ncbi.nlm.nih.gov/pubmed/23388002
## rs74617384-T 6 160997118 0.986 rs74617384(T)/rs55730499(T) Cholesterol, total 3e-09 3.88e-09 [0.081-0.161] mg/dl increase LPA www.ncbi.nlm.nih.gov/pubmed/28270201
## rs10455872-G 6 161010118 0.972 rs10455872(G)/rs55730499(T) Coronary artery disease (myocardial infarction, percutaneous transluminal coronary angioplasty, coronary artery bypass grafting, angina or chromic ischemic heart disease) 2e-49 4.51e-48 [1.27-1.36] LPA www.ncbi.nlm.nih.gov/pubmed/28714975
## rs74617384-T 6 160997118 0.986 rs74617384(T)/rs55730499(T) Coronary artery disease in diabetes 3e-12 4.27e-12 [1.26-1.51] LPA www.ncbi.nlm.nih.gov/pubmed/30003307
## rs10455872-? 6 161010118 0.972 rs10455872(G)/rs55730499(T) Coronary artery disease or ischemic stroke 2e-12 4.09e-12 LPA www.ncbi.nlm.nih.gov/pubmed/24262325
## rs10455872-? 6 161010118 0.972 rs10455872(G)/rs55730499(T) Coronary artery disease or large artery stroke 9e-14 2.01e-13 LPA www.ncbi.nlm.nih.gov/pubmed/24262325
## rs74617384-A 6 160997118 0.986 rs74617384(T)/rs55730499(T) LDL cholesterol x physical activity interaction (2df test) 4e-46 1.71e-45 LPA www.ncbi.nlm.nih.gov/pubmed/30670697
## rs118039278-A 6 160985526 0.972 rs118039278(G)/rs55730499(T) Lipoprotein (a) levels 0e+00 0.00e+00 [31.14-33.72] mg/dL increase LPA www.ncbi.nlm.nih.gov/pubmed/28512139
## rs10455872-G 6 161010118 0.972 rs10455872(G)/rs55730499(T) Lipoprotein phospholipase A2 activity in cardiovascular disease 4e-13 8.57e-13 [5.26-9.18] (nmol/min/ml) increase LPA www.ncbi.nlm.nih.gov/pubmed/28753643
## rs10455872-G 6 161010118 0.972 rs10455872(G)/rs55730499(T) Lipoprotein-associated phospholipase A2 activity change in response to statin therapy 2e-16 5.30e-16 [0.82-2.78] percent increase LPA www.ncbi.nlm.nih.gov/pubmed/23118302
## rs10455872-? 6 161010118 0.972 rs10455872(G)/rs55730499(T) Low density lipoprotein cholesterol levels 5e-17 1.38e-16 [6.2-9.98] unit increase LPA www.ncbi.nlm.nih.gov/pubmed/31217584
## rs10455872-G 6 161010118 0.972 rs10455872(G)/rs55730499(T) Lp (a) levels 5e-39 5.73e-38 [1.08-1.48] mg/dL increase LPA www.ncbi.nlm.nih.gov/pubmed/21900290
## rs10455872-G 6 161010118 0.972 rs10455872(G)/rs55730499(T) Metabolite levels (lipoprotein measures) 1e-12 2.09e-12 [0.14-0.26] unit decrease LPA www.ncbi.nlm.nih.gov/pubmed/27005778
## rs10455872-G 6 161010118 0.972 rs10455872(G)/rs55730499(T) Myocardial infarction 9e-27 4.66e-26 [1.27-1.4] LPA www.ncbi.nlm.nih.gov/pubmed/26343387
## rs10455872-A 6 161010118 0.972 rs10455872(G)/rs55730499(T) Response to statin therapy 1e-09 1.72e-09 [0.20-0.36] mmol/L decrease LPA www.ncbi.nlm.nih.gov/pubmed/22368281
## rs10455872-G 6 161010118 0.972 rs10455872(G)/rs55730499(T) Response to statins (LDL cholesterol change) 7e-44 1.10e-42 [0.044-0.060] unit increase LPA www.ncbi.nlm.nih.gov/pubmed/25350695
## rs10455872-? 6 161010118 0.972 rs10455872(G)/rs55730499(T) Total cholesterol levels 2e-15 4.97e-15 [6.57-10.89] unit increase LPA www.ncbi.nlm.nih.gov/pubmed/31217584
The variant near LPA is also significant in the conventional GWAS, and
using BF and posterior effects. It has a quite strong effect in the
conventional GWAS (z=-10.25) but only a part of its effect on lifespan
is going through the risk factors (Coronary Artery Disease and LDL
cholesterol) used to create the prior (moderate prior effect,
mu=-1.963). Some part of the observed effect is likely to be explained
by some risk factors not included here (Lipoprotein levels, Response to
statin therapy), or some direct effects.
variant near RAD52
# look if significant in conventional GWAS / using BF or posterior p-values)
# look at SNPs in a 100kb window around (to assess significance in conventional GWAS / bGWAS results)
dist=100000
Direct_Hits %>% dplyr::slice(2) %>% pull(chrm_UK10K) -> chr_RAD52
Direct_Hits %>% dplyr::slice(2) %>% pull(pos_UK10K) -> pos_RAD52
extract_results_bGWAS(Lifespan_bGWAS, SNPs = "all", results="everything") %>%
filter(chrm_UK10K == chr_RAD52,
pos_UK10K > pos_RAD52 - dist,
pos_UK10K < pos_RAD52 + dist) %>%
mutate(p_obs = 2*pnorm(-abs(z_obs))) %>%
dplyr::select(p_obs, BF_p, p_posterior) -> res_RAD52
# not significant in conventional GWAS
any(res_RAD52$p_obs<5e-8)
[1] FALSE
# not significant in bGWAS
any(res_RAD52$BF_p<5e-8)
[1] FALSE
# not significant using posterior effects
any(res_RAD52$p_posterior<5e-8)
[1] FALSE
# Association with other traits
RAD52 = Direct_Hits[2,]
knitr::kable(extract_results_bGWAS(Lifespan_bGWAS, SNPs = "all", results="everything") %>%
filter(rsid == RAD52$rsid) %>%
mutate(BF = as.character(format(BF, scientific=T, digits=3)),
BF_p = as.character(format(BF_p, scientific=T, digits=3)),
p_direct = as.character(format(p_direct, scientific=T, digits=3)),
p_posterior = as.character(format(p_posterior, scientific=T, digits=3))),
digits=3)
| chrm_UK10K | pos_UK10K | rsid | alt | ref | beta | se | z_obs | mu_prior_estimate | mu_prior_std_error | mu_posterior_estimate | mu_posterior_std_error | z_posterior | mu_direct_estimate | mu_direct_std_error | z_direct | beta_prior_estimate | beta_prior_std_error | beta_posterior_estimate | beta_posterior_std_error | beta_direct_estimate | beta_direct_std_error | CRR | BF | BF_p | p_posterior | p_direct |
| ----------: | ---------: | :-------- | :-- | :-- | ------: | ----: | ------: | ------------------: | --------------------: | ----------------------: | ------------------------: | -----------: | -------------------: | ---------------------: | --------: | --------------------: | ----------------------: | ------------------------: | --------------------------: | ---------------------: | -----------------------: | ----: | :------- | :------- | :----------- | :-------- |
| 12 | 1052488 | rs7307680 | A | G | -0.026 | 0.005 | -5.286 | 0.91 | 0.534 | -0.466 | 0.471 | -0.989 | -6.196 | 1.134 | -5.465 | 0.005 | 0.003 | -0.002 | 0.002 | -0.031 | 0.006 | 1.172 | 3.38e-01 | 9.73e-01 | 3.23e-01 | 4.64e-08 |
Info_RAD52 = get_associatedTraits(RAD52$rsid, RAD52$chrm_UK10K, RAD52$pos_UK10K,
LD=0.1, distance=100000, P=5e-8,
gwascatdata = my_ebicat37)
# format for nice kable output
Info_RAD52 %>%
mutate(p=as.character(format(p, scientific=T, digits=3)),
adjusted_p = as.character(format(adjusted_p, scientific=T, digits=3))) -> Info_RAD52
Reported Associations for variant near RAD52
##
##
## snp chrm posh19 LD_R2 LD_alleles trait p adjusted_p effect gene url
## ------------- ----- ------- ------ --------------------------- --------------------- ------ ----------- -------------------------- ----- -------------------------------------
## rs12301299-C 12 991710 0.567 rs12301299(T)/rs7307680(A) Blood protein levels 2e-14 8.44e-09 [0.19-0.31] unit increase WNK1 www.ncbi.nlm.nih.gov/pubmed/29875488
The variant near RAD52 is not significant in the conventional GWAS,
using BF or posterior effects. It has a quite strong effect in the
conventional GWAS (z=-5.28) but does not have an effect on lifespan
through any of risk factors used to create the prior (small prior effect
in the opposite direction, mu=0.909). The observed effect could be
explained by some risk factors not included here (but no strong
association reported in GWAS catalog for this region), or some direct
effects.
variant near HYKK
# look if significant in conventional GWAS / using BF or posterior p-values)
# look at SNPs in a 100kb window around (to assess significance in conventional GWAS / bGWAS results)
dist=100000
Direct_Hits %>% dplyr::slice(3) %>% pull(chrm_UK10K) -> chr_HYKK
Direct_Hits %>% dplyr::slice(3) %>% pull(pos_UK10K) -> pos_HYKK
extract_results_bGWAS(Lifespan_bGWAS, SNPs = "all", results="everything") %>%
filter(chrm_UK10K == chr_HYKK,
pos_UK10K > pos_HYKK - dist,
pos_UK10K < pos_HYKK + dist) %>%
mutate(p_obs = 2*pnorm(-abs(z_obs))) %>%
dplyr::select(p_obs, BF_p, p_posterior) -> res_HYKK
# significant in conventional GWAS
any(res_HYKK$p_obs<5e-8)
[1] TRUE
# significant in bGWAS
any(res_HYKK$BF_p<5e-8)
[1] TRUE
# not significant using posterior effects
any(res_HYKK$p_posterior<5e-8)
[1] FALSE
# Association with other traits
HYKK = Direct_Hits[3,]
knitr::kable(extract_results_bGWAS(Lifespan_bGWAS, SNPs = "all", results="everything") %>%
filter(rsid == HYKK$rsid) %>%
mutate(BF = as.character(format(BF, scientific=T, digits=3)),
BF_p = as.character(format(BF_p, scientific=T, digits=3)),
p_direct = as.character(format(p_direct, scientific=T, digits=3)),
p_posterior = as.character(format(p_posterior, scientific=T, digits=3))),
digits=3)
| chrm_UK10K | pos_UK10K | rsid | alt | ref | beta | se | z_obs | mu_prior_estimate | mu_prior_std_error | mu_posterior_estimate | mu_posterior_std_error | z_posterior | mu_direct_estimate | mu_direct_std_error | z_direct | beta_prior_estimate | beta_prior_std_error | beta_posterior_estimate | beta_posterior_std_error | beta_direct_estimate | beta_direct_std_error | CRR | BF | BF_p | p_posterior | p_direct |
| ----------: | ---------: | :-------- | :-- | :-- | ----: | ----: | -----: | ------------------: | --------------------: | ----------------------: | ------------------------: | -----------: | -------------------: | ---------------------: | --------: | --------------------: | ----------------------: | ------------------------: | --------------------------: | ---------------------: | -----------------------: | ----: | :------- | :------ | :----------- | :-------- |
| 15 | 78817929 | rs8042849 | T | C | 0.044 | 0.004 | 10.659 | 0.265 | 0.499 | 2.339 | 0.447 | 5.236 | 10.395 | 1.118 | 9.3 | 0.001 | 0.002 | 0.01 | 0.002 | 0.043 | 0.005 | 0.975 | 6.99e+05 | 9.9e-13 | 1.64e-07 | 1.41e-20 |
Info_HYKK = get_associatedTraits(HYKK$rsid, HYKK$chrm_UK10K, HYKK$pos_UK10K,
LD=0.1, distance=100000, P=5e-8,
gwascatdata = my_ebicat37)
# format for nice kable output
Info_HYKK %>%
mutate(p=as.character(format(p, scientific=T, digits=3)),
adjusted_p = as.character(format(adjusted_p, scientific=T, digits=3))) -> Info_HYKK
Reported Associations variant near HYKK
##
##
## snp chrm posh19 LD_R2 LD_alleles trait p adjusted_p effect gene url
## -------------- ----- --------- ------ ---------------------------- ----------------------------------------------------------------------- ------- ----------- ---------------------------------------- ------------------- -------------------------------------
## rs8042849-C 15 78817929 NA NA Parental lifespan 4e-14 NA years decrease HYKK www.ncbi.nlm.nih.gov/pubmed/29030599
## rs8042849-T 15 78817929 NA NA Post bronchodilator FEV1 4e-15 NA NR unit increase HYKK www.ncbi.nlm.nih.gov/pubmed/26634245
## rs8042849-T 15 78817929 NA NA Post bronchodilator FEV1/FVC ratio 3e-15 NA unit increase HYKK www.ncbi.nlm.nih.gov/pubmed/26634245
## rs8031948-T 15 78816057 0.935 rs8031948(T)/rs8042849(C) Airflow obstruction 3e-09 9.74e-09 HYKK www.ncbi.nlm.nih.gov/pubmed/22837378
## rs17486278-C 15 78867482 0.829 rs17486278(C)/rs8042849(C) Chronic obstructive pulmonary disease 2e-28 7.66e-24 [1.15,1.22] CHRNA5 www.ncbi.nlm.nih.gov/pubmed/28166215
## rs12914385-T 15 78898723 0.696 rs12914385(T)/rs8042849(C) Chronic obstructive pulmonary disease (moderate to severe) 6e-14 3.74e-10 [1.20-1.36] CHRNA3 www.ncbi.nlm.nih.gov/pubmed/24621683
## rs12914385-T 15 78898723 0.696 rs12914385(T)/rs8042849(C) Chronic obstructive pulmonary disease (severe) 3e-16 9.12e-12 [1.29-1.51] CHRNA3 www.ncbi.nlm.nih.gov/pubmed/24621683
## rs8192482-T 15 78886198 0.829 rs8192482(T)/rs8042849(C) Current cigarettes per day in chronic obstructive pulmonary disease 2e-15 4.83e-13 [NR] unit increase CHRNA5, CHRNA3 www.ncbi.nlm.nih.gov/pubmed/29767774
## rs112878080-G 15 78900647 0.813 rs141518190(G)/rs8042849(C) Diffusing capacity of carbon monoxide 3e-10 1.35e-08 0.09-0.17 unit decrease CHRNA3 www.ncbi.nlm.nih.gov/pubmed/30694715
## rs12914385-T 15 78898723 0.696 rs12914385(T)/rs8042849(C) Emphysema distribution in smoking 2e-17 1.37e-12 NR unit increase CHRNA3 www.ncbi.nlm.nih.gov/pubmed/27669027
## rs16969968-G 15 78882925 0.829 rs16969968(G)/rs8042849(T) Fagerstr**m test for nicotine dependence 4e-28 1.36e-23 [0.19-0.27] unit decrease CHRNA5, AC027228.2 www.ncbi.nlm.nih.gov/pubmed/31294817
## rs8040868-C 15 78911181 0.678 rs8040868(T)/rs8042849(T) Familial lung cancer 4e-13 2.28e-09 [1.23-1.43] CHRNA3 www.ncbi.nlm.nih.gov/pubmed/29924316
## rs9788721-T 15 78802869 0.979 rs9788721(T)/rs8042849(T) Local histogram emphysema pattern 2e-13 3.54e-13 [0.0050-0.0090] unit decrease HYKK www.ncbi.nlm.nih.gov/pubmed/25006744
## rs55781567-G 15 78857986 0.883 rs55781567(G)/rs8042849(C) Lung adenocarcinoma 3e-48 8.51e-43 [1.235221079-1.318857475] CHRNA5 www.ncbi.nlm.nih.gov/pubmed/28604730
## rs55781567-G 15 78857986 0.883 rs55781567(G)/rs8042849(C) Lung cancer 3e-103 2.30e-91 [1.267092551-1.328476032] CHRNA5 www.ncbi.nlm.nih.gov/pubmed/28604730
## rs55781567-G 15 78857986 0.883 rs55781567(G)/rs8042849(C) Lung cancer in ever smokers 2e-78 1.91e-69 [1.292694434-1.372529125] CHRNA5 www.ncbi.nlm.nih.gov/pubmed/28604730
## rs10519203-G 15 78814046 0.951 rs10519203(G)/rs8042849(T) Mortality 2e-17 1.20e-16 HYKK www.ncbi.nlm.nih.gov/pubmed/27029810
## rs1051730-T 15 78894339 0.817 rs1051730(G)/rs8042849(T) Nicotine dependence 6e-20 1.39e-16 [0.08-0.12] cigarettes per day increase CHRNA3 www.ncbi.nlm.nih.gov/pubmed/18385739
## rs8034191-? 15 78806023 0.919 rs8034191(T)/rs8042849(T) Pre bronchodilator FEV1 3e-09 1.31e-08 unit decrease HYKK www.ncbi.nlm.nih.gov/pubmed/26634245
## rs8034191-? 15 78806023 0.919 rs8034191(T)/rs8042849(T) Pre bronchodilator FEV1/FVC ratio 2e-10 1.08e-09 unit decrease HYKK www.ncbi.nlm.nih.gov/pubmed/26634245
## rs17486278-C 15 78867482 0.829 rs17486278(C)/rs8042849(C) Pulmonary artery enlargement and chronic obstructive pulmonary disease 7e-10 1.98e-08 [1.27-1.59] CHRNA5 www.ncbi.nlm.nih.gov/pubmed/25101718
## rs55853698-G 15 78857939 0.887 rs55853698(T)/rs8042849(T) Small cell lung carcinoma 5e-21 7.92e-19 [1.256072968-1.416074239] CHRNA5 www.ncbi.nlm.nih.gov/pubmed/28604730
## rs1051730-G 15 78894339 0.817 rs1051730(G)/rs8042849(T) Smoking behavior 3e-73 3.44e-60 [0.91-1.13] CPD decrease CHRNA3 www.ncbi.nlm.nih.gov/pubmed/20418890
## rs8040868-C 15 78911181 0.678 rs8040868(T)/rs8042849(T) Squamous cell lung carcinoma 3e-41 1.56e-28 [1.243425108-1.339233734] CHRNA3 www.ncbi.nlm.nih.gov/pubmed/28604730
## rs16969968-G 15 78882925 0.829 rs16969968(G)/rs8042849(T) Time to smoke first cigarette in the morning 1e-17 5.90e-15 [0.05-0.09] unit decrease CHRNA5, AC027228.2 www.ncbi.nlm.nih.gov/pubmed/31294817
The variant near HYKK is significant in the conventional GWAS and using
BF but not posterior effects. It has a quite strong effect in the
conventional GWAS (z=10.65) but does not have an effect on lifespan
through any of risk factors used to create the prior (small prior effect
in the same direction, mu=0.499). The strength of the observed effect is
enough to make the BF significant, even if the prior is not very large.
The observed effect is likely to be explained by some risk factors not
included here (smoking, pulmonary diseases/cancers), or some direct
effects.
variant near APOE
# look if significant in conventional GWAS / using BF or posterior p-values)
# look at SNPs in a 100kb window around (to assess significance in conventional GWAS / bGWAS results)
dist=100000
Direct_Hits %>% dplyr::slice(4) %>% pull(chrm_UK10K) -> chr_APOE
Direct_Hits %>% dplyr::slice(4) %>% pull(pos_UK10K) -> pos_APOE
extract_results_bGWAS(Lifespan_bGWAS, SNPs = "all", results="everything") %>%
filter(chrm_UK10K == chr_APOE,
pos_UK10K > pos_APOE - dist,
pos_UK10K < pos_APOE + dist) %>%
mutate(p_obs = 2*pnorm(-abs(z_obs))) %>%
dplyr::select(p_obs, BF_p, p_posterior) -> res_APOE
# significant in conventional GWAS
any(res_APOE$p_obs<5e-8)
[1] TRUE
# significant in bGWAS
any(res_APOE$BF_p<5e-8)
[1] TRUE
# significant using posterior effects
any(res_APOE$p_posterior<5e-8)
[1] TRUE
# Association with other traits
APOE = Direct_Hits[4,]
knitr::kable(extract_results_bGWAS(Lifespan_bGWAS, SNPs = "all", results="everything") %>%
filter(rsid == APOE$rsid) %>%
mutate(BF = as.character(format(BF, scientific=T, digits=3)),
BF_p = as.character(format(BF_p, scientific=T, digits=3)),
p_direct = as.character(format(p_direct, scientific=T, digits=3)),
p_posterior = as.character(format(p_posterior, scientific=T, digits=3))),
digits=3)
| chrm_UK10K | pos_UK10K | rsid | alt | ref | beta | se | z_obs | mu_prior_estimate | mu_prior_std_error | mu_posterior_estimate | mu_posterior_std_error | z_posterior | mu_direct_estimate | mu_direct_std_error | z_direct | beta_prior_estimate | beta_prior_std_error | beta_posterior_estimate | beta_posterior_std_error | beta_direct_estimate | beta_direct_std_error | CRR | BF | BF_p | p_posterior | p_direct |
| ----------: | ---------: | :------- | :-- | :-- | ----: | ----: | -----: | ------------------: | --------------------: | ----------------------: | ------------------------: | -----------: | -------------------: | ---------------------: | --------: | --------------------: | ----------------------: | ------------------------: | --------------------------: | ---------------------: | -----------------------: | ----: | :------- | :------- | :----------- | :-------- |
| 19 | 45411941 | rs429358 | T | C | 0.106 | 0.005 | 19.328 | 1.854 | 0.712 | 7.736 | 0.58 | 13.334 | 17.473 | 1.228 | 14.232 | 0.01 | 0.004 | 0.042 | 0.003 | 0.095 | 0.007 | 0.904 | 1.11e+37 | 7.06e-70 | 1.47e-40 | 5.79e-46 |
Info_APOE = get_associatedTraits(APOE$rsid, APOE$chrm_UK10K, APOE$pos_UK10K,
LD=0.1, distance=100000, P=5e-8,
gwascatdata = my_ebicat37)
# format for nice kable output
Info_APOE %>%
mutate(p=as.character(format(p, scientific=T, digits=3)),
adjusted_p = as.character(format(adjusted_p, scientific=T, digits=3))) -> Info_APOE
Reported Associations for variant near APOE
##
##
## snp chrm posh19 LD_R2 LD_alleles trait p adjusted_p effect gene url
## ------------- ----- --------- ------ -------------------------- -------------------------------------------------------------------- ------- ----------- -------------------------------------------- -------------------- -------------------------------------
## rs429358-? 19 45411941 NA NA Advanced age-related macular degeneration 2e-42 NA APOE www.ncbi.nlm.nih.gov/pubmed/26691988
## rs429358-? 19 45411941 NA NA Alzheimer's disease 2e-91 NA APOE www.ncbi.nlm.nih.gov/pubmed/30636644
## rs429358-C 19 45411941 NA NA Alzheimer's disease biomarkers 5e-14 NA APOE www.ncbi.nlm.nih.gov/pubmed/23419831
## rs429358-? 19 45411941 NA NA Alzheimer's disease progression score 4e-33 NA APOE www.ncbi.nlm.nih.gov/pubmed/29860282
## rs429358-C 19 45411941 NA NA Blood protein levels 0e+00 NA [1.3-1.38] unit decrease APOE www.ncbi.nlm.nih.gov/pubmed/29875488
## rs429358-? 19 45411941 NA NA Brain imaging 1e-09 NA APOE www.ncbi.nlm.nih.gov/pubmed/20100581
## rs429358-T 19 45411941 NA NA Cerebral amyloid deposition (PET imaging) 8e-32 NA [NR] unit decrease APOE www.ncbi.nlm.nih.gov/pubmed/26252872
## rs429358-C 19 45411941 NA NA Cerebral amyloid deposition positivity (PET imaging) 5e-20 NA [4.39-5.05] APOE www.ncbi.nlm.nih.gov/pubmed/26252872
## rs429358-? 19 45411941 NA NA Cerebrospinal AB1-42 levels in Alzheimer's disease dementia 4e-17 NA [0.30-0.50] unit decrease APOE www.ncbi.nlm.nih.gov/pubmed/25027320
## rs429358-? 19 45411941 NA NA Cognitive decline (age-related) 2e-14 NA unit decrease APOE www.ncbi.nlm.nih.gov/pubmed/28078323
## rs429358-? 19 45411941 NA NA Cortical amyloid beta load 3e-50 NA APOE www.ncbi.nlm.nih.gov/pubmed/29860282
## rs429358-C 19 45411941 NA NA Dementia with Lewy bodies 3e-64 NA [2.22-2.74] APOE www.ncbi.nlm.nih.gov/pubmed/29263008
## rs429358-T 19 45411941 NA NA HDL cholesterol 1e-14 NA [0.048-0.084] s.d. increase APOE www.ncbi.nlm.nih.gov/pubmed/25961943
## rs429358-? 19 45411941 NA NA Hippocampal volume 1e-10 NA APOE www.ncbi.nlm.nih.gov/pubmed/29860282
## rs429358-C 19 45411941 NA NA Lewy body disease 1e-12 NA [0.36-0.64] unit decrease APOE www.ncbi.nlm.nih.gov/pubmed/25188341
## rs429358-T 19 45411941 NA NA Longevity (age >90th survival percentile) 1e-61 NA [1.52-1.82] APOE www.ncbi.nlm.nih.gov/pubmed/31413261
## rs429358-T 19 45411941 NA NA Longevity (age >99th survival percentile) 1e-36 NA [1.64-2] APOE www.ncbi.nlm.nih.gov/pubmed/31413261
## rs429358-T 19 45411941 NA NA Moderate to vigorous physical activity levels 6e-13 NA unit decrease APOE www.ncbi.nlm.nih.gov/pubmed/29899525
## rs429358-C 19 45411941 NA NA Mortality 1e-20 NA APOE www.ncbi.nlm.nih.gov/pubmed/27029810
## rs429358-C 19 45411941 NA NA Neuritic plaque 9e-25 NA [1-1.47] unit increase APOE www.ncbi.nlm.nih.gov/pubmed/31497858
## rs429358-? 19 45411941 NA NA Neuritic plaques or cerebral amyloid angiopathy (pleiotropy) 5e-40 NA APOE www.ncbi.nlm.nih.gov/pubmed/29458411
## rs429358-? 19 45411941 NA NA Neuritic plaques or neurofibrillary tangles (pleiotropy) 3e-47 NA APOE www.ncbi.nlm.nih.gov/pubmed/29458411
## rs429358-C 19 45411941 NA NA Neurofibrillary tangles 3e-31 NA [1.25-1.75] unit increase APOE www.ncbi.nlm.nih.gov/pubmed/31497858
## rs429358-? 19 45411941 NA NA Neurofibrillary tangles or cerebral amyloid angiopathy (pleiotropy) 6e-35 NA APOE www.ncbi.nlm.nih.gov/pubmed/29458411
## rs429358-C 19 45411941 NA NA Parental lifespan 1e-27 NA years decrease APOE www.ncbi.nlm.nih.gov/pubmed/29030599
## rs4420638-? 19 45422946 0.686 rs4420638(G)/rs429358(T) Age-related disease endophenotypes 2e-25 6.10e-18 APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/27790247
## rs4420638-? 19 45422946 0.686 rs4420638(G)/rs429358(T) Age-related diseases, mortality and associated endophenotypes 1e-22 4.41e-16 APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/27790247
## rs4420638-A 19 45422946 0.686 rs4420638(G)/rs429358(T) Age-related macular degeneration 2e-20 1.70e-14 [1.24-1.36] APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/23455636
## rs56131196-A 19 45422846 0.686 rs56131196(G)/rs429358(T) Alzheimer disease and age of onset 3e-24 3.94e-17 unit increase APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/26830138
## rs4420638-? 19 45422946 0.686 rs4420638(G)/rs429358(T) Alzheimer's disease (age of onset) 1e-12 3.51e-09 unit increase APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/22005931
## rs4420638-? 19 45422946 0.686 rs4420638(G)/rs429358(T) Alzheimer's disease (late onset) 1e-39 8.78e-28 [NR] APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/17474819
## rs1081105-C 19 45412955 0.173 rs1081105(C)/rs429358(C) Alzheimer's disease or family history of Alzheimer's disease 1e-231 1.09e-41 NR z-unit increase AC011481.4 www.ncbi.nlm.nih.gov/pubmed/30617256
## rs77301115-? 19 45396973 0.171 rs77301115(G)/rs429358(T) Alzheimer's disease or HDL levels (pleiotropy) 7e-74 5.80e-14 AC011481.1 www.ncbi.nlm.nih.gov/pubmed/30805717
## rs4420638-G 19 45422946 0.686 rs4420638(G)/rs429358(T) C-reactive protein 5e-27 4.81e-19 [18.1-25.3] % decrease APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/19567438
## rs4420638-A 19 45422946 0.686 rs4420638(G)/rs429358(T) C-reactive protein levels 9e-139 8.16e-96 [0.22-0.26] unit increase APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/21300955
## rs4420638-A 19 45422946 0.686 rs4420638(G)/rs429358(T) C-reactive protein levels or HDL-cholesterol levels (pleiotropy) 2e-164 2.00e-113 APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/27286809
## rs4420638-A 19 45422946 0.686 rs4420638(G)/rs429358(T) C-reactive protein levels or LDL-cholesterol levels (pleiotropy) 1e-283 2.66e-195 APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/27286809
## rs4420638-A 19 45422946 0.686 rs4420638(G)/rs429358(T) C-reactive protein levels or total cholesterol levels (pleiotropy) 4e-249 1.48e-171 APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/27286809
## rs4420638-A 19 45422946 0.686 rs4420638(G)/rs429358(T) C-reactive protein levels or triglyceride levels (pleiotropy) 2e-171 3.14e-118 APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/27286809
## rs6857-T 19 45392254 0.690 rs6857(T)/rs429358(C) Cerebral amyloid angiopathy 3e-21 3.87e-15 [0.53-0.81] unit increase AC011481.2, NECTIN2 www.ncbi.nlm.nih.gov/pubmed/25188341
## rs769449-A 19 45410002 0.766 rs769449(G)/rs429358(T) Cerebrospinal fluid AB1-42 levels 5e-94 1.81e-72 [0.091-0.111] unit decrease APOE www.ncbi.nlm.nih.gov/pubmed/28247064
## rs4420638-? 19 45422946 0.686 rs4420638(G)/rs429358(T) Cerebrospinal fluid p-Tau181p:AB1-42 ratio 3e-27 3.38e-19 [NR] unit decrease APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/30319691
## rs4420638-? 19 45422946 0.686 rs4420638(G)/rs429358(T) Cerebrospinal fluid t-tau levels 2e-13 1.15e-09 APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/28641921
## rs769449-? 19 45410002 0.766 rs769449(G)/rs429358(T) Cerebrospinal fluid t-tau:AB1-42 ratio 4e-26 2.18e-20 [NR] unit decrease APOE www.ncbi.nlm.nih.gov/pubmed/30319691
## rs769449-A 19 45410002 0.766 rs769449(G)/rs429358(T) Cerebrospinal P-tau181p levels 5e-33 1.09e-25 [0.067-0.091] unit increase APOE www.ncbi.nlm.nih.gov/pubmed/28247064
## rs769449-A 19 45410002 0.766 rs769449(G)/rs429358(T) Cerebrospinal T-tau levels 4e-29 1.08e-22 [0.064-0.092] unit increase APOE www.ncbi.nlm.nih.gov/pubmed/28247064
## rs4420638-G 19 45422946 0.686 rs4420638(G)/rs429358(T) Cholesterol, total 1e-149 2.47e-103 [NR] unit increase APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/24097068
## rs4420638-? 19 45422946 0.686 rs4420638(G)/rs429358(T) Cingulate cortical amyloid beta load 5e-21 6.53e-15 APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/26421299
## rs4420638-? 19 45422946 0.686 rs4420638(G)/rs429358(T) Cognitive decline 4e-27 4.12e-19 APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/22054870
## rs6857-C 19 45392254 0.690 rs6857(T)/rs429358(C) Cognitive impairment 4e-12 8.33e-09 [1.38-2.53] AC011481.2, NECTIN2 www.ncbi.nlm.nih.gov/pubmed/31201950
## rs769449-? 19 45410002 0.766 rs769449(G)/rs429358(T) Cognitive impairment test score 2e-14 2.14e-11 [NR] unit decrease APOE www.ncbi.nlm.nih.gov/pubmed/30319691
## rs6857-T 19 45392254 0.690 rs6857(T)/rs429358(C) Dementia and core Alzheimer's disease neuropathologic changes 2e-62 1.29e-43 [1.42-1.8] unit increase AC011481.2, NECTIN2 www.ncbi.nlm.nih.gov/pubmed/25188341
## rs75627662-T 19 45413576 0.450 rs75627662(T)/rs429358(C) Family history of Alzheimer's disease 1e-295 3.69e-134 NR z-score increase AC011481.4 www.ncbi.nlm.nih.gov/pubmed/30617256
## rs4420638-A 19 45422946 0.686 rs4420638(G)/rs429358(T) HDL cholesterol levels 6e-34 8.28e-24 [0.054-0.08] unit increase (EA Beta values) APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/28334899
## rs4420638-G 19 45422946 0.686 rs4420638(G)/rs429358(T) LDL cholesterol 2e-178 4.93e-123 [NR] unit increase APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/24097068
## rs4420638-A 19 45422946 0.686 rs4420638(G)/rs429358(T) LDL cholesterol levels 2e-82 3.99e-57 [4.94-6.06] unit decrease APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/30926973
## rs4420638-G 19 45422946 0.686 rs4420638(G)/rs429358(T) Lipid traits 1e-14 1.45e-10 unit increase APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/24023261
## rs4420638-A 19 45422946 0.686 rs4420638(G)/rs429358(T) Lipoprotein-associated phospholipase A2 activity and mass 5e-30 4.13e-21 [0.044-0.064] unit decrease APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/22003152
## rs4420638-? 19 45422946 0.686 rs4420638(G)/rs429358(T) Longevity 2e-16 9.76e-12 APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/21740922
## rs4420638-? 19 45422946 0.686 rs4420638(G)/rs429358(T) Longevity (85 years and older) 2e-26 1.25e-18 [1.18-1.27] APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/24688116
## rs4420638-? 19 45422946 0.686 rs4420638(G)/rs429358(T) Longevity (90 years and older) 3e-36 2.16e-25 [1.32-1.47] APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/24688116
## rs769449-? 19 45410002 0.766 rs769449(G)/rs429358(T) Low density lipoprotein cholesterol levels 3e-22 2.07e-17 [4.86-7.31] unit increase APOE www.ncbi.nlm.nih.gov/pubmed/31217584
## rs157582-? 19 45396219 0.524 rs157582(T)/rs429358(T) Psychosis and Alzheimer's disease 9e-52 6.12e-28 [NR] TOMM40 www.ncbi.nlm.nih.gov/pubmed/22005930
## rs4420638-G 19 45422946 0.686 rs4420638(G)/rs429358(T) Red cell distribution width 2e-25 6.10e-18 [0.041-0.061] unit decrease APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/28957414
## rs769449-? 19 45410002 0.766 rs769449(G)/rs429358(T) Total cholesterol levels 5e-19 6.18e-15 [4.88-7.63] unit increase APOE www.ncbi.nlm.nih.gov/pubmed/31217584
## rs438811-T 19 45416741 0.634 rs438811(T)/rs429358(T) Triglycerides 9e-37 6.16e-24 [0.08-0.112] s.d. increase AC011481.3 www.ncbi.nlm.nih.gov/pubmed/25961943
## rs4420638-G 19 45422946 0.686 rs4420638(G)/rs429358(T) Verbal declarative memory 1e-16 6.05e-12 APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/25648963
## rs769449-A 19 45410002 0.766 rs769449(G)/rs429358(T) Verbal memory performance (residualized delayed recall level) 3e-12 1.02e-09 unit decrease APOE www.ncbi.nlm.nih.gov/pubmed/28800603
The variant near APOE is significant in the conventional GWAS and using
BF but not posterior effects. It has a very strong effect in the
conventional GWAS (z=19.32) but only a part of its effect on lifespan is
going through the risk factors used to create the prior (moderate prior
effect, mu=1.854). Some part of the observed effect is likely to be
explained by some risk factors not included here (Alzheimer, dementia,
cognitive decline, C-reactive protein…), or some direct effects.
n-mounier/bGWAS documentation built on Oct. 11, 2023, 1:39 a.m.
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Lifespan Analysis
- Description
- Risk factors (Prior GWASs) used
- Results - BF
- Results - Posterior Effects
- Results - Direct Effects
Description
In this example, we will use the data from Timmers et
al to apply our Bayesian GWAS
approach to study lifespan.
Here, we assume that the bGWAS package is already installed, that the
Z-matrix files have already been downloaded and stored in
"~/ZMatrices". If that is not the case, please follow the steps
described here.
library(bGWAS) # bGWAS github version: v.1.0.2
# Download data to working directory (~460 MB) if not already here
if(!file.exists("lifegen_phase2_bothpl_alldr_2017_09_18.tsv.gz")) download.file(url = "https://datashare.is.ed.ac.uk/bitstream/handle/10283/3209/lifegen_phase2_bothpl_alldr_2017_09_18.tsv.gz?sequence=1&isAllowed=y", destfile = "lifegen_phase2_bothpl_alldr_2017_09_18.tsv.gz")
Now that we have the data in our working directory, we can launch the analysis (with default parameters):
Lifespan_bGWAS = bGWAS(name = "Lifespan_Timmers2019",
GWAS = "lifegen_phase2_bothpl_alldr_2017_09_18.tsv.gz")
Lifespan_bGWAS = bGWAS(name = "Lifespan_Timmers2019",
GWAS = "lifegen_phase2_bothpl_alldr_2017_09_18.tsv.gz")
## <<< Preparation of analysis >>>
## > Checking parameters
## The name of your analysis is: "Lifespan_Timmers2019".
## The Z-Matrix files are stored in "/Users/nmounier/ZMatrices".
## # Preparation of the data...
## The conventional GWAS used as input is: "lifegen_phase2_bothpl_alldr_2017_09_18.tsv.gz".
## SNPID column, ok - ALT column, ok - REF column, ok - BETA column, ok - SE column, ok
## Posterior effects will be rescaled using BETA and SE.The analysis will be run in the folder: "/Users/nmounier/Documents/SGG/Projects/Packaging/bGWAS/doc".
## The p-value threshold used for selecting MR instruments is: 1e-06.
## The minimum number instruments required for each trait is: 3.
## The distance used for pruning MR instruments is: 500Kb.
## Distance-based pruning will be used for MR instruments.
## No shrinkage applied before performing MR.The p-value threshold used for stepwise selection will be derived according to the number of Prior GWASs used.
## Using MR_shrinkage as default for prior_shrinkage:No shrinkage applied before performing calculating the prior.The p-value threshold used for stepwise selection will be derived according to the number of Prior GWASs used.
## Significant SNPs will be identified according to p-value. The threshold used is :5e-08.
## The distance used for pruning results is: 500Kb.
## Distance-based pruning will be used for results.
## <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
## <<< Identification of significant prior GWASs for MR >>>
## > Creating the Z-Matrix of strong instruments
## # Loading the ZMatrix...
## Selecting studies :
## 38 studies
## 209,840 SNPs
## # Adding data from the conventional GWAS :
## "lifegen_phase2_bothpl_alldr_2017_09_18.tsv.gz"
## Done!
## 197,681 SNPs in common between prior studies and the conventional GWAS
## # Thresholding...
## 134,807 SNPs left after thresholding
## Neuroticism (GPC) - Smoking - ever smoked (TAG) - Smoking - age of onset (TAG) : removed (less than 3 instrument after thresholding)
## 35 studies left after thresholding
## Pruning MR instruments...
## distance : 500Kb
## 1,934 SNPs left after pruning
## Anorexia (GCAN) - Openness to Experience (GPC) - Extraversion (GPC) - Insulin (MAGIC) - 2010 - HOMA-IR (MAGIC) - Depression (PGC) - Autism (PGC) - Smoking - cigarettes per day (TAG) - Smoking - former smoker (TAG) : removed (less than 3 strong instrument after pruning)
## 26 studies left after thresholding+pruning
## 1,927 SNPs left after removing studies with only one strong instrument
## > Performing MR
## #Preparation of the MR analyses to identify significant studies...
## Conventionnal GWAS of interest : lifegen_phase2_bothpl_alldr_2017_09_18.tsv.gz
## # Univariate regressions for each trait...
## Number of trait-specific instruments per univariate regression:
## . Body Mass Index (GIANT) : 89
## . Schizophrenia (PGC) - 2014 : 6
## . Coronary Artery Disease (CARDIoGRAM) : 12
## . Type 2 Diabetes (DIAGRAM) : 12
## . Years of Schooling (SSGAC) : 80
## . Glucose (ENGAGE) : 11
## . Crohns Disease (IBD) : 43
## . Ulcerative Colitis (IBD) : 75
## . HDL Cholesterol (GLGC) : 72
## . LDL Cholesterol (GLGC) : 57
## . Total Cholesterol (GLGC) : 73
## . Triglycerides (GLGC) : 51
## . Glucose (MAGIC) - 2010 : 10
## . HOMA-B (MAGIC) : 6
## . Glucose (MAGIC) : 17
## . Insulin (MAGIC) : 5
## . Heart Rate (HRgene) : 14
## . Height (GIANT) : 522
## . Parkinsons : 382
## . Neuroblastoma : 353
## . Multiple Sclerosis : 153
## . Systolic Blood Pressure (ICBP) : 8
## . Diastolic Blood Pressure (ICBP) : 9
## . Schizophrenia (PGC) - 2013 : 19
## . Schizophrenia (PGC) : 107
## . College Completion (SSGAC) : 3
## Done!
## # Stepwise selection (all traits)...
## The p-value threshold used for stepwise selection is 0.0019 (26 Prior GWASs tested).
## Studies tested (reaching p<0.05 in univariate models) :
## Years of Schooling (SSGAC)
## Body Mass Index (GIANT)
## Coronary Artery Disease (CARDIoGRAM)
## Ulcerative Colitis (IBD)
## HDL Cholesterol (GLGC)
## LDL Cholesterol (GLGC)
## Total Cholesterol (GLGC)
## Triglycerides (GLGC)
## Glucose (MAGIC) - 2010
## Glucose (MAGIC)
## Systolic Blood Pressure (ICBP)
## Diastolic Blood Pressure (ICBP)
## Adding the first study :Years of Schooling (SSGAC)
## iteration 1: 1 studies
## #Run model
## #Test if any study can be added with p<0.0019
## Adding one study :LDL Cholesterol (GLGC)
## Done!
## #Update model
## #Test if any study has p>0.0019 now
## iteration 2: 2 studies
## #Run model
## #Test if any study can be added with p<0.0019
## Adding one study :Body Mass Index (GIANT)
## Done!
## #Update model
## #Test if any study has p>0.0019 now
## iteration 3: 3 studies
## #Run model
## #Test if any study can be added with p<0.0019
## Adding one study :Coronary Artery Disease (CARDIoGRAM)
## Done!
## #Update model
## #Test if any study has p>0.0019 now
## iteration 4: 4 studies
## #Run model
## #Test if any study can be added with p<0.0019
## Adding one study :Diastolic Blood Pressure (ICBP)
## Done!
## #Update model
## #Test if any study has p>0.0019 now
## iteration 5: 5 studies
## #Run model
## #Test if any study can be added with p<0.0019
## #Update model
## #Test if any study has p>0.0019 now
## It converged!
## # Final regression...
## The studies used are:
## - Years of Schooling (SSGAC)
## - LDL Cholesterol (GLGC)
## - Body Mass Index (GIANT)
## - Coronary Artery Disease (CARDIoGRAM)
## - Diastolic Blood Pressure (ICBP)
## Estimating adjusted R-squared:
## - in-sample adjusted R-squared for the all-chromosomes multivariate regression is 0.5435
## - out-of-sample R-squared (masking one chromosome at a time), for the multivariate regression will be estimated when calculating the prior.
## <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
## <<< Estimation of the prior >>>
## > Creating the full Z-Matrix
## # Loading the ZMatrix...
## Selecting studies :
## 5 studies
## 6,811,310 SNPs
## # Adding data from the conventional GWAS :
## "lifegen_phase2_bothpl_alldr_2017_09_18.tsv.gz"
## Done!
## 6,513,704 SNPs in common between prior studies and the conventional GWAS
## > Computing prior
## # Calculating the prior chromosome by chromosome...
## Chromosome 1
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## Chromosome 2
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## Chromosome 3
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## Chromosome 4
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## Chromosome 5
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## Chromosome 6
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## Chromosome 7
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## Chromosome 8
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## Chromosome 9
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## Chromosome 10
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## Chromosome 11
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## Chromosome 12
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## Chromosome 13
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## Chromosome 14
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## Chromosome 15
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## Chromosome 16
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## Chromosome 17
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## Chromosome 18
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## Chromosome 19
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## Chromosome 20
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## Chromosome 21
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## Chromosome 22
## Running regression,
## Calculating prior estimates for SNPs on this chromosome,
## Calculating prior standard errors for SNPs on this chromosome,
## ## Out-of-sample R-squared for MR instruments across all chromosomes is 0.5298
## ## Out-of-sample squared correlation for MR instruments across all chromosome is 0.53
## ## Correlation between prior and observed effects for all SNPs is 0.1953
## ## Correlation between prior and observed effects for SNPs with GWAS p-value < 0.001 is 0.6141
## Done!
## <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
## <<< Calculation of Bayes Factors and p-values >>>
## > Calculating them for all SNPs
## # Computing observed Bayes Factor for all SNPs...
## Done!
## # Computing BF p-values...
## using a distribution approach:
## ... getting approximated p-values using non-linear quantiles
## ... checking p-values near significance threshold
## 9 p-values have been re-estimated using the exact formula.
## # Estimating p-values for posterior effects...
## Done!
## # Estimating p-values for direct effects...
## Done!
## > Pruning and identifying significant SNPs
## Identification based on BFs
## Starting with 6,513,704 SNPs
## # Selecting significant SNPs according to p-values...
## 871 SNPs left
## Done!
## # Pruning significant SNPs...
## distance : 500Kb
## 28 SNPs left
## Done!
## Identification based on posterior effects
## Starting with 6,513,704 SNPs
## # Selecting significant SNPs according to p-values...
## 975 SNPs left
## Done!
## # Pruning significant SNPs...
## distance : 500Kb
## 28 SNPs left
## Done!
## Identification based on direct effects
## Starting with 6,513,704 SNPs
## # Selecting significant SNPs according to p-values...
## 166 SNPs left
## Done!
## # Pruning significant SNPs...
## distance : 500Kb
## 4 SNPs left
## Done!
## <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
## Time of the analysis: 94 minute(s) and 15 second(s).
We can now look at the results more in details.
Risk factors (Prior GWASs) used
coefficients_plot_bGWAS(Lifespan_bGWAS)
5 risk factors are used to create the prior, the multivariate causal effect estimates are consistent with what we would expect. On this figure, the multivariate causal effect estimate and the 95% interval from the multivariate MR model using all chromosomes (black dot and bars) as well as the 22 per-chromosome estimates (grey bars) are represented for each prior GWASs. Coronary Artery Disease (CAD) has the strongest negative effect on lifespan. High Diastolic Blood Pressure (DBP) and Body Mass Index (BMI) also decreases lifespan. We can also see that education, in this case the number of years of schooling, has a positive effect on lifespan.
Overall, the squared correlation between prior and observed effects is about 0.038 and goes up to 0.377 when we consider only SNPs having at least a moderate effect on lifespan (observed p-value \< 0.001). Using the previous version (Timmers et al), squared correlation was around 0.003 when considering all SNPs and around 0.082 for SNPs having a moderate effect.
Results - BF
All significant hits
With this approach, we identified 28 SNPs affecting lifespan through the selected risk factors:
# all hits
extract_results_bGWAS(Lifespan_bGWAS) %>%
mutate(BF = as.character(format(BF, scientific=T, digits=3)), BF_p = as.character(format(BF_p, scientific=T, digits=3))) %>%
arrange(chrm_UK10K, pos_UK10K) -> Hits
knitr::kable(Hits, digits=3)
| rsid | chrm_UK10K | pos_UK10K | alt | ref | beta | se | z_obs | mu_prior_estimate | mu_prior_std_error | beta_prior_estimate | beta_prior_std_error | BF | BF_p | | :--------- | ----------: | ---------: | :-- | :-- | ------: | ----: | ------: | ------------------: | --------------------: | --------------------: | ----------------------: | :------- | :------- | | rs646776 | 1 | 109818530 | T | C | -0.023 | 0.005 | -4.908 | -4.822 | 0.748 | -0.022 | 0.003 | 1.36e+05 | 1.21e-11 | | rs1230666 | 1 | 114173410 | A | G | -0.032 | 0.006 | -5.805 | -1.433 | 0.527 | -0.008 | 0.003 | 1.03e+04 | 6.99e-10 | | rs7536152 | 1 | 154423909 | A | G | -0.018 | 0.004 | -4.627 | -1.636 | 0.544 | -0.006 | 0.002 | 1.24e+03 | 2.86e-08 | | rs6719980 | 2 | 651507 | T | C | -0.028 | 0.005 | -5.407 | -1.906 | 0.581 | -0.010 | 0.003 | 1.97e+04 | 2.45e-10 | | rs1275922 | 2 | 26932887 | A | G | -0.026 | 0.004 | -5.817 | -0.850 | 0.515 | -0.004 | 0.002 | 1.15e+03 | 3.31e-08 | | rs7599488 | 2 | 60718347 | T | C | -0.018 | 0.004 | -4.663 | -1.956 | 0.521 | -0.008 | 0.002 | 2.62e+03 | 7.32e-09 | | rs13082711 | 3 | 27537909 | T | C | 0.020 | 0.005 | 4.428 | 1.954 | 0.616 | 0.009 | 0.003 | 1.67e+03 | 1.65e-08 | | rs2271961 | 3 | 49878113 | T | C | 0.015 | 0.004 | 3.824 | 3.085 | 0.572 | 0.012 | 0.002 | 1.06e+03 | 3.87e-08 | | rs61348208 | 4 | 3089564 | T | C | 0.023 | 0.004 | 5.823 | 1.479 | 0.502 | 0.006 | 0.002 | 1.10e+04 | 6.32e-10 | | rs9393691 | 6 | 26272829 | T | C | -0.022 | 0.004 | -5.570 | -1.565 | 0.505 | -0.006 | 0.002 | 8.18e+03 | 1.03e-09 | | rs4580876 | 6 | 98322872 | A | G | 0.016 | 0.004 | 4.112 | 2.915 | 0.526 | 0.011 | 0.002 | 2.37e+03 | 8.76e-09 | | rs10455872 | 6 | 161010118 | A | G | 0.076 | 0.007 | 10.282 | 2.150 | 0.606 | 0.016 | 0.005 | 2.42e+12 | 2.85e-22 | | rs11556924 | 7 | 129663496 | T | C | 0.020 | 0.004 | 5.062 | 3.276 | 0.648 | 0.013 | 0.003 | 1.00e+05 | 1.93e-11 | | rs10104032 | 8 | 9616664 | A | C | -0.017 | 0.004 | -4.232 | -2.169 | 0.533 | -0.009 | 0.002 | 1.31e+03 | 2.61e-08 | | rs11986845 | 8 | 10691318 | T | C | -0.017 | 0.004 | -4.237 | -2.428 | 0.544 | -0.010 | 0.002 | 1.96e+03 | 1.23e-08 | | rs59234174 | 9 | 16730258 | T | C | -0.028 | 0.005 | -5.127 | -1.492 | 0.508 | -0.008 | 0.003 | 2.38e+03 | 8.70e-09 | | rs1333045 | 9 | 22119195 | T | C | 0.024 | 0.004 | 6.256 | 2.981 | 0.844 | 0.012 | 0.003 | 1.05e+07 | 1.62e-14 | | rs2519093 | 9 | 136141870 | T | C | -0.022 | 0.005 | -4.517 | -2.275 | 0.596 | -0.011 | 0.003 | 3.62e+03 | 4.13e-09 | | rs10841520 | 12 | 20586395 | T | C | 0.024 | 0.005 | 4.944 | 1.334 | 0.522 | 0.006 | 0.003 | 1.08e+03 | 3.75e-08 | | rs10849925 | 12 | 111495518 | A | G | 0.021 | 0.004 | 5.193 | 1.881 | 0.605 | 0.008 | 0.002 | 1.11e+04 | 6.24e-10 | | rs11065979 | 12 | 112059557 | T | C | -0.028 | 0.004 | -7.128 | -2.227 | 0.719 | -0.009 | 0.003 | 3.19e+07 | 3.06e-15 | | rs17630235 | 12 | 112591686 | A | G | -0.026 | 0.004 | -6.503 | -1.884 | 0.713 | -0.007 | 0.003 | 1.06e+06 | 5.27e-13 | | rs8042849 | 15 | 78817929 | T | C | 0.044 | 0.004 | 10.659 | 0.265 | 0.499 | 0.001 | 0.002 | 6.99e+05 | 9.90e-13 | | rs8039305 | 15 | 91422543 | T | C | 0.025 | 0.004 | 6.414 | 1.473 | 0.550 | 0.006 | 0.002 | 6.40e+04 | 3.86e-11 | | rs12924886 | 16 | 72075593 | A | T | 0.028 | 0.005 | 5.679 | 2.455 | 0.522 | 0.012 | 0.003 | 1.51e+05 | 1.03e-11 | | rs6511720 | 19 | 11202306 | T | G | 0.034 | 0.006 | 5.631 | 3.787 | 0.754 | 0.023 | 0.005 | 2.07e+06 | 1.89e-13 | | rs12459965 | 19 | 18452195 | T | C | 0.020 | 0.004 | 4.426 | 2.781 | 0.533 | 0.012 | 0.002 | 5.51e+03 | 2.00e-09 | | rs429358 | 19 | 45411941 | T | C | 0.106 | 0.005 | 19.328 | 1.854 | 0.712 | 0.010 | 0.004 | 1.11e+37 | 7.06e-70 |
New hits
# new hits (compared to conventional GWAS)
# look at SNPs in a 100kb window around (to assess significance in conventional GWAS)
Hits$NewHits = NA
dist=100000
for(snp in 1:nrow(Hits)){
Hits %>% dplyr::slice(snp) %>% pull(chrm_UK10K) -> chr
Hits %>% dplyr::slice(snp) %>% pull(pos_UK10K) -> pos
extract_results_bGWAS(Lifespan_bGWAS, SNPs = "all") %>%
filter(chrm_UK10K == chr,
pos_UK10K > pos - dist,
pos_UK10K < pos + dist) %>%
mutate(p_obs = 2*pnorm(-abs(z_obs))) %>%
pull(p_obs) %>% min() -> minP_region
Hits$NewHits[snp] = ifelse(minP_region<5e-8,FALSE,TRUE)
}
Hits %>%
filter(NewHits == TRUE) %>%
mutate(NewHits = NULL)-> New_Hits
# also add gene names using annovar
suppressWarnings(suppressMessages(source(system.file("Scripts/Get_GenesAndTraits.R", package="bGWAS"))))
Gene_Info <- do.call(rbind.data.frame,
apply(New_Hits, 1, function(x) get_geneInfo(as.numeric(x[2]), as.numeric(x[3]), x[4], x[5])))
# clean some names for intergenic regions
knitr::kable(Gene_Info)
| Function | Gene | Distance | | :--------- | :------------------- | :------------ | | downstream | CELSR2 | 157 | | intronic | IL6R | 0 | | intergenic | LOC105373352/TMEM18 | 89415/12371 | | intronic | BCL11A | 0 | | intergenic | SLC4A7/EOMES | 12034/219531 | | intronic | TRAIP | 0 | | intergenic | LOC101927314/MIR2113 | 166079/149535 | | exonic | ZC3HC1 | 0 | | intronic | TNKS | 0 | | intronic | PINX1 | 0 | | intronic | BNC2 | 0 | | intronic | ABO | 0 | | intronic | PDE3A | 0 | | intronic | CUX2 | 0 | | intronic | PGPEP1 | 0 |
# intergenic LOC105373352/TMEM18 89415/12371 -> TMEM18
Gene_Info[3,2:3] = c("TMEM18", "12371")
# intergenic SLC4A7/EOMES 12034/219531
Gene_Info[5,2:3] = c("SLC4A7", "12034")
# intergenic LOC101927314/MIR2113 166079/149535 -> MIR2113
Gene_Info[7,2:3] = c("MIR2113", "149535")
Gene_Info %>%
bind_cols(New_Hits) -> New_Hits
knitr::kable(New_Hits, digits=3)
| Function | Gene | Distance | rsid | chrm_UK10K | pos_UK10K | alt | ref | beta | se | z_obs | mu_prior_estimate | mu_prior_std_error | beta_prior_estimate | beta_prior_std_error | BF | BF_p | | :--------- | :------ | :------- | :--------- | ----------: | ---------: | :-- | :-- | ------: | ----: | ------: | ------------------: | --------------------: | --------------------: | ----------------------: | :------- | :------- | | downstream | CELSR2 | 157 | rs646776 | 1 | 109818530 | T | C | -0.023 | 0.005 | -4.908 | -4.822 | 0.748 | -0.022 | 0.003 | 1.36e+05 | 1.21e-11 | | intronic | IL6R | 0 | rs7536152 | 1 | 154423909 | A | G | -0.018 | 0.004 | -4.627 | -1.636 | 0.544 | -0.006 | 0.002 | 1.24e+03 | 2.86e-08 | | intergenic | TMEM18 | 12371 | rs6719980 | 2 | 651507 | T | C | -0.028 | 0.005 | -5.407 | -1.906 | 0.581 | -0.010 | 0.003 | 1.97e+04 | 2.45e-10 | | intronic | BCL11A | 0 | rs7599488 | 2 | 60718347 | T | C | -0.018 | 0.004 | -4.663 | -1.956 | 0.521 | -0.008 | 0.002 | 2.62e+03 | 7.32e-09 | | intergenic | SLC4A7 | 12034 | rs13082711 | 3 | 27537909 | T | C | 0.020 | 0.005 | 4.428 | 1.954 | 0.616 | 0.009 | 0.003 | 1.67e+03 | 1.65e-08 | | intronic | TRAIP | 0 | rs2271961 | 3 | 49878113 | T | C | 0.015 | 0.004 | 3.824 | 3.085 | 0.572 | 0.012 | 0.002 | 1.06e+03 | 3.87e-08 | | intergenic | MIR2113 | 149535 | rs4580876 | 6 | 98322872 | A | G | 0.016 | 0.004 | 4.112 | 2.915 | 0.526 | 0.011 | 0.002 | 2.37e+03 | 8.76e-09 | | exonic | ZC3HC1 | 0 | rs11556924 | 7 | 129663496 | T | C | 0.020 | 0.004 | 5.062 | 3.276 | 0.648 | 0.013 | 0.003 | 1.00e+05 | 1.93e-11 | | intronic | TNKS | 0 | rs10104032 | 8 | 9616664 | A | C | -0.017 | 0.004 | -4.232 | -2.169 | 0.533 | -0.009 | 0.002 | 1.31e+03 | 2.61e-08 | | intronic | PINX1 | 0 | rs11986845 | 8 | 10691318 | T | C | -0.017 | 0.004 | -4.237 | -2.428 | 0.544 | -0.010 | 0.002 | 1.96e+03 | 1.23e-08 | | intronic | BNC2 | 0 | rs59234174 | 9 | 16730258 | T | C | -0.028 | 0.005 | -5.127 | -1.492 | 0.508 | -0.008 | 0.003 | 2.38e+03 | 8.70e-09 | | intronic | ABO | 0 | rs2519093 | 9 | 136141870 | T | C | -0.022 | 0.005 | -4.517 | -2.275 | 0.596 | -0.011 | 0.003 | 3.62e+03 | 4.13e-09 | | intronic | PDE3A | 0 | rs10841520 | 12 | 20586395 | T | C | 0.024 | 0.005 | 4.944 | 1.334 | 0.522 | 0.006 | 0.003 | 1.08e+03 | 3.75e-08 | | intronic | CUX2 | 0 | rs10849925 | 12 | 111495518 | A | G | 0.021 | 0.004 | 5.193 | 1.881 | 0.605 | 0.008 | 0.002 | 1.11e+04 | 6.24e-10 | | intronic | PGPEP1 | 0 | rs12459965 | 19 | 18452195 | T | C | 0.020 | 0.004 | 4.426 | 2.781 | 0.533 | 0.012 | 0.002 | 5.51e+03 | 2.00e-09 |
15 of the 28 genome-wide significant loci are missed by the conventional GWAS (using same p-value threshold of 5e-8 to assess significance). Using the previous version (Timmers et al), we identified 7 new loci (using a threshold of 2.5e-8 for both GWAS and bGWAS results), 4 of them are also significant in this analysis (near CELSR2, TMEM18, ZC3HC1 and ABO). The 11 other variants identified in this analysis (near IL6R, BCL11A, SLC4A7, TRAIP, MIR2113, PINX1, TNKS, BNC2, CUX2, PDE3A and PGPEP1) are reported to be associated with lifespan for the first time.
# For the plots, we will use only the new hits
New_Hits %>%
transmute(rs=rsid,
gene = Gene,
color="#932735") -> my_SNPs
manhattan_plot_bGWAS(Lifespan_bGWAS, SNPs=my_SNPs)
New hits, association with risk factors
my_SNPs %>%
mutate(color=NULL) -> my_SNPs
heatmap_bGWAS(Lifespan_bGWAS, SNPs = my_SNPs)
On this figure, the contribution of each risk factor to the prior effects of new hits (alleles aligned to be life-lengthening) is represented as a heatmap. Overall, we observe a lot of red, as expected since alleles are aligned to be life-lengthening. Among these 15 new variants, 8 were known to be associated with at least one of the RFs (indicated with a star on the heatmap - variant near CELSR2 associated with LDL cholesterol, variants near TMEM18 and PGPEP1 associated with Body Mass Index, variants near BCL11A, TRAIP and MIR2113 associated with Years of Schooling, variant near ZC3HC1 associated with Coronary Artery Disease and variant near ABO associated with both LDL cholesterol and Coronary Artery Disease). 7 variants (near IL6R, SLC4A7, PINX1, TNKS, BNC2, CUX2 and PDE3A) are not associated with any of the RFs (at least not in the summary statistics used to create the prior), suggesting that they could be acting on lifespan through smaller pleiotropic effects on several RFs. These variants (and the ones in a 100kb window) can be further investigated using the GWAS Catalog. R2 estimates in EUR population from LDlink are used to keep only SNPs in LD (R2>0.1) with the variant identified. SNP-trait associations p-values below 5e-8 are reported below (when LD-friends are used, p-values adjusted for the correlation between the variants).
Reported Associations##
## * SNP - rs7536152 (IL6R):
##
##
## snp chrm posh19 LD_R2 LD_alleles trait p adjusted_p effect gene url
## -------------- ----- ---------- ------ ---------------------------- --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ------ ----------- ---------------------------- ----- -------------------------------------
## rs6689306-A 1 154395946 0.902 rs6689306(G)/rs7536152(G) Atrial fibrillation 1e-18 4.76e-17 [1.05-1.08] IL6R www.ncbi.nlm.nih.gov/pubmed/30061737
## rs146402667-G 1 154398553 0.616 rs34280647(AC)/rs7536152(G) Blood protein levels 0e+00 0.00e+00 [0.91-0.99] unit increase IL6R www.ncbi.nlm.nih.gov/pubmed/29875488
## rs4129267-C 1 154426264 0.462 rs4129267(T)/rs7536152(G) C-reactive protein levels 2e-48 2.72e-23 [0.07-0.09] unit increase IL6R www.ncbi.nlm.nih.gov/pubmed/21300955
## rs2228145-C 1 154426970 0.462 rs2228145(C)/rs7536152(G) Cerebrospinal fluid biomarker levels 7e-29 3.39e-14 IL6R www.ncbi.nlm.nih.gov/pubmed/28031287
## rs61812598-G 1 154420087 0.464 rs61812598(G)/rs7536152(A) Cerebrospinal fluid levels of Alzheimer's disease-related proteins 6e-63 3.68e-30 IL6R www.ncbi.nlm.nih.gov/pubmed/25340798
## rs4129267-? 1 154426264 0.462 rs4129267(T)/rs7536152(G) Chronic inflammatory diseases (ankylosing spondylitis, Crohn's disease, psoriasis, primary sclerosing cholangitis, ulcerative colitis) (pleiotropy) 9e-18 5.29e-09 IL6R www.ncbi.nlm.nih.gov/pubmed/26974007
## rs4845625-T 1 154422067 1.000 rs4845625(T)/rs7536152(A) Coronary artery disease 6e-16 6.00e-16 [0.034-0.056] unit increase IL6R www.ncbi.nlm.nih.gov/pubmed/29212778
## rs6689306-A 1 154395946 0.902 rs6689306(G)/rs7536152(G) Coronary artery disease (myocardial infarction, percutaneous transluminal coronary angioplasty, coronary artery bypass grafting, angina or chromic ischemic heart disease) 2e-09 1.22e-08 [1.03-1.07] IL6R www.ncbi.nlm.nih.gov/pubmed/28714975
## rs4129267-T 1 154426264 0.462 rs4129267(T)/rs7536152(G) Fibrinogen 6e-27 2.70e-13 [0.009-0.013] unit decrease IL6R www.ncbi.nlm.nih.gov/pubmed/23969696
## rs61812598-A 1 154420087 0.464 rs61812598(G)/rs7536152(A) Fibrinogen levels 3e-36 1.07e-17 NR unit decrease IL6R www.ncbi.nlm.nih.gov/pubmed/26561523
## rs4129267-? 1 154426264 0.462 rs4129267(T)/rs7536152(G) Protein quantitative trait loci 2e-57 1.79e-27 IL6R www.ncbi.nlm.nih.gov/pubmed/18464913
##
##
## * SNP - rs13082711 (SLC4A7):
##
##
## snp chrm posh19 LD_R2 LD_alleles trait p adjusted_p effect gene url
## ------------- ----- --------- ------ --------------------------- --------------------------------------------------------------------- ------ ----------- -------------------------- ---------------------- -------------------------------------
## rs13082711-T 3 27537909 NA NA Blood pressure 5e-09 NA [0.22-0.45] mmHg decrease AC099535.1 - RNU1-96P www.ncbi.nlm.nih.gov/pubmed/21909110
## rs13082711-C 3 27537909 NA NA Diastolic blood pressure 3e-10 NA [0.23-0.43] unit increase AC099535.1 - RNU1-96P www.ncbi.nlm.nih.gov/pubmed/28739976
## rs13082711-? 3 27537909 NA NA Diastolic blood pressure (cigarette smoking interaction) 7e-12 NA AC099535.1 - RNU1-96P www.ncbi.nlm.nih.gov/pubmed/29455858
## rs13082711-? 3 27537909 NA NA Systolic blood pressure (cigarette smoking interaction) 3e-11 NA AC099535.1 - RNU1-96P www.ncbi.nlm.nih.gov/pubmed/29455858
## rs2643826-T 3 27562988 0.309 rs2643826(T)/rs13082711(T) Systolic blood pressure x alcohol consumption interaction (2df test) 6e-32 6.14e-11 RNU1-96P - AC137675.1 www.ncbi.nlm.nih.gov/pubmed/29912962
##
##
## * SNP - rs10104032 (TNKS):
##
##
## snp chrm posh19 LD_R2 LD_alleles trait p adjusted_p effect gene url
## ------------ ----- -------- ------ --------------------------- ----------------------------------------------------------------------------------------- ------ ----------- ---------------------------- ----------------- -------------------------------------
## rs9286060-A 8 9653145 0.702 rs9286060(C)/rs10104032(C) Diastolic blood pressure x alcohol consumption interaction (2df test) 2e-13 7.46e-10 TNKS - LINC00599 www.ncbi.nlm.nih.gov/pubmed/29912962
## rs4383974-? 8 9619348 0.704 rs4383974(G)/rs10104032(A) Heel bone mineral density 2e-17 1.02e-12 [0.014-0.023] unit decrease TNKS www.ncbi.nlm.nih.gov/pubmed/30048462
## rs1976671-A 8 9679634 0.642 rs1976671(G)/rs10104032(C) Systolic blood pressure x alcohol consumption interaction (2df test) 7e-18 5.14e-12 TNKS - LINC00599 www.ncbi.nlm.nih.gov/pubmed/29912962
## rs4841235-? 8 9683358 0.538 rs4841235(G)/rs10104032(C) Systolic blood pressure x smoking status (current vs non-current) interaction (2df test) 5e-15 9.54e-09 TNKS - LINC00599 www.ncbi.nlm.nih.gov/pubmed/29455858
## rs4841235-? 8 9683358 0.538 rs4841235(G)/rs10104032(C) Systolic blood pressure x smoking status (ever vs never) interaction (2df test) 4e-14 2.96e-08 TNKS - LINC00599 www.ncbi.nlm.nih.gov/pubmed/29455858
##
##
## * SNP - rs11986845 (PINX1):
##
##
## snp chrm posh19 LD_R2 LD_alleles trait p adjusted_p effect gene url
## ------------ ----- --------- ------ --------------------------- ---------------------------------------------------------------------- ------ ----------- -------------------------- ------------- -------------------------------------
## rs4551304-A 8 10665069 0.723 rs4551304(G)/rs11986845(T) Diastolic blood pressure x alcohol consumption interaction (2df test) 2e-14 7.82e-11 PINX1, PINX1 www.ncbi.nlm.nih.gov/pubmed/29912962
## rs1821002-G 8 10640065 0.660 rs1821002(G)/rs11986845(T) Systolic blood pressure 4e-19 3.83e-13 [0.33-0.52] unit decrease PINX1, PINX1 www.ncbi.nlm.nih.gov/pubmed/28135244
## rs7814757-T 8 10675188 0.716 rs7814757(T)/rs11986845(T) Systolic blood pressure x alcohol consumption interaction (2df test) 3e-22 2.28e-16 PINX1, PINX1 www.ncbi.nlm.nih.gov/pubmed/29912962
##
##
## * SNP - rs59234174 (BNC2):
##
## No association reported
##
## * SNP - rs10841520 (PDE3A):
##
## No association reported
##
## * SNP - rs10849925 (CUX2):
##
## No association reported
Interestingly, we can see that a few loci identified have been associated with some of the risk factors used to create the prior in more recent studies. Variants in LD with the variant identified near IL6R have been associated with Coronary Artery Disease. Variants in LD with the variants identified near SLC4A7, PINX1 and TNKS have been associated with Diastolic Blood Pressure. The other loci have not been associated with any of the risk factors, and are likely acting on lifespan through moderate effects on several risk factors (pleiotropic effects).
Comparison with Timmers et al results
# Download data to working directory (~230 MB) if not already here
if(!file.exists("bGWAS_Timmers2019/bGWAS_Timmers2019.csv.gz")){
download.file(url = "https://drive.switch.ch/index.php/s/zlNLUSCUyfgovcp/download", destfile = "bGWAS_Timmers2019.tar.gz")
system("tar -xzvf bGWAS_Timmers2019.tar.gz") }
Data_Timmers = data.table::fread("bGWAS_Timmers2019/bGWAS_Timmers2019.csv.gz")
All_Results = extract_results_bGWAS(Lifespan_bGWAS, SNPs = "all")
# combine results
All_Results %>%
inner_join(Data_Timmers, by="rsid", suffix=c("", "_Timmers")) -> All_Results
# keep SNPs significant in one of the two analyses
# (note that in Timmers et al paper a different threshold was used because of multiple analyses)
All_Results %>%
filter(BF_p < 5e-8 | pvalue < 2.5e-8) -> Combined_Results
# identify lead SNP in each region (distance pruning)
prune_byDistance = getFromNamespace("prune_byDistance", "bGWAS")
Combined_Results %>%
transmute(SNP = rsid,
chr_name = chrm_UK10K,
chr_start = pos_UK10K,
pval.exposure = pmin(pvalue, BF_p)) -> ToPrune
SNPsToKeep = prune_byDistance(ToPrune, prune.dist=500, byP=T)
Combined_Results %>%
filter(rsid %in% SNPsToKeep) %>%
transmute(rsid,
chr= chrm_UK10K,
pos = pos_UK10K,
obs = z_obs,
# align alleles to be able to compare directions
prior_new = mu_prior_estimate,
prior_Timmers = case_when(
a1 == alt ~ prior_estimate,
TRUE ~ -prior_estimate),
BF_new = BF,
BF_Timmers,
p_new = BF_p,
p_Timmers = pvalue,
Significance = case_when(
p_new > 5e-8 ~ "Timmers et al only",
p_Timmers > 2.5e-8 ~ "new signal",
TRUE ~ "significant in both analyses"
)) -> Combined_Results
nrow(Combined_Results %>% filter(p_new<5e-8))
## [1] 23
# here we do have less new hits
Hits %>% filter(!rsid %in% Combined_Results$rsid) %>% pull(rsid) -> Missing_SNPs
Missing_SNPs %in% Data_Timmers$rsid
## [1] FALSE FALSE FALSE FALSE FALSE
# this is because we are only looking at variants present in both analyses
# these variants where not included in Timmers et al (i.e no prior effect could
# be estimated) because when creating the Z-Matrices, variants with low imputation
# quality for any RF were excluded instead of set to 0 as they are now
knitr::kable(Combined_Results, digits=3)
| rsid | chr | pos | obs | prior_new | prior_Timmers | BF_new | BF_Timmers | p_new | p_Timmers | Significance | | :---------- | --: | --------: | ------: | ---------: | -------------: | -----------: | :------------------- | -----: | ---------: | :--------------------------- | | rs429358 | 19 | 45411941 | 19.328 | 1.854 | 4.026 | 1.110463e+37 | 29171248738146925e45 | 0 | 0 | significant in both analyses | | rs10455872 | 6 | 161010118 | 10.282 | 2.150 | 2.534 | 2.424978e+12 | 492671224373715e3 | 0 | 0 | significant in both analyses | | rs1333045 | 9 | 22119195 | 6.256 | 2.981 | 2.743 | 1.050057e+07 | 20029321.026961345 | 0 | 0 | significant in both analyses | | rs6511720 | 19 | 11202306 | 5.631 | 3.787 | 3.804 | 2.071058e+06 | 2507806.256755927 | 0 | 0 | significant in both analyses | | rs8042849 | 15 | 78817929 | 10.659 | 0.265 | 1.175 | 6.987773e+05 | 148475882457531e5 | 0 | 0 | significant in both analyses | | rs12924886 | 16 | 72075593 | 5.679 | 2.455 | 1.477 | 1.507003e+05 | 204801.97853190443 | 0 | 0 | significant in both analyses | | rs646776 | 1 | 109818530 | -4.908 | -4.822 | -6.902 | 1.357283e+05 | 49399.20478889484 | 0 | 0 | significant in both analyses | | rs11556924 | 7 | 129663496 | 5.062 | 3.276 | 1.446 | 1.001113e+05 | 17649.28405521511 | 0 | 0 | significant in both analyses | | rs8039305 | 15 | 91422543 | 6.414 | 1.473 | -0.194 | 6.395981e+04 | 50550.771535476524 | 0 | 0 | significant in both analyses | | rs6719980 | 2 | 651507 | -5.407 | -1.906 | -2.107 | 1.973024e+04 | 173370.50556369557 | 0 | 0 | significant in both analyses | | rs61348208 | 4 | 3089564 | 5.823 | 1.479 | 0.972 | 1.099280e+04 | 142313.10294079248 | 0 | 0 | significant in both analyses | | rs1230666 | 1 | 114173410 | -5.805 | -1.433 | 0.000 | 1.033311e+04 | 18404.921566568224 | 0 | 0 | significant in both analyses | | rs12459965 | 19 | 18452195 | 4.426 | 2.781 | 0.857 | 5.514180e+03 | 920.439727029316 | 0 | 0 | new signal | | rs2519093 | 9 | 136141870 | -4.517 | -2.275 | -3.020 | 3.620905e+03 | 10857.171015204418 | 0 | 0 | significant in both analyses | | rs7599488 | 2 | 60718347 | -4.663 | -1.956 | -1.290 | 2.618682e+03 | 3659.2921681571866 | 0 | 0 | new signal | | rs59234174 | 9 | 16730258 | -5.127 | -1.492 | 0.000 | 2.378208e+03 | 1940.52607021053 | 0 | 0 | new signal | | rs4580876 | 6 | 98322872 | 4.112 | 2.915 | 1.792 | 2.368247e+03 | 1017.410230589991 | 0 | 0 | new signal | | rs11986845 | 8 | 10691318 | -4.237 | -2.428 | 0.000 | 1.964372e+03 | 163.1002364768018 | 0 | 0 | new signal | | rs10104032 | 8 | 9616664 | -4.232 | -2.169 | -0.124 | 1.305137e+03 | 196.65685527856158 | 0 | 0 | new signal | | rs7536152 | 1 | 154423909 | -4.627 | -1.636 | 0.000 | 1.240998e+03 | 431.52332466696873 | 0 | 0 | new signal | | rs1275922 | 2 | 26932887 | -5.817 | -0.850 | -0.816 | 1.148873e+03 | 111238.35721435159 | 0 | 0 | significant in both analyses | | rs10841520 | 12 | 20586395 | 4.944 | 1.334 | 0.000 | 1.075240e+03 | 1081.8164977967003 | 0 | 0 | new signal | | rs2271961 | 3 | 49878113 | 3.824 | 3.085 | 1.757 | 1.058031e+03 | 406.50704370647395 | 0 | 0 | new signal | | rs113160991 | 7 | 75094329 | -5.137 | -0.963 | -1.276 | 4.765290e+02 | 19355.298796056322 | 0 | 0 | Timmers et al only | | rs10211471 | 2 | 237081854 | -4.871 | -0.862 | -1.433 | 2.151440e+02 | 9263.378759875068 | 0 | 0 | Timmers et al only | | rs111333005 | 6 | 160487196 | -5.496 | -0.183 | -0.614 | 3.618300e+01 | 20202.541832372604 | 0 | 0 | Timmers et al only |
There are 3 variants from Timmers et al that are not significant in the new analysis. Details about these variants and their association with the RFs used to create the prior in the Timmers et al analysis can be found here. The first (rs113160991, near POM121C) and the second (rs113160991, near GBX2/ASB18) variants were not significantly associated with any of the risk factors used to create the prior in Timmers et al and they were likely acting through small effects on various RFs. In our new analysis, a different set of RFs is used, leading to a much smaller prior effect. The third variant (rs111333005, LPA/IGF2R) is known to be significantly associated with LDL, a RF that was selected to create the prior in both analyses. However, the multivariate causal effect estimate of HDL on lifespan is slighlty smaller in our new analysis (-0.09 vs -0.13), reducing the prior effect. Additionnaly, this variant was also having small effects on some additional RFs included in Timmers et al analyses, explaining why the prior effect estimated in our new analysis is smaller.
Prior Effects Estimates
# abs(prior effects):
p=ggplot2::ggplot(Combined_Results, ggplot2::aes(x=prior_Timmers, y=prior_new, shape=Significance)) +
geom_abline(slope=1, intercept=0, col="darkgrey", lty=2) +
geom_hline(yintercept = 0, size=0.4) +
geom_vline(xintercept = 0, size=0.4) +
ggplot2::geom_point(size=2, alpha=0.6) +
labs(title = "Prior Effects Estimates") +
ylab("bGWAS v.1.0.2") + xlab("Timmers et al") +
apatheme
p
The prior effects are quite consistent (all except 1 agree in sign).
This variant (rs8039305) is still identified as significantly associated
with lifespan by both analyses because of its strong observed effect
(z-score of 6.413708).
The differences that are observed likely come from the fact that
different RFs are selected (update of the stepwise selection approach).
It is also important to note that in the new analysis no shrinkage is
applied before the prior estimation. This also creates some differences
and this is why some of the prior effects from Timmers
Bayes Factors
# abs(prior effects):
p=ggplot2::ggplot(Combined_Results, ggplot2::aes(x=log(as.numeric(BF_Timmers)), y=log(as.numeric(BF_new)), shape=Significance)) +
geom_abline(slope=1, intercept=0, col="darkgrey", lty=2) +
ggplot2::geom_point(size=2, alpha=0.6) +
labs(title = "Bayes Factors (log scale)") +
ylab("bGWAS v.1.0.2") + xlab("Timmers et al") +
apatheme
p
The BFs are also quite consistent. Differences in BFs are directly correlated to the differences in prior effects, since the observed effects are the same for both analyses.
P-values
# abs(prior effects):
p=ggplot2::ggplot(Combined_Results, ggplot2::aes(x=-log10(p_Timmers), y=-log10(p_new), shape=Significance)) +
geom_abline(slope=1, intercept=0, col="darkgrey", lty=2) +
ggplot2::geom_point(size=2, alpha=0.6) +
labs(title = "P-values (-log10 scale)") +
ylab("bGWAS v.1.0.2") + xlab("Timmers et al") +
apatheme
p
The
p-values are also quite similar. Differences in p-values can be
explained by the differences in BFs (because each BF value might differ,
but also because the distribution of BFs is different in each analysis,
meaning that for a specific BF value, the respective p-value will
differ). In addition, this is important to note that since the p-values
from Timmers et al were estimated using a permutation approach, the
minimal p-value that could be estimated (1.4e-10) was dependent on the
number of permutations performed.
Results - Posterior Effects
All significant hits
With this approach, we identified 28 SNPs having significant posterior effects:
# all posterior hits
extract_results_bGWAS(Lifespan_bGWAS, results="posterior") %>%
mutate(p_posterior = as.character(format(p_posterior, scientific=T, digits=3))) %>%
arrange(chrm_UK10K, pos_UK10K) -> Posterior_Hits
knitr::kable(Posterior_Hits, digits=3)
| rsid | chrm_UK10K | pos_UK10K | alt | ref | beta | se | z_obs | mu_posterior_estimate | mu_posterior_std_error | beta_posterior_estimate | beta_posterior_std_error | z_posterior | p_posterior | | :---------- | ----------: | ---------: | :-- | :-- | ------: | ----: | -------: | ----------------------: | ------------------------: | ------------------------: | --------------------------: | -----------: | :----------- | | rs629301 | 1 | 109818306 | T | G | -0.022 | 0.005 | -4.699 | -4.824 | 0.589 | -0.022 | 0.003 | -8.196 | 2.48e-16 | | rs13386964 | 2 | 650828 | A | G | 0.027 | 0.005 | 5.314 | 2.837 | 0.504 | 0.015 | 0.003 | 5.626 | 1.84e-08 | | rs7599488 | 2 | 60718347 | T | C | -0.018 | 0.004 | -4.663 | -2.534 | 0.462 | -0.010 | 0.002 | -5.484 | 4.15e-08 | | rs13086611 | 3 | 49385417 | A | T | -0.013 | 0.004 | -3.019 | -3.043 | 0.503 | -0.013 | 0.002 | -6.049 | 1.46e-09 | | rs2681781 | 3 | 49898273 | A | G | 0.013 | 0.004 | 3.316 | 3.577 | 0.511 | 0.014 | 0.002 | 6.999 | 2.58e-12 | | rs3135169 | 4 | 3262374 | T | C | 0.017 | 0.004 | 4.398 | 2.512 | 0.451 | 0.010 | 0.002 | 5.563 | 2.65e-08 | | rs13130484 | 4 | 45175691 | T | C | -0.012 | 0.004 | -3.161 | -3.270 | 0.487 | -0.013 | 0.002 | -6.716 | 1.86e-11 | | rs3004179 | 6 | 98513032 | A | G | 0.015 | 0.004 | 3.930 | 3.461 | 0.479 | 0.014 | 0.002 | 7.220 | 5.20e-13 | | rs118039278 | 6 | 160985526 | A | G | -0.076 | 0.007 | -10.275 | -4.283 | 0.512 | -0.032 | 0.004 | -8.366 | 5.97e-17 | | rs34809719 | 7 | 2028968 | T | G | 0.016 | 0.005 | 3.227 | 2.774 | 0.483 | 0.014 | 0.002 | 5.749 | 8.97e-09 | | rs11556924 | 7 | 129663496 | T | C | 0.020 | 0.004 | 5.062 | 3.804 | 0.544 | 0.015 | 0.002 | 6.998 | 2.60e-12 | | rs10104032 | 8 | 9616664 | A | C | -0.017 | 0.004 | -4.232 | -2.626 | 0.470 | -0.011 | 0.002 | -5.583 | 2.37e-08 | | rs2271355 | 8 | 10692158 | T | C | 0.015 | 0.004 | 3.911 | 2.866 | 0.479 | 0.011 | 0.002 | 5.982 | 2.20e-09 | | rs1333045 | 9 | 22119195 | T | C | 0.024 | 0.004 | 6.256 | 4.344 | 0.645 | 0.017 | 0.003 | 6.733 | 1.66e-11 | | rs2519093 | 9 | 136141870 | T | C | -0.022 | 0.005 | -4.517 | -2.862 | 0.512 | -0.014 | 0.003 | -5.593 | 2.23e-08 | | rs964184 | 11 | 116648917 | C | G | 0.021 | 0.006 | 3.791 | 3.165 | 0.497 | 0.018 | 0.003 | 6.364 | 1.96e-10 | | rs11065979 | 12 | 112059557 | T | C | -0.028 | 0.004 | -7.128 | -3.897 | 0.584 | -0.015 | 0.002 | -6.677 | 2.45e-11 | | rs11066309 | 12 | 112883476 | A | G | -0.025 | 0.004 | -6.330 | -3.444 | 0.578 | -0.014 | 0.002 | -5.958 | 2.55e-09 | | rs1183910 | 12 | 121420807 | A | G | -0.015 | 0.004 | -3.676 | -2.915 | 0.481 | -0.012 | 0.002 | -6.064 | 1.33e-09 | | rs1029420 | 15 | 91441086 | T | C | 0.023 | 0.004 | 5.738 | 2.848 | 0.499 | 0.011 | 0.002 | 5.701 | 1.19e-08 | | rs8049439 | 16 | 28837515 | T | C | 0.011 | 0.004 | 2.809 | 2.949 | 0.480 | 0.012 | 0.002 | 6.144 | 8.05e-10 | | rs1421085 | 16 | 53800954 | T | C | 0.014 | 0.004 | 3.550 | 3.760 | 0.612 | 0.015 | 0.002 | 6.147 | 7.89e-10 | | rs12924886 | 16 | 72075593 | A | T | 0.028 | 0.005 | 5.679 | 3.146 | 0.463 | 0.015 | 0.002 | 6.798 | 1.06e-11 | | rs999474 | 17 | 46987665 | A | G | 0.014 | 0.004 | 3.502 | 2.674 | 0.469 | 0.010 | 0.002 | 5.697 | 1.22e-08 | | rs303757 | 18 | 21078716 | T | G | 0.011 | 0.004 | 2.793 | 2.983 | 0.478 | 0.012 | 0.002 | 6.240 | 4.37e-10 | | rs6511720 | 19 | 11202306 | T | G | 0.034 | 0.006 | 5.631 | 4.455 | 0.602 | 0.027 | 0.004 | 7.400 | 1.36e-13 | | rs17724992 | 19 | 18454825 | A | G | -0.019 | 0.004 | -4.402 | -3.170 | 0.471 | -0.014 | 0.002 | -6.725 | 1.76e-11 | | rs429358 | 19 | 45411941 | T | C | 0.106 | 0.005 | 19.328 | 7.736 | 0.580 | 0.042 | 0.003 | 13.334 | 1.47e-40 |
New hits
# new hits (compared to conventional GWAS & BF p-values)
# look at SNPs in a 100kb window around (to assess significance in conventional GWAS / bGWAS results)
Posterior_Hits$NewHits = NA
dist=100000
for(snp in 1:nrow(Posterior_Hits)){
Posterior_Hits %>% dplyr::slice(snp) %>% pull(chrm_UK10K) -> chr
Posterior_Hits %>% dplyr::slice(snp) %>% pull(pos_UK10K) -> pos
extract_results_bGWAS(Lifespan_bGWAS, SNPs = "all") %>%
filter(chrm_UK10K == chr,
pos_UK10K > pos - dist,
pos_UK10K < pos + dist) %>%
mutate(p_obs = 2*pnorm(-abs(z_obs))) %>%
dplyr::select(p_obs, BF_p) %>% min() -> minP_region
Posterior_Hits$NewHits[snp] = ifelse(minP_region<5e-8,FALSE,TRUE)
}
Posterior_Hits %>%
filter(NewHits == TRUE) %>%
mutate(NewHits = NULL)-> NewPosterior_Hits
# also add gene names using annovar
Gene_Info_Posterior <- do.call(rbind.data.frame,
apply(NewPosterior_Hits, 1, function(x) get_geneInfo(as.numeric(x[2]), as.numeric(x[3]), x[4], x[5])))
# clean some names for intergenic regions
knitr::kable(Gene_Info_Posterior)
| Function | Gene | Distance | | :--------- | :------------ | :------------ | | intergenic | USP4/GPX1 | 7931/9187 | | intergenic | GNPDA2/GABRG1 | 447040/862095 | | intronic | MAD1L1 | 0 | | UTR3 | ZPR1 | 0 | | intronic | HNF1A | 0 | | intronic | ATXN2L | 0 | | intronic | FTO | 0 | | intronic | UBE2Z | 0 | | intergenic | RIOK3/RMC1 | 15612/4718 |
# intergenic USP4/GPX1 7931/9187 -> USP4
Gene_Info_Posterior[1,2:3] = c("USP4", "7931")
# intergenic GNPDA2/GABRG1 447040/862095 -> GNPDA2 (super far away though ... )
Gene_Info_Posterior[2,2:3] = c("GNPDA2", "447040")
# intergenic RIOK3/RMC1 15612/4718 -> RMC1
Gene_Info_Posterior[9,2:3] = c("RMC1", "4718")
Gene_Info_Posterior %>%
bind_cols(NewPosterior_Hits) -> NewPosterior_Hits
knitr::kable(NewPosterior_Hits, digits=3)
| Function | Gene | Distance | rsid | chrm_UK10K | pos_UK10K | alt | ref | beta | se | z_obs | mu_posterior_estimate | mu_posterior_std_error | beta_posterior_estimate | beta_posterior_std_error | z_posterior | p_posterior | | :--------- | :----- | :------- | :--------- | ----------: | ---------: | :-- | :-- | ------: | ----: | ------: | ----------------------: | ------------------------: | ------------------------: | --------------------------: | -----------: | :----------- | | intergenic | USP4 | 7931 | rs13086611 | 3 | 49385417 | A | T | -0.013 | 0.004 | -3.019 | -3.043 | 0.503 | -0.013 | 0.002 | -6.049 | 1.46e-09 | | intergenic | GNPDA2 | 447040 | rs13130484 | 4 | 45175691 | T | C | -0.012 | 0.004 | -3.161 | -3.270 | 0.487 | -0.013 | 0.002 | -6.716 | 1.86e-11 | | intronic | MAD1L1 | 0 | rs34809719 | 7 | 2028968 | T | G | 0.016 | 0.005 | 3.227 | 2.774 | 0.483 | 0.014 | 0.002 | 5.749 | 8.97e-09 | | UTR3 | ZPR1 | 0 | rs964184 | 11 | 116648917 | C | G | 0.021 | 0.006 | 3.791 | 3.165 | 0.497 | 0.018 | 0.003 | 6.364 | 1.96e-10 | | intronic | HNF1A | 0 | rs1183910 | 12 | 121420807 | A | G | -0.015 | 0.004 | -3.676 | -2.915 | 0.481 | -0.012 | 0.002 | -6.064 | 1.33e-09 | | intronic | ATXN2L | 0 | rs8049439 | 16 | 28837515 | T | C | 0.011 | 0.004 | 2.809 | 2.949 | 0.480 | 0.012 | 0.002 | 6.144 | 8.05e-10 | | intronic | FTO | 0 | rs1421085 | 16 | 53800954 | T | C | 0.014 | 0.004 | 3.550 | 3.760 | 0.612 | 0.015 | 0.002 | 6.147 | 7.89e-10 | | intronic | UBE2Z | 0 | rs999474 | 17 | 46987665 | A | G | 0.014 | 0.004 | 3.502 | 2.674 | 0.469 | 0.010 | 0.002 | 5.697 | 1.22e-08 | | intergenic | RMC1 | 4718 | rs303757 | 18 | 21078716 | T | G | 0.011 | 0.004 | 2.793 | 2.983 | 0.478 | 0.012 | 0.002 | 6.240 | 4.37e-10 |
9 of the 28 genome-wide significant loci are missed by the conventional GWAS and by the identification based on BFs (using same p-value threshold of 5e-8 to assess significance).
# For the plots, we will use only the new posterior hits
NewPosterior_Hits %>%
transmute(rs=rsid,
gene = Gene,
color="#932735") -> my_SNPsPosterior
manhattan_plot_bGWAS(Lifespan_bGWAS, SNPs=my_SNPsPosterior, results = "posterior")
New hits, association with risk factors
These variants (and the ones in a 100kb window) can be further investigated using the GWAS Catalog. R2 estimates in EUR population from LDlink are used to keep only SNPs in LD (R2>0.1) with the variant identified. SNP-trait associations p-values below 5e-8 are reported below (when LD-friends are used, p-values adjusted for the correlation between the variants).
Reported Associations for posterior Hits##
## * SNP - rs13086611 (USP4):
##
##
## snp chrm posh19 LD_R2 LD_alleles trait p adjusted_p effect gene url
## -------------- ----- --------- ------ ----------------------------- -------------------------------------------- ------ ----------- ----------------------------- ------------------ -------------------------------------
## rs13086611-? 3 49385417 NA NA Educational attainment 2e-14 NA [NR] unit increase USP4 - GPX1 www.ncbi.nlm.nih.gov/pubmed/27046643
## rs7623659-T 3 49414791 0.991 rs7623659(T)/rs13086611(A) Cognitive performance (MTAG) 4e-57 1.24e-56 [0.035-0.044] unit increase RHOA www.ncbi.nlm.nih.gov/pubmed/30038396
## rs148734725-A 3 49406708 0.991 rs148734725(G)/rs13086611(T) Educational attainment (college completion) 5e-12 6.21e-12 [NR] RHOA www.ncbi.nlm.nih.gov/pubmed/27225129
## rs9859556-T 3 49455986 0.991 rs9859556(T)/rs13086611(A) Educational attainment (years of education) 5e-82 2.58e-81 [0.026-0.032] unit increase AC104452.1, AMT www.ncbi.nlm.nih.gov/pubmed/30038396
## rs1987628-A 3 49399259 0.991 rs1987628(G)/rs13086611(T) Extremely high intelligence 4e-08 4.59e-08 NR z score increase increase RHOA www.ncbi.nlm.nih.gov/pubmed/29520040
## rs7646366-A 3 49470668 0.991 rs7646366(G)/rs13086611(T) General cognitive ability 2e-19 2.88e-19 z-score increase NICN1 - RNA5SP130 www.ncbi.nlm.nih.gov/pubmed/29844566
## rs7623659-T 3 49414791 0.991 rs7623659(T)/rs13086611(A) Intelligence 1e-27 1.71e-27 z-score increase RHOA www.ncbi.nlm.nih.gov/pubmed/29942086
##
##
## * SNP - rs13130484 (GNPDA2):
##
##
## snp chrm posh19 LD_R2 LD_alleles trait p adjusted_p effect gene url
## ------------- ----- --------- ------ ---------------------------- ---------------------------------------------------------------------- ------ ----------- ------------------------------ ---------------------- -------------------------------------
## rs13130484-? 4 45175691 NA NA Body mass index 2e-13 NA [0.036-0.063] unit increase AC108467.1 - THAP12P9 www.ncbi.nlm.nih.gov/pubmed/31217584
## rs13130484-T 4 45175691 NA NA Childhood body mass index 2e-23 NA [0.053-0.081] unit increase AC108467.1 - THAP12P9 www.ncbi.nlm.nih.gov/pubmed/26604143
## rs13130484-T 4 45175691 NA NA Hand grip strength 1e-12 NA [0.0014-0.0026] unit decrease AC108467.1 - THAP12P9 www.ncbi.nlm.nih.gov/pubmed/29691431
## rs13130484-T 4 45175691 NA NA Obesity 4e-28 NA [NR] AC108467.1 - THAP12P9 www.ncbi.nlm.nih.gov/pubmed/23563607
## rs10938397-A 4 45182527 0.992 rs10938397(G)/rs13130484(T) BMI (adjusted for smoking behaviour) 2e-23 3.00e-23 [0.026-0.039] kg/m2 decrease AC108467.1 - THAP12P9 www.ncbi.nlm.nih.gov/pubmed/28443625
## rs10938397-A 4 45182527 0.992 rs10938397(G)/rs13130484(T) BMI in non-smokers 4e-18 5.45e-18 [0.025-0.04] kg/m2 decrease AC108467.1 - THAP12P9 www.ncbi.nlm.nih.gov/pubmed/28443625
## rs10938397-G 4 45182527 0.992 rs10938397(G)/rs13130484(T) Body mass index (adult) 6e-46 1.37e-45 [0.028-0.06] unit increase AC108467.1 - THAP12P9 www.ncbi.nlm.nih.gov/pubmed/28430825
## rs10938397-A 4 45182527 0.992 rs10938397(G)/rs13130484(T) Body mass index (joint analysis main effects and smoking interaction) 7e-23 1.04e-22 AC108467.1 - THAP12P9 www.ncbi.nlm.nih.gov/pubmed/28443625
## rs10938397-A 4 45182527 0.992 rs10938397(G)/rs13130484(T) Hip circumference 9e-17 1.20e-16 [0.023-0.038] unit decrease AC108467.1 - THAP12P9 www.ncbi.nlm.nih.gov/pubmed/25673412
## rs10938397-A 4 45182527 0.992 rs10938397(G)/rs13130484(T) Menarche (age at onset) 4e-13 4.97e-13 [0.03-0.05] unit increase AC108467.1 - THAP12P9 www.ncbi.nlm.nih.gov/pubmed/25231870
## rs12507026-T 4 45181334 0.996 rs12507026(T)/rs13130484(T) Obese vs. thin 6e-15 6.81e-15 [1.13-1.23] AC108467.1 - THAP12P9 www.ncbi.nlm.nih.gov/pubmed/30677029
## rs10938398-A 4 45186139 0.992 rs10938398(G)/rs13130484(T) Type 2 diabetes 2e-09 2.32e-09 1.04-1.08 AC108467.1 - THAP12P9 www.ncbi.nlm.nih.gov/pubmed/30718926
## rs10938397-A 4 45182527 0.992 rs10938397(G)/rs13130484(T) Waist circumference 6e-20 8.45e-20 [0.025-0.038] unit decrease AC108467.1 - THAP12P9 www.ncbi.nlm.nih.gov/pubmed/25673412
##
##
## * SNP - rs34809719 (MAD1L1):
##
##
## snp chrm posh19 LD_R2 LD_alleles trait p adjusted_p effect gene url
## ------------- ----- -------- ------ ---------------------------- -------------------------------------------- ------ ----------- ------------------------------ ------------------- -------------------------------------
## rs62444881-T 7 2052318 0.942 rs62444881(T)/rs34809719(T) Educational attainment (years of education) 3e-19 3.21e-18 [0.012-0.019] unit increase AC069288.1, MAD1L1 www.ncbi.nlm.nih.gov/pubmed/30038396
## rs11766468-A 7 2082604 0.680 rs11766468(G)/rs34809719(G) Hand grip strength 8e-12 1.72e-08 [0.0022-0.0038] unit increase AC069288.1, MAD1L1 www.ncbi.nlm.nih.gov/pubmed/29691431
## rs62442924-C 7 1989976 0.887 rs62442924(T)/rs34809719(T) Highest math class taken 3e-10 3.00e-09 [0.012-0.023] unit decrease MAD1L1, AC069288.1 www.ncbi.nlm.nih.gov/pubmed/30038396
##
##
## * SNP - rs964184 (ZPR1):
##
##
## snp chrm posh19 LD_R2 LD_alleles trait p adjusted_p effect gene url
## ------------- ----- ---------- ------ -------------------------- -------------------------------------------------------------- ------- ----------- ------------------------------------------- ------ -------------------------------------
## rs964184-? 11 116648917 NA NA Age-related disease endophenotypes 3e-116 NA ZPR1 www.ncbi.nlm.nih.gov/pubmed/27790247
## rs964184-? 11 116648917 NA NA Age-related diseases, mortality and associated endophenotypes 2e-108 NA ZPR1 www.ncbi.nlm.nih.gov/pubmed/27790247
## rs964184-C 11 116648917 NA NA Blood metabolite levels 8e-15 NA [0.019-0.031] unit decrease ZPR1 www.ncbi.nlm.nih.gov/pubmed/24816252
## rs964184-C 11 116648917 NA NA Blood protein levels 8e-24 NA [NR] unit increase ZPR1 www.ncbi.nlm.nih.gov/pubmed/30072576
## rs964184-G 11 116648917 NA NA Cholesterol, total 6e-57 NA [4.11-5.25] mg/dL increase ZPR1 www.ncbi.nlm.nih.gov/pubmed/20686565
## rs964184-C 11 116648917 NA NA Coronary artery disease 8e-15 NA [0.053-0.089] unit decrease ZPR1 www.ncbi.nlm.nih.gov/pubmed/29212778
## rs964184-? 11 116648917 NA NA Coronary artery disease or large artery stroke 9e-10 NA ZPR1 www.ncbi.nlm.nih.gov/pubmed/24262325
## rs964184-G 11 116648917 NA NA Coronary heart disease 1e-17 NA [1.10-1.16] ZPR1 www.ncbi.nlm.nih.gov/pubmed/21378990
## rs964184-C 11 116648917 NA NA HDL cholesterol 6e-48 NA [NR] unit increase ZPR1 www.ncbi.nlm.nih.gov/pubmed/24097068
## rs964184-C 11 116648917 NA NA HDL cholesterol levels 6e-49 NA [0.018-0.024] unit increase ZPR1 www.ncbi.nlm.nih.gov/pubmed/30926973
## rs964184-? 11 116648917 NA NA High density lipoprotein cholesterol levels 2e-19 NA [0.91-1.43] unit increase ZPR1 www.ncbi.nlm.nih.gov/pubmed/31217584
## rs964184-? 11 116648917 NA NA Hypertriglyceridemia 5e-35 NA [1.67-1.87] ZPR1 www.ncbi.nlm.nih.gov/pubmed/23505323
## rs964184-G 11 116648917 NA NA LDL cholesterol 1e-26 NA [2.32-3.38] mg/dL increase ZPR1 www.ncbi.nlm.nih.gov/pubmed/20686565
## rs964184-C 11 116648917 NA NA LDL cholesterol levels 3e-18 NA [0.07-0.101] unit decrease (EA Beta value) ZPR1 www.ncbi.nlm.nih.gov/pubmed/28334899
## rs964184-C 11 116648917 NA NA Lipoprotein-associated phospholipase A2 activity and mass 8e-11 NA [0.022-0.042] unit decrease ZPR1 www.ncbi.nlm.nih.gov/pubmed/22003152
## rs964184-G 11 116648917 NA NA Metabolic syndrome 3e-31 NA [NR] mmol/l increase ZPR1 www.ncbi.nlm.nih.gov/pubmed/22399527
## rs964184-? 11 116648917 NA NA Metabolite levels 8e-20 NA ZPR1 www.ncbi.nlm.nih.gov/pubmed/22916037
## rs964184-C 11 116648917 NA NA Metabolite levels (lipoprotein measures) 8e-66 NA [0.22-0.26] unit decrease ZPR1 www.ncbi.nlm.nih.gov/pubmed/27005778
## rs964184-? 11 116648917 NA NA Phospholipid levels (plasma) 2e-10 NA [NR] % increase ZPR1 www.ncbi.nlm.nih.gov/pubmed/22359512
## rs964184-C 11 116648917 NA NA Postprandial triglyceride response to high fat diet meal 1e-09 NA [0.2-0.4] unit decrease ZPR1 www.ncbi.nlm.nih.gov/pubmed/26256467
## rs964184-G 11 116648917 NA NA Response to Vitamin E supplementation 3e-12 NA [0.050-0.090] mg/L increase ZPR1 www.ncbi.nlm.nih.gov/pubmed/22437554
## rs964184-C 11 116648917 NA NA Total cholesterol levels 2e-55 NA [0.11-0.14] unit decrease (EA Beta values) ZPR1 www.ncbi.nlm.nih.gov/pubmed/28334899
## rs964184-C 11 116648917 NA NA Triacylglyceride levels 1e-08 NA [0.17-0.35] unit decrease ZPR1 www.ncbi.nlm.nih.gov/pubmed/31551469
## rs964184-G 11 116648917 NA NA Triacylglycerol 56:2 levels 8e-10 NA [0.17-0.33] unit increase ZPR1 www.ncbi.nlm.nih.gov/pubmed/31560688
## rs964184-C 11 116648917 NA NA Triglyceride levels 2e-138 NA [0.07-0.082] unit decrease ZPR1 www.ncbi.nlm.nih.gov/pubmed/30926973
## rs964184-G 11 116648917 NA NA Triglycerides 7e-240 NA [16.01-17.89] mg/dL increase ZPR1 www.ncbi.nlm.nih.gov/pubmed/20686565
## rs964184-C 11 116648917 NA NA Triglycerides x physical activity interaction (2df test) 0e+00 NA ZPR1 www.ncbi.nlm.nih.gov/pubmed/30670697
## rs964184-G 11 116648917 NA NA Very low density lipoprotein cholesterol levels 4e-12 NA [0.17-0.31] unit increase ZPR1 www.ncbi.nlm.nih.gov/pubmed/28548082
## rs964184-G 11 116648917 NA NA Vitamin E levels 8e-12 NA [0.02-0.06] unit increase ZPR1 www.ncbi.nlm.nih.gov/pubmed/21729881
## rs10790162-A 11 116639104 0.507 rs10790162(G)/rs964184(G) HDL Cholesterol - Triglycerides (HDLC-TG) 3e-15 1.91e-08 [0.28-0.48] unit increase BUD13 www.ncbi.nlm.nih.gov/pubmed/21386085
## rs651821-? 11 116662579 0.439 rs651821(T)/rs964184(C) Lipid metabolism phenotypes 8e-20 1.58e-09 [0.21-0.33] unit increase APOA5 www.ncbi.nlm.nih.gov/pubmed/22286219
## rs651821-C 11 116662579 0.439 rs651821(T)/rs964184(C) Lipid traits 2e-59 4.90e-27 [0.14-0.20] mmol/L increase APOA5 www.ncbi.nlm.nih.gov/pubmed/24386095
## rs66505542-T 11 116623214 0.849 rs66505542(A)/rs964184(G) Red cell distribution width 2e-14 1.78e-12 [0.032-0.054] unit increase BUD13 www.ncbi.nlm.nih.gov/pubmed/28957414
## rs2075290-C 11 116653296 0.527 rs2075290(T)/rs964184(C) Waist Circumference - Triglycerides (WC-TG) 1e-16 1.66e-09 [0.31-0.51] unit increase ZPR1 www.ncbi.nlm.nih.gov/pubmed/21386085
##
##
## * SNP - rs1183910 (HNF1A):
##
##
## snp chrm posh19 LD_R2 LD_alleles trait p adjusted_p effect gene url
## ------------ ----- ---------- ------ -------------------------- --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ------- ----------- --------------------------------------------- ----------------- -------------------------------------
## rs1183910-T 12 121420807 NA NA C-reactive protein 1e-30 NA [10.9-16.6] % decrease HNF1A www.ncbi.nlm.nih.gov/pubmed/19567438
## rs1183910-G 12 121420807 NA NA C-reactive protein levels 2e-124 NA [0.14-0.16] unit increase HNF1A www.ncbi.nlm.nih.gov/pubmed/21300955
## rs1183910-A 12 121420807 NA NA C-reactive protein levels or LDL-cholesterol levels (pleiotropy) 6e-128 NA HNF1A www.ncbi.nlm.nih.gov/pubmed/27286809
## rs1183910-A 12 121420807 NA NA C-reactive protein levels or total cholesterol levels (pleiotropy) 8e-128 NA HNF1A www.ncbi.nlm.nih.gov/pubmed/27286809
## rs3213545-A 12 121471337 0.663 rs3213545(G)/rs1183910(G) Cardiovascular disease risk factors 4e-15 1.59e-10 [0.092-0.150] units/l decrease OASL www.ncbi.nlm.nih.gov/pubmed/21943158
## rs1169288-C 12 121416650 0.888 rs1169288(C)/rs1183910(A) Cholesterol, total 4e-17 2.25e-15 [NR] unit increase HNF1A-AS1, HNF1A www.ncbi.nlm.nih.gov/pubmed/24097068
## rs2244608-A 12 121416988 0.909 rs2244608(G)/rs1183910(G) Coronary artery disease 7e-19 2.62e-17 [0.04-0.063] unit decrease HNF1A, HNF1A-AS1 www.ncbi.nlm.nih.gov/pubmed/29212778
## rs2244608-G 12 121416988 0.909 rs2244608(G)/rs1183910(G) Coronary artery disease (myocardial infarction, percutaneous transluminal coronary angioplasty, coronary artery bypass grafting, angina or chromic ischemic heart disease) 8e-10 4.65e-09 [1.035-1.070] HNF1A, HNF1A-AS1 www.ncbi.nlm.nih.gov/pubmed/28714975
## rs1169288-? 12 121416650 0.888 rs1169288(C)/rs1183910(A) Gallstone disease 2e-14 5.67e-13 [1.06-1.1] HNF1A-AS1, HNF1A www.ncbi.nlm.nih.gov/pubmed/30504769
## rs1169288-G 12 121416650 0.888 rs1169288(C)/rs1183910(A) Gamma glutamyl transferase levels 2e-18 1.57e-16 [0.10-0.16] unit decrease HNF1A-AS1, HNF1A www.ncbi.nlm.nih.gov/pubmed/22010049
## rs2393791-G 12 121423956 0.692 rs2393791(T)/rs1183910(G) Gamma glutamyl transpeptidase 7e-30 3.46e-21 [0.0062-0.0090] IU/L increase HNF1A www.ncbi.nlm.nih.gov/pubmed/21909109
## rs2244608-G 12 121416988 0.909 rs2244608(G)/rs1183910(G) HDL cholesterol levels x alcohol consumption (drinkers vs non-drinkers) interaction (2df) 4e-09 2.02e-08 HNF1A, HNF1A-AS1 www.ncbi.nlm.nih.gov/pubmed/30698716
## rs2251468-A 12 121405126 0.814 rs2251468(C)/rs1183910(A) Homocysteine levels 1e-12 1.23e-10 [0.037-0.065] unit decrease HNF1A-AS1 www.ncbi.nlm.nih.gov/pubmed/23824729
## rs2650000-A 12 121388962 0.807 rs2650000(C)/rs1183910(G) Insulinogenic index 5e-10 2.30e-08 [0.052-0.1] unit decrease HNF1A-AS1 www.ncbi.nlm.nih.gov/pubmed/23263489
## rs1169288-C 12 121416650 0.888 rs1169288(C)/rs1183910(A) LDL cholesterol 6e-21 8.99e-19 [NR] unit increase HNF1A-AS1, HNF1A www.ncbi.nlm.nih.gov/pubmed/24097068
## rs1169288-A 12 121416650 0.888 rs1169288(C)/rs1183910(A) LDL cholesterol levels 2e-22 4.37e-20 [0.03-0.045] unit decrease (EA Beta value) HNF1A-AS1, HNF1A www.ncbi.nlm.nih.gov/pubmed/28334899
## rs2244608-G 12 121416988 0.909 rs2244608(G)/rs1183910(G) LDL cholesterol levels in current drinkers 1e-19 4.46e-18 NR unit decrease HNF1A, HNF1A-AS1 www.ncbi.nlm.nih.gov/pubmed/30698716
## rs2244608-G 12 121416988 0.909 rs2244608(G)/rs1183910(G) LDL cholesterol levels x alcohol consumption (drinkers vs non-drinkers) interaction (2df) 2e-28 5.40e-26 HNF1A, HNF1A-AS1 www.ncbi.nlm.nih.gov/pubmed/30698716
## rs2244608-G 12 121416988 0.909 rs2244608(G)/rs1183910(G) LDL cholesterol levels x alcohol consumption (regular vs non-regular drinkers) interaction (2df) 3e-26 5.16e-24 HNF1A, HNF1A-AS1 www.ncbi.nlm.nih.gov/pubmed/30698716
## rs6489786-A 12 121397875 0.810 rs6489786(G)/rs1183910(A) LDL cholesterol x physical activity interaction (2df test) 7e-19 1.37e-15 HNF1A-AS1 www.ncbi.nlm.nih.gov/pubmed/30670697
## rs7310409-G 12 121424861 0.688 rs7310409(G)/rs1183910(G) Liver enzyme levels (gamma-glutamyl transferase) 7e-45 2.05e-31 [5.70-7.80] % increase HNF1A www.ncbi.nlm.nih.gov/pubmed/22001757
## rs2650000-A 12 121388962 0.807 rs2650000(C)/rs1183910(G) Metabolic traits 3e-11 2.34e-09 [0.25-0.55] mmol/l decrease HNF1A-AS1 www.ncbi.nlm.nih.gov/pubmed/19060910
## rs1169288-C 12 121416650 0.888 rs1169288(C)/rs1183910(A) Serum alpha1-antitrypsin levels 2e-12 3.41e-11 [1.3-2.28] mg dl-1 increase HNF1A-AS1, HNF1A www.ncbi.nlm.nih.gov/pubmed/26174136
## rs2244608-A 12 121416988 0.909 rs2244608(G)/rs1183910(G) Total cholesterol levels 4e-19 1.57e-17 [0.024-0.039] unit decrease (EA Beta values) HNF1A, HNF1A-AS1 www.ncbi.nlm.nih.gov/pubmed/28334899
## rs1169288-C 12 121416650 0.888 rs1169288(C)/rs1183910(A) Type 2 diabetes 1e-11 1.43e-10 [1.03-1.06] HNF1A-AS1, HNF1A www.ncbi.nlm.nih.gov/pubmed/29632382
## rs1169288-C 12 121416650 0.888 rs1169288(C)/rs1183910(A) Type 2 diabetes (adjusted for BMI) 2e-10 2.05e-09 [1.02-1.06] HNF1A-AS1, HNF1A www.ncbi.nlm.nih.gov/pubmed/29632382
##
##
## * SNP - rs8049439 (ATXN2L):
##
##
## snp chrm posh19 LD_R2 LD_alleles trait p adjusted_p effect gene url
## -------------- ----- --------- ------ ---------------------------- ---------------------------------------------------------------------- ------ ----------- ----------------------------------- ----------------------- -------------------------------------
## rs8049439-T 16 28837515 NA NA Educational attainment (years of education) 7e-11 NA NR unit increase ATXN2L www.ncbi.nlm.nih.gov/pubmed/27225129
## rs8049439-G 16 28837515 NA NA Inflammatory bowel disease (early onset) 2e-09 NA [1.00-1.30] ATXN2L www.ncbi.nlm.nih.gov/pubmed/19915574
## rs113079736-A 16 28860183 0.935 rs113079736(G)/rs8049439(T) Alcohol consumption (drinks per week) 3e-11 1.29e-10 [0.011-0.02] unit decrease SH2B1 www.ncbi.nlm.nih.gov/pubmed/30643258
## rs3888190-A 16 28889486 0.931 rs3888190(C)/rs8049439(T) BMI (adjusted for smoking behaviour) 2e-18 2.92e-17 [0.023-0.035] kg/m2 increase SH2B1 - ATP2A1 www.ncbi.nlm.nih.gov/pubmed/28443625
## rs3888190-A 16 28889486 0.931 rs3888190(C)/rs8049439(T) BMI in non-smokers 4e-15 3.48e-14 [0.022-0.037] kg/m2 increase SH2B1 - ATP2A1 www.ncbi.nlm.nih.gov/pubmed/28443625
## rs4788099-G 16 28855727 0.935 rs4788099(G)/rs8049439(C) Body fat percentage 1e-08 2.98e-08 [0.017-0.037] body fat % increase TUFM www.ncbi.nlm.nih.gov/pubmed/26833246
## rs3888190-A 16 28889486 0.931 rs3888190(C)/rs8049439(T) Body mass index 2e-31 2.26e-29 [0.025-0.037] kg/m2 increase SH2B1 - ATP2A1 www.ncbi.nlm.nih.gov/pubmed/28892062
## rs3888190-A 16 28889486 0.931 rs3888190(C)/rs8049439(T) Body mass index (joint analysis main effects and smoking interaction) 6e-18 8.13e-17 SH2B1 - ATP2A1 www.ncbi.nlm.nih.gov/pubmed/28443625
## rs8055982-? 16 28881202 0.935 rs8055982(C)/rs8049439(C) Cognitive ability 1e-09 3.45e-09 z score increase SH2B1 www.ncbi.nlm.nih.gov/pubmed/29186694
## rs8062405-? 16 28837906 0.931 rs8062405(G)/rs8049439(C) Cognitive ability (MTAG) 4e-14 2.96e-13 z score increase ATXN2L www.ncbi.nlm.nih.gov/pubmed/29186694
## rs12448902-C 16 28871191 0.858 rs12448902(G)/rs8049439(T) Cognitive performance 5e-19 1.54e-16 [0.02-0.032] unit increase SH2B1 www.ncbi.nlm.nih.gov/pubmed/30038396
## rs72793812-A 16 28840381 0.922 rs72793812(G)/rs8049439(T) Cognitive performance (MTAG) 8e-35 2.98e-32 [0.024-0.033] unit increase ATXN2L www.ncbi.nlm.nih.gov/pubmed/30038396
## rs12928404-T 16 28847246 0.974 rs12928404(T)/rs8049439(T) Extremely high intelligence 2e-08 3.06e-08 NR z score increase increase AC133550.1, ATXN2L www.ncbi.nlm.nih.gov/pubmed/29520040
## rs62037363-T 16 28865042 0.935 rs62037363(T)/rs8049439(T) General cognitive ability 7e-19 9.21e-18 z-score increase SH2B1 www.ncbi.nlm.nih.gov/pubmed/29844566
## rs12928404-C 16 28847246 0.974 rs12928404(T)/rs8049439(T) Hand grip strength 1e-25 4.28e-25 [0.0024-0.0036] unit decrease AC133550.1, ATXN2L www.ncbi.nlm.nih.gov/pubmed/29691431
## rs11864750-A 16 28875204 0.935 rs11864750(T)/rs8049439(C) Highest math class taken (MTAG) 7e-37 1.31e-34 [0.018-0.025] unit increase SH2B1 www.ncbi.nlm.nih.gov/pubmed/30038396
## rs3888190-A 16 28889486 0.931 rs3888190(C)/rs8049439(T) Hip circumference 9e-22 2.22e-20 [0.027-0.042] unit increase SH2B1 - ATP2A1 www.ncbi.nlm.nih.gov/pubmed/25673412
## rs7187776-A 16 28857645 0.991 rs7187776(G)/rs8049439(C) Hip circumference adjusted for BMI 4e-08 4.59e-08 [0.013-0.027] unit decrease TUFM www.ncbi.nlm.nih.gov/pubmed/25673412
## rs62037363-? 16 28865042 0.935 rs62037363(T)/rs8049439(T) Inflammatory bowel disease 6e-22 1.24e-20 SH2B1 www.ncbi.nlm.nih.gov/pubmed/26192919
## rs2008514-A 16 28825605 0.927 rs2008514(G)/rs8049439(T) Intelligence 1e-24 4.97e-23 z-score decrease AC145285.1, AC145285.2 www.ncbi.nlm.nih.gov/pubmed/29942086
## rs7498665-G 16 28883241 0.935 rs7498665(G)/rs8049439(C) Obesity 3e-13 1.73e-12 [NR] SH2B1 www.ncbi.nlm.nih.gov/pubmed/23563607
## rs12446589-G 16 28870962 0.927 rs12446589(G)/rs8049439(T) Self-reported math ability 3e-27 2.25e-25 [0.018-0.026] unit increase SH2B1 www.ncbi.nlm.nih.gov/pubmed/30038396
## rs12446589-A 16 28870962 0.927 rs12446589(G)/rs8049439(T) Self-reported math ability (MTAG) 2e-36 6.96e-34 [0.02-0.027] unit decrease SH2B1 www.ncbi.nlm.nih.gov/pubmed/30038396
## rs56163509-? 16 28864471 0.931 rs56163509(G)/rs8049439(C) Tonsillectomy 1e-08 3.20e-08 [0.034-0.07] unit increase SH2B1 www.ncbi.nlm.nih.gov/pubmed/28928442
## rs7498665-A 16 28883241 0.935 rs7498665(G)/rs8049439(C) Waist circumference 1e-22 2.32e-21 [0.027-0.041] unit decrease SH2B1 www.ncbi.nlm.nih.gov/pubmed/25673412
## rs7498665-G 16 28883241 0.935 rs7498665(G)/rs8049439(C) Weight 1e-09 3.45e-09 [2.51-4.87] percentage SD increase SH2B1 www.ncbi.nlm.nih.gov/pubmed/19079260
##
##
## * SNP - rs1421085 (FTO):
##
##
## snp chrm posh19 LD_R2 LD_alleles trait p adjusted_p effect gene url
## ------------- ----- --------- ------ --------------------------- ------------------------------------------------------------------------------------------ ------- ----------- ----------------------------------- ----- -------------------------------------
## rs1421085-T 16 53800954 NA NA Alcohol consumption 9e-13 NA [0.006-0.01] unit decrease FTO www.ncbi.nlm.nih.gov/pubmed/31358974
## rs1421085-C 16 53800954 NA NA Body mass index 2e-75 NA [0.068-0.084] unit increase FTO www.ncbi.nlm.nih.gov/pubmed/26961502
## rs1421085-C 16 53800954 NA NA Childhood body mass index 5e-16 NA [0.045-0.073] unit increase FTO www.ncbi.nlm.nih.gov/pubmed/26604143
## rs1421085-C 16 53800954 NA NA Dietary macronutrient intake 1e-16 NA [0.059-0.099] % increase FTO www.ncbi.nlm.nih.gov/pubmed/29988085
## rs1421085-C 16 53800954 NA NA Hand grip strength 5e-38 NA [0.0034-0.0046] unit decrease FTO www.ncbi.nlm.nih.gov/pubmed/29691431
## rs1421085-C 16 53800954 NA NA Obesity 6e-39 NA [NR] FTO www.ncbi.nlm.nih.gov/pubmed/23563607
## rs1421085-C 16 53800954 NA NA Obesity (early onset extreme) 3e-28 NA [1.35-1.54] FTO www.ncbi.nlm.nih.gov/pubmed/23563609
## rs1421085-C 16 53800954 NA NA Type 2 diabetes 1e-28 NA 1.11-1.15 FTO www.ncbi.nlm.nih.gov/pubmed/30718926
## rs8050136-C 16 53816275 0.918 rs8050136(C)/rs1421085(T) Adiposity 3e-26 3.19e-24 [NR] % decrease FTO www.ncbi.nlm.nih.gov/pubmed/21706003
## rs9939609-? 16 53820527 0.918 rs9939609(T)/rs1421085(T) Age-related disease endophenotypes 2e-17 4.06e-16 FTO www.ncbi.nlm.nih.gov/pubmed/27790247
## rs9939609-? 16 53820527 0.918 rs9939609(T)/rs1421085(T) Age-related diseases, mortality and associated endophenotypes 1e-14 1.23e-13 FTO www.ncbi.nlm.nih.gov/pubmed/27790247
## rs1558902-A 16 53803574 1.000 rs1558902(T)/rs1421085(T) BMI (adjusted for smoking behaviour) 3e-110 3.00e-110 [0.068-0.082] kg/m2 increase FTO www.ncbi.nlm.nih.gov/pubmed/28443625
## rs1558902-A 16 53803574 1.000 rs1558902(T)/rs1421085(T) BMI in non-smokers 1e-79 1.00e-79 [0.065-0.079] kg/m2 increase FTO www.ncbi.nlm.nih.gov/pubmed/28443625
## rs1558902-A 16 53803574 1.000 rs1558902(T)/rs1421085(T) BMI in smokers 8e-39 8.00e-39 [0.079-0.108] kg/m2 increase FTO www.ncbi.nlm.nih.gov/pubmed/28443625
## rs1558902-A 16 53803574 1.000 rs1558902(T)/rs1421085(T) Body fat percentage 4e-27 4.00e-27 [0.041-0.061] body fat % increase FTO www.ncbi.nlm.nih.gov/pubmed/26833246
## rs17817964-T 16 53828066 0.918 rs17817964(T)/rs1421085(T) Body mass index (adult) 1e-146 7.36e-135 [0.049-0.089] unit increase FTO www.ncbi.nlm.nih.gov/pubmed/28430825
## rs1558902-A 16 53803574 1.000 rs1558902(T)/rs1421085(T) Body mass index (age interaction) 1e-26 1.00e-26 FTO www.ncbi.nlm.nih.gov/pubmed/25953783
## rs1558902-A 16 53803574 1.000 rs1558902(T)/rs1421085(T) Body mass index (joint analysis main effects and smoking interaction) 2e-115 2.00e-115 FTO www.ncbi.nlm.nih.gov/pubmed/28443625
## rs12149832-A 16 53842908 0.837 rs12149832(G)/rs1421085(T) Body mass index (SNP x SNP interaction) 5e-22 1.07e-18 [0.057-0.089] unit increase FTO www.ncbi.nlm.nih.gov/pubmed/22344221
## rs17817449-T 16 53813367 0.922 rs17817449(T)/rs1421085(T) Breast cancer 2e-14 2.07e-13 [1.05-1.1] FTO www.ncbi.nlm.nih.gov/pubmed/25751625
## rs17817449-T 16 53813367 0.922 rs17817449(T)/rs1421085(T) Breast cancer (estrogen-receptor negative) 2e-10 1.02e-09 [1.04-1.11] (Oncoarray) FTO www.ncbi.nlm.nih.gov/pubmed/29058716
## rs1558902-A 16 53803574 1.000 rs1558902(T)/rs1421085(T) C-reactive protein levels or HDL-cholesterol levels (pleiotropy) 5e-09 5.00e-09 FTO www.ncbi.nlm.nih.gov/pubmed/27286809
## rs8043757-T 16 53813450 0.918 rs8043757(T)/rs1421085(C) circulating leptin levels 1e-09 4.84e-09 [0.02-0.04] unit increase FTO www.ncbi.nlm.nih.gov/pubmed/26833098
## rs9937053-A 16 53799507 0.949 rs9937053(G)/rs1421085(T) Coffee consumption 2e-21 2.08e-20 [0.011-0.017] unit increase FTO www.ncbi.nlm.nih.gov/pubmed/31046077
## rs11642015-? 16 53802494 1.000 rs11642015(T)/rs1421085(C) Diastolic blood pressure x smoking status (current vs non-current) interaction (2df test) 3e-11 3.00e-11 FTO www.ncbi.nlm.nih.gov/pubmed/29455858
## rs11642015-? 16 53802494 1.000 rs11642015(T)/rs1421085(C) Diastolic blood pressure x smoking status (ever vs never) interaction (2df test) 2e-10 2.00e-10 FTO www.ncbi.nlm.nih.gov/pubmed/29455858
## rs1558902-A 16 53803574 1.000 rs1558902(T)/rs1421085(T) Glycated hemoglobin levels 3e-08 3.00e-08 [0.0066-0.014] unit increase FTO www.ncbi.nlm.nih.gov/pubmed/28898252
## rs1121980-A 16 53809247 0.956 rs1121980(G)/rs1421085(T) HDL cholesterol 7e-09 1.48e-08 [NR] unit decrease FTO www.ncbi.nlm.nih.gov/pubmed/24097068
## rs8063057-T 16 53812433 0.918 rs8063057(T)/rs1421085(T) Heel bone mineral density 5e-26 5.15e-24 [0.016-0.024] unit decrease FTO www.ncbi.nlm.nih.gov/pubmed/30598549
## rs1558902-A 16 53803574 1.000 rs1558902(T)/rs1421085(T) Height adjusted BMI 9e-10 9.00e-10 [0.13-0.27] unit increase FTO www.ncbi.nlm.nih.gov/pubmed/25044758
## rs9939973-A 16 53800568 0.956 rs9939973(G)/rs1421085(T) Hip circumference 2e-86 9.50e-83 [0.063-0.077] unit increase FTO www.ncbi.nlm.nih.gov/pubmed/25673412
## rs9936385-? 16 53819169 0.918 rs9936385(T)/rs1421085(T) Lean body mass 7e-13 6.08e-12 [0.11-0.19] unit decrease FTO www.ncbi.nlm.nih.gov/pubmed/28724990
## rs8050136-C 16 53816275 0.918 rs8050136(C)/rs1421085(T) Menarche (age at onset) 2e-17 4.06e-16 [0.03-0.05] unit increase FTO www.ncbi.nlm.nih.gov/pubmed/25231870
## rs9940128-A 16 53800754 0.956 rs9940128(G)/rs1421085(T) Metabolic syndrome 2e-09 4.47e-09 [NR] cm increase FTO www.ncbi.nlm.nih.gov/pubmed/22399527
## rs9928094-G 16 53799905 0.953 rs9928094(G)/rs1421085(T) Obesity (extreme) 6e-101 2.89e-96 [1.29-1.36] FTO www.ncbi.nlm.nih.gov/pubmed/30677029
## rs9928094-A 16 53799905 0.953 rs9928094(G)/rs1421085(T) Pulse pressure x alcohol consumption interaction (2df test) 3e-15 1.34e-14 FTO www.ncbi.nlm.nih.gov/pubmed/29912962
## rs9972653-G 16 53814363 0.918 rs9972653(T)/rs1421085(C) Pure non-grapefruit juice consumption 1e-09 4.84e-09 [0.0054-0.0104] unit increase FTO www.ncbi.nlm.nih.gov/pubmed/31046077
## rs55872725-? 16 53809123 1.000 rs55872725(T)/rs1421085(C) Strenuous sports or other exercises 3e-13 3.00e-13 FTO www.ncbi.nlm.nih.gov/pubmed/29899525
## rs7185735-A 16 53822651 0.922 rs7185735(G)/rs1421085(C) Subcutaneous adipose tissue 1e-09 4.48e-09 z-score decrease FTO www.ncbi.nlm.nih.gov/pubmed/27918534
## rs55872725-C 16 53809123 1.000 rs55872725(T)/rs1421085(C) Sugar-sweetened beverage consumption 2e-15 2.00e-15 [0.0071-0.0119] unit increase FTO www.ncbi.nlm.nih.gov/pubmed/31046077
## rs55872725-T 16 53809123 1.000 rs55872725(T)/rs1421085(C) Systolic blood pressure x alcohol consumption interaction (2df test) 2e-13 2.00e-13 FTO www.ncbi.nlm.nih.gov/pubmed/29912962
## rs11642015-? 16 53802494 1.000 rs11642015(T)/rs1421085(C) Systolic blood pressure x smoking status (current vs non-current) interaction (2df test) 1e-20 1.00e-20 FTO www.ncbi.nlm.nih.gov/pubmed/29455858
## rs11642015-? 16 53802494 1.000 rs11642015(T)/rs1421085(C) Systolic blood pressure x smoking status (ever vs never) interaction (2df test) 4e-20 4.00e-20 FTO www.ncbi.nlm.nih.gov/pubmed/29455858
## rs56094641-? 16 53806453 0.984 rs56094641(G)/rs1421085(T) Type 2 diabetes nephropathy 8e-10 1.09e-09 [1.14-1.29] FTO www.ncbi.nlm.nih.gov/pubmed/30566433
## rs56094641-G 16 53806453 0.984 rs56094641(G)/rs1421085(T) Type 2 diabetes nephropathy including microalbuminuria 2e-09 2.69e-09 [1.12-1.24] FTO www.ncbi.nlm.nih.gov/pubmed/30566433
## rs11642015-C 16 53802494 1.000 rs11642015(T)/rs1421085(C) Urinary sodium excretion 7e-23 7.00e-23 [0.0099-0.0149] unit decrease FTO www.ncbi.nlm.nih.gov/pubmed/31409800
## rs1558902-A 16 53803574 1.000 rs1558902(T)/rs1421085(T) Waist circumference 4e-101 4.00e-101 [0.067-0.081] unit increase FTO www.ncbi.nlm.nih.gov/pubmed/25673412
## rs1121980-A 16 53809247 0.956 rs1121980(G)/rs1421085(T) Waist-hip ratio 1e-38 4.07e-37 [0.037-0.05] unit increase FTO www.ncbi.nlm.nih.gov/pubmed/25673412
## rs8050136-A 16 53816275 0.918 rs8050136(C)/rs1421085(T) Weight 5e-36 3.34e-33 [5.95-8.15] percentage SD increase FTO www.ncbi.nlm.nih.gov/pubmed/19079260
##
##
## * SNP - rs999474 (UBE2Z):
##
##
## snp chrm posh19 LD_R2 LD_alleles trait p adjusted_p effect gene url
## ------------ ----- --------- ------ ------------------------- -------------------------------------------- ------ ----------- ----------------------------- --------- -------------------------------------
## rs1058018-T 17 47000251 0.984 rs1058018(T)/rs999474(G) Educational attainment (years of education) 9e-10 1.24e-09 [0.006-0.0114] unit decrease UBE2Z www.ncbi.nlm.nih.gov/pubmed/30038396
## rs318095-T 17 46974734 0.815 rs318095(T)/rs999474(G) Height 2e-16 1.15e-13 [0.018-0.03] unit increase SUMO2P17 www.ncbi.nlm.nih.gov/pubmed/25282103
## rs9894220-G 17 46989154 1.000 rs9894220(G)/rs999474(G) Type 2 diabetes 2e-13 2.00e-13 [0.043-0.074] unit decrease UBE2Z www.ncbi.nlm.nih.gov/pubmed/30054458
##
##
## * SNP - rs303757 (RMC1):
##
##
## snp chrm posh19 LD_R2 LD_alleles trait p adjusted_p effect gene url
## ------------ ----- --------- ------ ------------------------- -------------------------------------------- ------ ----------- ------------------------------ ------------- -------------------------------------
## rs303753-A 18 21074922 0.991 rs303753(G)/rs303757(T) Alcohol consumption (drinks per week) 4e-08 4.59e-08 [0.0083-0.0175] unit decrease RIOK3 - RMC1 www.ncbi.nlm.nih.gov/pubmed/30643258
## rs1788820-G 18 21101944 0.965 rs1788820(G)/rs303757(T) Bitter alcoholic beverage consumption 2e-10 4.11e-10 [0.0045-0.0089] unit increase NPC1, RMC1 www.ncbi.nlm.nih.gov/pubmed/31046077
## rs1788799-C 18 21124945 0.965 .(G)/rs303757(T) Body mass index 1e-17 3.66e-17 [0.012-0.02] unit increase NPC1 www.ncbi.nlm.nih.gov/pubmed/29273807
## rs303752-A 18 21074255 0.696 rs303752(G)/rs303757(T) Educational attainment (MTAG) 3e-12 5.87e-09 [0.0068-0.0118] unit decrease RIOK3 - RMC1 www.ncbi.nlm.nih.gov/pubmed/30038396
## rs1623003-T 18 21165163 0.893 rs1623003(T)/rs303757(T) Educational attainment (years of education) 4e-12 5.50e-11 [0.0074-0.0132] unit increase NPC1 www.ncbi.nlm.nih.gov/pubmed/30038396
## rs303760-T 18 21083738 0.978 rs303760(T)/rs303757(G) Hand grip strength 2e-08 2.85e-08 [0.0014-0.0026] unit decrease RMC1 www.ncbi.nlm.nih.gov/pubmed/29691431
Interestingly, we can see that all these loci have been associated with some of the risk factors used to create the prior. The variants near USP4 and MAD1L1 have been associated with Educational Attainment. The variants near GNPDA2 and FTO have been associated with Body Mass Index. The variant near ZPR1 has been associated with Coronary Artery Disease and LDL Cholesterol. Variants in LD with the variant identified near HNF1A have been associated with Coronary Artery Disease and LDL cholesterol. The variant near ATXN2L has been associated with Educational Attainment, and variants in LD have been associated with Body Mass Index. A variant in LD with the variant identified near UBE2Z has been associated with Educational Attainment. Variants in LD with the variant identified near RMC1 have been associated with Body Mass Index and Educational Attainment.
Results - Direct Effects
With this approach, we identified 4 SNPs having significant direct effects. Since there are only a small number of hits here, look at all of them in details:
# all direct hits
extract_results_bGWAS(Lifespan_bGWAS, results="direct") %>%
mutate(p_direct = as.character(format(p_direct, scientific=T, digits=3))) %>%
arrange(chrm_UK10K, pos_UK10K) -> Direct_Hits
Gene_Info_Direct <- do.call(rbind.data.frame,
apply(Direct_Hits, 1, function(x) get_geneInfo(as.numeric(x[2]), as.numeric(x[3]), x[4], x[5])))
Gene_Info_Direct %>%
bind_cols(Direct_Hits) -> Direct_Hits
knitr::kable(Direct_Hits, digits=3)
| Function | Gene | Distance | rsid | chrm_UK10K | pos_UK10K | alt | ref | beta | se | z_obs | mu_direct_estimate | mu_direct_std_error | beta_direct_estimate | beta_direct_std_error | z_direct | p_direct | CRR | | :------- | :---- | -------: | :--------- | ----------: | ---------: | :-- | :-- | ------: | ----: | -------: | -------------------: | ---------------------: | ---------------------: | -----------------------: | --------: | :-------- | ----: | | intronic | LPA | 0 | rs55730499 | 6 | 161005610 | T | C | -0.076 | 0.007 | -10.258 | -8.295 | 1.166 | -0.061 | 0.009 | -7.114 | 1.13e-12 | 0.809 | | intronic | RAD52 | 0 | rs7307680 | 12 | 1052488 | A | G | -0.026 | 0.005 | -5.286 | -6.196 | 1.134 | -0.031 | 0.006 | -5.465 | 4.64e-08 | 1.172 | | intronic | HYKK | 0 | rs8042849 | 15 | 78817929 | T | C | 0.044 | 0.004 | 10.659 | 10.395 | 1.118 | 0.043 | 0.005 | 9.300 | 1.41e-20 | 0.975 | | exonic | APOE | 0 | rs429358 | 19 | 45411941 | T | C | 0.106 | 0.005 | 19.328 | 17.473 | 1.228 | 0.095 | 0.007 | 14.232 | 5.79e-46 | 0.904 |
Direct_Hits %>%
transmute(rs=rsid,
gene = Gene,
color="#932735") -> my_SNPsDirect
manhattan_plot_bGWAS(Lifespan_bGWAS, SNPs=my_SNPsDirect, results = "direct")
variant near LPA
# look if significant in conventional GWAS / using BF or posterior p-values)
# look at SNPs in a 100kb window around (to assess significance in conventional GWAS / bGWAS results)
dist=100000
Direct_Hits %>% dplyr::slice(1) %>% pull(chrm_UK10K) -> chr_LPA
Direct_Hits %>% dplyr::slice(1) %>% pull(pos_UK10K) -> pos_LPA
extract_results_bGWAS(Lifespan_bGWAS, SNPs = "all", results="everything") %>%
filter(chrm_UK10K == chr_LPA,
pos_UK10K > pos_LPA - dist,
pos_UK10K < pos_LPA + dist) %>%
mutate(p_obs = 2*pnorm(-abs(z_obs))) %>%
dplyr::select(p_obs, BF_p, p_posterior) -> res_LPA
# significant in conventional GWAS
any(res_LPA$p_obs<5e-8)
[1] TRUE
# significant in bGWAS
any(res_LPA$BF_p<5e-8)
[1] TRUE
# significant using posterior effects
any(res_LPA$p_posterior<5e-8)
[1] TRUE
# Association with other traits
LPA = Direct_Hits[1,]
knitr::kable(extract_results_bGWAS(Lifespan_bGWAS, SNPs = "all", results="everything") %>%
filter(rsid == LPA$rsid) %>%
mutate(BF = as.character(format(BF, scientific=T, digits=3)),
BF_p = as.character(format(BF_p, scientific=T, digits=3)),
p_direct = as.character(format(p_direct, scientific=T, digits=3)),
p_posterior = as.character(format(p_posterior, scientific=T, digits=3))),
digits=3)
| chrm_UK10K | pos_UK10K | rsid | alt | ref | beta | se | z_obs | mu_prior_estimate | mu_prior_std_error | mu_posterior_estimate | mu_posterior_std_error | z_posterior | mu_direct_estimate | mu_direct_std_error | z_direct | beta_prior_estimate | beta_prior_std_error | beta_posterior_estimate | beta_posterior_std_error | beta_direct_estimate | beta_direct_std_error | CRR | BF | BF_p | p_posterior | p_direct | | ----------: | ---------: | :--------- | :-- | :-- | ------: | ----: | -------: | ------------------: | --------------------: | ----------------------: | ------------------------: | -----------: | -------------------: | ---------------------: | --------: | --------------------: | ----------------------: | ------------------------: | --------------------------: | ---------------------: | -----------------------: | ----: | :------- | :------- | :----------- | :-------- | | 6 | 161005610 | rs55730499 | T | C | -0.076 | 0.007 | -10.258 | -1.963 | 0.6 | -4.157 | 0.514 | -8.083 | -8.295 | 1.166 | -7.114 | -0.015 | 0.004 | -0.031 | 0.004 | -0.061 | 0.009 | 0.809 | 6.21e+11 | 2.11e-21 | 6.33e-16 | 1.13e-12 |
Info_LPA = get_associatedTraits(LPA$rsid, LPA$chrm_UK10K, LPA$pos_UK10K,
LD=0.1, distance=100000, P=5e-8,
gwascatdata = my_ebicat37)
# format for nice kable output
Info_LPA %>%
mutate(p=as.character(format(p, scientific=T, digits=3)),
adjusted_p = as.character(format(adjusted_p, scientific=T, digits=3))) -> Info_LPA
##
##
## snp chrm posh19 LD_R2 LD_alleles trait p adjusted_p effect gene url
## -------------- ----- ---------- ------ ----------------------------- --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ------- ----------- ----------------------------------- ----- -------------------------------------
## rs55730499-T 6 161005610 NA NA Coronary artery disease 3e-154 NA [0.31-0.36] unit increase LPA www.ncbi.nlm.nih.gov/pubmed/29212778
## rs55730499-T 6 161005610 NA NA Lipoprotein(a) levels adjusted for apolipoprotein(a) isoforms 4e-163 NA [15.63-17.91] unit increase LPA www.ncbi.nlm.nih.gov/pubmed/28512139
## rs55730499-T 6 161005610 NA NA Parental lifespan 9e-11 NA years decrease LPA www.ncbi.nlm.nih.gov/pubmed/29030599
## rs10455872-G 6 161010118 0.972 rs10455872(G)/rs55730499(T) Aortic valve stenosis 2e-31 1.40e-30 [1.37-1.56] LPA www.ncbi.nlm.nih.gov/pubmed/29511194
## rs10455872-G 6 161010118 0.972 rs10455872(G)/rs55730499(T) Aortic-valve calcification 3e-11 5.70e-11 [1.66-2.53] LPA www.ncbi.nlm.nih.gov/pubmed/23388002
## rs74617384-T 6 160997118 0.986 rs74617384(T)/rs55730499(T) Cholesterol, total 3e-09 3.88e-09 [0.081-0.161] mg/dl increase LPA www.ncbi.nlm.nih.gov/pubmed/28270201
## rs10455872-G 6 161010118 0.972 rs10455872(G)/rs55730499(T) Coronary artery disease (myocardial infarction, percutaneous transluminal coronary angioplasty, coronary artery bypass grafting, angina or chromic ischemic heart disease) 2e-49 4.51e-48 [1.27-1.36] LPA www.ncbi.nlm.nih.gov/pubmed/28714975
## rs74617384-T 6 160997118 0.986 rs74617384(T)/rs55730499(T) Coronary artery disease in diabetes 3e-12 4.27e-12 [1.26-1.51] LPA www.ncbi.nlm.nih.gov/pubmed/30003307
## rs10455872-? 6 161010118 0.972 rs10455872(G)/rs55730499(T) Coronary artery disease or ischemic stroke 2e-12 4.09e-12 LPA www.ncbi.nlm.nih.gov/pubmed/24262325
## rs10455872-? 6 161010118 0.972 rs10455872(G)/rs55730499(T) Coronary artery disease or large artery stroke 9e-14 2.01e-13 LPA www.ncbi.nlm.nih.gov/pubmed/24262325
## rs74617384-A 6 160997118 0.986 rs74617384(T)/rs55730499(T) LDL cholesterol x physical activity interaction (2df test) 4e-46 1.71e-45 LPA www.ncbi.nlm.nih.gov/pubmed/30670697
## rs118039278-A 6 160985526 0.972 rs118039278(G)/rs55730499(T) Lipoprotein (a) levels 0e+00 0.00e+00 [31.14-33.72] mg/dL increase LPA www.ncbi.nlm.nih.gov/pubmed/28512139
## rs10455872-G 6 161010118 0.972 rs10455872(G)/rs55730499(T) Lipoprotein phospholipase A2 activity in cardiovascular disease 4e-13 8.57e-13 [5.26-9.18] (nmol/min/ml) increase LPA www.ncbi.nlm.nih.gov/pubmed/28753643
## rs10455872-G 6 161010118 0.972 rs10455872(G)/rs55730499(T) Lipoprotein-associated phospholipase A2 activity change in response to statin therapy 2e-16 5.30e-16 [0.82-2.78] percent increase LPA www.ncbi.nlm.nih.gov/pubmed/23118302
## rs10455872-? 6 161010118 0.972 rs10455872(G)/rs55730499(T) Low density lipoprotein cholesterol levels 5e-17 1.38e-16 [6.2-9.98] unit increase LPA www.ncbi.nlm.nih.gov/pubmed/31217584
## rs10455872-G 6 161010118 0.972 rs10455872(G)/rs55730499(T) Lp (a) levels 5e-39 5.73e-38 [1.08-1.48] mg/dL increase LPA www.ncbi.nlm.nih.gov/pubmed/21900290
## rs10455872-G 6 161010118 0.972 rs10455872(G)/rs55730499(T) Metabolite levels (lipoprotein measures) 1e-12 2.09e-12 [0.14-0.26] unit decrease LPA www.ncbi.nlm.nih.gov/pubmed/27005778
## rs10455872-G 6 161010118 0.972 rs10455872(G)/rs55730499(T) Myocardial infarction 9e-27 4.66e-26 [1.27-1.4] LPA www.ncbi.nlm.nih.gov/pubmed/26343387
## rs10455872-A 6 161010118 0.972 rs10455872(G)/rs55730499(T) Response to statin therapy 1e-09 1.72e-09 [0.20-0.36] mmol/L decrease LPA www.ncbi.nlm.nih.gov/pubmed/22368281
## rs10455872-G 6 161010118 0.972 rs10455872(G)/rs55730499(T) Response to statins (LDL cholesterol change) 7e-44 1.10e-42 [0.044-0.060] unit increase LPA www.ncbi.nlm.nih.gov/pubmed/25350695
## rs10455872-? 6 161010118 0.972 rs10455872(G)/rs55730499(T) Total cholesterol levels 2e-15 4.97e-15 [6.57-10.89] unit increase LPA www.ncbi.nlm.nih.gov/pubmed/31217584
The variant near LPA is also significant in the conventional GWAS, and using BF and posterior effects. It has a quite strong effect in the conventional GWAS (z=-10.25) but only a part of its effect on lifespan is going through the risk factors (Coronary Artery Disease and LDL cholesterol) used to create the prior (moderate prior effect, mu=-1.963). Some part of the observed effect is likely to be explained by some risk factors not included here (Lipoprotein levels, Response to statin therapy), or some direct effects.
variant near RAD52
# look if significant in conventional GWAS / using BF or posterior p-values)
# look at SNPs in a 100kb window around (to assess significance in conventional GWAS / bGWAS results)
dist=100000
Direct_Hits %>% dplyr::slice(2) %>% pull(chrm_UK10K) -> chr_RAD52
Direct_Hits %>% dplyr::slice(2) %>% pull(pos_UK10K) -> pos_RAD52
extract_results_bGWAS(Lifespan_bGWAS, SNPs = "all", results="everything") %>%
filter(chrm_UK10K == chr_RAD52,
pos_UK10K > pos_RAD52 - dist,
pos_UK10K < pos_RAD52 + dist) %>%
mutate(p_obs = 2*pnorm(-abs(z_obs))) %>%
dplyr::select(p_obs, BF_p, p_posterior) -> res_RAD52
# not significant in conventional GWAS
any(res_RAD52$p_obs<5e-8)
[1] FALSE
# not significant in bGWAS
any(res_RAD52$BF_p<5e-8)
[1] FALSE
# not significant using posterior effects
any(res_RAD52$p_posterior<5e-8)
[1] FALSE
# Association with other traits
RAD52 = Direct_Hits[2,]
knitr::kable(extract_results_bGWAS(Lifespan_bGWAS, SNPs = "all", results="everything") %>%
filter(rsid == RAD52$rsid) %>%
mutate(BF = as.character(format(BF, scientific=T, digits=3)),
BF_p = as.character(format(BF_p, scientific=T, digits=3)),
p_direct = as.character(format(p_direct, scientific=T, digits=3)),
p_posterior = as.character(format(p_posterior, scientific=T, digits=3))),
digits=3)
| chrm_UK10K | pos_UK10K | rsid | alt | ref | beta | se | z_obs | mu_prior_estimate | mu_prior_std_error | mu_posterior_estimate | mu_posterior_std_error | z_posterior | mu_direct_estimate | mu_direct_std_error | z_direct | beta_prior_estimate | beta_prior_std_error | beta_posterior_estimate | beta_posterior_std_error | beta_direct_estimate | beta_direct_std_error | CRR | BF | BF_p | p_posterior | p_direct | | ----------: | ---------: | :-------- | :-- | :-- | ------: | ----: | ------: | ------------------: | --------------------: | ----------------------: | ------------------------: | -----------: | -------------------: | ---------------------: | --------: | --------------------: | ----------------------: | ------------------------: | --------------------------: | ---------------------: | -----------------------: | ----: | :------- | :------- | :----------- | :-------- | | 12 | 1052488 | rs7307680 | A | G | -0.026 | 0.005 | -5.286 | 0.91 | 0.534 | -0.466 | 0.471 | -0.989 | -6.196 | 1.134 | -5.465 | 0.005 | 0.003 | -0.002 | 0.002 | -0.031 | 0.006 | 1.172 | 3.38e-01 | 9.73e-01 | 3.23e-01 | 4.64e-08 |
Info_RAD52 = get_associatedTraits(RAD52$rsid, RAD52$chrm_UK10K, RAD52$pos_UK10K,
LD=0.1, distance=100000, P=5e-8,
gwascatdata = my_ebicat37)
# format for nice kable output
Info_RAD52 %>%
mutate(p=as.character(format(p, scientific=T, digits=3)),
adjusted_p = as.character(format(adjusted_p, scientific=T, digits=3))) -> Info_RAD52
##
##
## snp chrm posh19 LD_R2 LD_alleles trait p adjusted_p effect gene url
## ------------- ----- ------- ------ --------------------------- --------------------- ------ ----------- -------------------------- ----- -------------------------------------
## rs12301299-C 12 991710 0.567 rs12301299(T)/rs7307680(A) Blood protein levels 2e-14 8.44e-09 [0.19-0.31] unit increase WNK1 www.ncbi.nlm.nih.gov/pubmed/29875488
The variant near RAD52 is not significant in the conventional GWAS, using BF or posterior effects. It has a quite strong effect in the conventional GWAS (z=-5.28) but does not have an effect on lifespan through any of risk factors used to create the prior (small prior effect in the opposite direction, mu=0.909). The observed effect could be explained by some risk factors not included here (but no strong association reported in GWAS catalog for this region), or some direct effects.
variant near HYKK
# look if significant in conventional GWAS / using BF or posterior p-values)
# look at SNPs in a 100kb window around (to assess significance in conventional GWAS / bGWAS results)
dist=100000
Direct_Hits %>% dplyr::slice(3) %>% pull(chrm_UK10K) -> chr_HYKK
Direct_Hits %>% dplyr::slice(3) %>% pull(pos_UK10K) -> pos_HYKK
extract_results_bGWAS(Lifespan_bGWAS, SNPs = "all", results="everything") %>%
filter(chrm_UK10K == chr_HYKK,
pos_UK10K > pos_HYKK - dist,
pos_UK10K < pos_HYKK + dist) %>%
mutate(p_obs = 2*pnorm(-abs(z_obs))) %>%
dplyr::select(p_obs, BF_p, p_posterior) -> res_HYKK
# significant in conventional GWAS
any(res_HYKK$p_obs<5e-8)
[1] TRUE
# significant in bGWAS
any(res_HYKK$BF_p<5e-8)
[1] TRUE
# not significant using posterior effects
any(res_HYKK$p_posterior<5e-8)
[1] FALSE
# Association with other traits
HYKK = Direct_Hits[3,]
knitr::kable(extract_results_bGWAS(Lifespan_bGWAS, SNPs = "all", results="everything") %>%
filter(rsid == HYKK$rsid) %>%
mutate(BF = as.character(format(BF, scientific=T, digits=3)),
BF_p = as.character(format(BF_p, scientific=T, digits=3)),
p_direct = as.character(format(p_direct, scientific=T, digits=3)),
p_posterior = as.character(format(p_posterior, scientific=T, digits=3))),
digits=3)
| chrm_UK10K | pos_UK10K | rsid | alt | ref | beta | se | z_obs | mu_prior_estimate | mu_prior_std_error | mu_posterior_estimate | mu_posterior_std_error | z_posterior | mu_direct_estimate | mu_direct_std_error | z_direct | beta_prior_estimate | beta_prior_std_error | beta_posterior_estimate | beta_posterior_std_error | beta_direct_estimate | beta_direct_std_error | CRR | BF | BF_p | p_posterior | p_direct | | ----------: | ---------: | :-------- | :-- | :-- | ----: | ----: | -----: | ------------------: | --------------------: | ----------------------: | ------------------------: | -----------: | -------------------: | ---------------------: | --------: | --------------------: | ----------------------: | ------------------------: | --------------------------: | ---------------------: | -----------------------: | ----: | :------- | :------ | :----------- | :-------- | | 15 | 78817929 | rs8042849 | T | C | 0.044 | 0.004 | 10.659 | 0.265 | 0.499 | 2.339 | 0.447 | 5.236 | 10.395 | 1.118 | 9.3 | 0.001 | 0.002 | 0.01 | 0.002 | 0.043 | 0.005 | 0.975 | 6.99e+05 | 9.9e-13 | 1.64e-07 | 1.41e-20 |
Info_HYKK = get_associatedTraits(HYKK$rsid, HYKK$chrm_UK10K, HYKK$pos_UK10K,
LD=0.1, distance=100000, P=5e-8,
gwascatdata = my_ebicat37)
# format for nice kable output
Info_HYKK %>%
mutate(p=as.character(format(p, scientific=T, digits=3)),
adjusted_p = as.character(format(adjusted_p, scientific=T, digits=3))) -> Info_HYKK
##
##
## snp chrm posh19 LD_R2 LD_alleles trait p adjusted_p effect gene url
## -------------- ----- --------- ------ ---------------------------- ----------------------------------------------------------------------- ------- ----------- ---------------------------------------- ------------------- -------------------------------------
## rs8042849-C 15 78817929 NA NA Parental lifespan 4e-14 NA years decrease HYKK www.ncbi.nlm.nih.gov/pubmed/29030599
## rs8042849-T 15 78817929 NA NA Post bronchodilator FEV1 4e-15 NA NR unit increase HYKK www.ncbi.nlm.nih.gov/pubmed/26634245
## rs8042849-T 15 78817929 NA NA Post bronchodilator FEV1/FVC ratio 3e-15 NA unit increase HYKK www.ncbi.nlm.nih.gov/pubmed/26634245
## rs8031948-T 15 78816057 0.935 rs8031948(T)/rs8042849(C) Airflow obstruction 3e-09 9.74e-09 HYKK www.ncbi.nlm.nih.gov/pubmed/22837378
## rs17486278-C 15 78867482 0.829 rs17486278(C)/rs8042849(C) Chronic obstructive pulmonary disease 2e-28 7.66e-24 [1.15,1.22] CHRNA5 www.ncbi.nlm.nih.gov/pubmed/28166215
## rs12914385-T 15 78898723 0.696 rs12914385(T)/rs8042849(C) Chronic obstructive pulmonary disease (moderate to severe) 6e-14 3.74e-10 [1.20-1.36] CHRNA3 www.ncbi.nlm.nih.gov/pubmed/24621683
## rs12914385-T 15 78898723 0.696 rs12914385(T)/rs8042849(C) Chronic obstructive pulmonary disease (severe) 3e-16 9.12e-12 [1.29-1.51] CHRNA3 www.ncbi.nlm.nih.gov/pubmed/24621683
## rs8192482-T 15 78886198 0.829 rs8192482(T)/rs8042849(C) Current cigarettes per day in chronic obstructive pulmonary disease 2e-15 4.83e-13 [NR] unit increase CHRNA5, CHRNA3 www.ncbi.nlm.nih.gov/pubmed/29767774
## rs112878080-G 15 78900647 0.813 rs141518190(G)/rs8042849(C) Diffusing capacity of carbon monoxide 3e-10 1.35e-08 0.09-0.17 unit decrease CHRNA3 www.ncbi.nlm.nih.gov/pubmed/30694715
## rs12914385-T 15 78898723 0.696 rs12914385(T)/rs8042849(C) Emphysema distribution in smoking 2e-17 1.37e-12 NR unit increase CHRNA3 www.ncbi.nlm.nih.gov/pubmed/27669027
## rs16969968-G 15 78882925 0.829 rs16969968(G)/rs8042849(T) Fagerstr**m test for nicotine dependence 4e-28 1.36e-23 [0.19-0.27] unit decrease CHRNA5, AC027228.2 www.ncbi.nlm.nih.gov/pubmed/31294817
## rs8040868-C 15 78911181 0.678 rs8040868(T)/rs8042849(T) Familial lung cancer 4e-13 2.28e-09 [1.23-1.43] CHRNA3 www.ncbi.nlm.nih.gov/pubmed/29924316
## rs9788721-T 15 78802869 0.979 rs9788721(T)/rs8042849(T) Local histogram emphysema pattern 2e-13 3.54e-13 [0.0050-0.0090] unit decrease HYKK www.ncbi.nlm.nih.gov/pubmed/25006744
## rs55781567-G 15 78857986 0.883 rs55781567(G)/rs8042849(C) Lung adenocarcinoma 3e-48 8.51e-43 [1.235221079-1.318857475] CHRNA5 www.ncbi.nlm.nih.gov/pubmed/28604730
## rs55781567-G 15 78857986 0.883 rs55781567(G)/rs8042849(C) Lung cancer 3e-103 2.30e-91 [1.267092551-1.328476032] CHRNA5 www.ncbi.nlm.nih.gov/pubmed/28604730
## rs55781567-G 15 78857986 0.883 rs55781567(G)/rs8042849(C) Lung cancer in ever smokers 2e-78 1.91e-69 [1.292694434-1.372529125] CHRNA5 www.ncbi.nlm.nih.gov/pubmed/28604730
## rs10519203-G 15 78814046 0.951 rs10519203(G)/rs8042849(T) Mortality 2e-17 1.20e-16 HYKK www.ncbi.nlm.nih.gov/pubmed/27029810
## rs1051730-T 15 78894339 0.817 rs1051730(G)/rs8042849(T) Nicotine dependence 6e-20 1.39e-16 [0.08-0.12] cigarettes per day increase CHRNA3 www.ncbi.nlm.nih.gov/pubmed/18385739
## rs8034191-? 15 78806023 0.919 rs8034191(T)/rs8042849(T) Pre bronchodilator FEV1 3e-09 1.31e-08 unit decrease HYKK www.ncbi.nlm.nih.gov/pubmed/26634245
## rs8034191-? 15 78806023 0.919 rs8034191(T)/rs8042849(T) Pre bronchodilator FEV1/FVC ratio 2e-10 1.08e-09 unit decrease HYKK www.ncbi.nlm.nih.gov/pubmed/26634245
## rs17486278-C 15 78867482 0.829 rs17486278(C)/rs8042849(C) Pulmonary artery enlargement and chronic obstructive pulmonary disease 7e-10 1.98e-08 [1.27-1.59] CHRNA5 www.ncbi.nlm.nih.gov/pubmed/25101718
## rs55853698-G 15 78857939 0.887 rs55853698(T)/rs8042849(T) Small cell lung carcinoma 5e-21 7.92e-19 [1.256072968-1.416074239] CHRNA5 www.ncbi.nlm.nih.gov/pubmed/28604730
## rs1051730-G 15 78894339 0.817 rs1051730(G)/rs8042849(T) Smoking behavior 3e-73 3.44e-60 [0.91-1.13] CPD decrease CHRNA3 www.ncbi.nlm.nih.gov/pubmed/20418890
## rs8040868-C 15 78911181 0.678 rs8040868(T)/rs8042849(T) Squamous cell lung carcinoma 3e-41 1.56e-28 [1.243425108-1.339233734] CHRNA3 www.ncbi.nlm.nih.gov/pubmed/28604730
## rs16969968-G 15 78882925 0.829 rs16969968(G)/rs8042849(T) Time to smoke first cigarette in the morning 1e-17 5.90e-15 [0.05-0.09] unit decrease CHRNA5, AC027228.2 www.ncbi.nlm.nih.gov/pubmed/31294817
The variant near HYKK is significant in the conventional GWAS and using BF but not posterior effects. It has a quite strong effect in the conventional GWAS (z=10.65) but does not have an effect on lifespan through any of risk factors used to create the prior (small prior effect in the same direction, mu=0.499). The strength of the observed effect is enough to make the BF significant, even if the prior is not very large. The observed effect is likely to be explained by some risk factors not included here (smoking, pulmonary diseases/cancers), or some direct effects.
variant near APOE
# look if significant in conventional GWAS / using BF or posterior p-values)
# look at SNPs in a 100kb window around (to assess significance in conventional GWAS / bGWAS results)
dist=100000
Direct_Hits %>% dplyr::slice(4) %>% pull(chrm_UK10K) -> chr_APOE
Direct_Hits %>% dplyr::slice(4) %>% pull(pos_UK10K) -> pos_APOE
extract_results_bGWAS(Lifespan_bGWAS, SNPs = "all", results="everything") %>%
filter(chrm_UK10K == chr_APOE,
pos_UK10K > pos_APOE - dist,
pos_UK10K < pos_APOE + dist) %>%
mutate(p_obs = 2*pnorm(-abs(z_obs))) %>%
dplyr::select(p_obs, BF_p, p_posterior) -> res_APOE
# significant in conventional GWAS
any(res_APOE$p_obs<5e-8)
[1] TRUE
# significant in bGWAS
any(res_APOE$BF_p<5e-8)
[1] TRUE
# significant using posterior effects
any(res_APOE$p_posterior<5e-8)
[1] TRUE
# Association with other traits
APOE = Direct_Hits[4,]
knitr::kable(extract_results_bGWAS(Lifespan_bGWAS, SNPs = "all", results="everything") %>%
filter(rsid == APOE$rsid) %>%
mutate(BF = as.character(format(BF, scientific=T, digits=3)),
BF_p = as.character(format(BF_p, scientific=T, digits=3)),
p_direct = as.character(format(p_direct, scientific=T, digits=3)),
p_posterior = as.character(format(p_posterior, scientific=T, digits=3))),
digits=3)
| chrm_UK10K | pos_UK10K | rsid | alt | ref | beta | se | z_obs | mu_prior_estimate | mu_prior_std_error | mu_posterior_estimate | mu_posterior_std_error | z_posterior | mu_direct_estimate | mu_direct_std_error | z_direct | beta_prior_estimate | beta_prior_std_error | beta_posterior_estimate | beta_posterior_std_error | beta_direct_estimate | beta_direct_std_error | CRR | BF | BF_p | p_posterior | p_direct | | ----------: | ---------: | :------- | :-- | :-- | ----: | ----: | -----: | ------------------: | --------------------: | ----------------------: | ------------------------: | -----------: | -------------------: | ---------------------: | --------: | --------------------: | ----------------------: | ------------------------: | --------------------------: | ---------------------: | -----------------------: | ----: | :------- | :------- | :----------- | :-------- | | 19 | 45411941 | rs429358 | T | C | 0.106 | 0.005 | 19.328 | 1.854 | 0.712 | 7.736 | 0.58 | 13.334 | 17.473 | 1.228 | 14.232 | 0.01 | 0.004 | 0.042 | 0.003 | 0.095 | 0.007 | 0.904 | 1.11e+37 | 7.06e-70 | 1.47e-40 | 5.79e-46 |
Info_APOE = get_associatedTraits(APOE$rsid, APOE$chrm_UK10K, APOE$pos_UK10K,
LD=0.1, distance=100000, P=5e-8,
gwascatdata = my_ebicat37)
# format for nice kable output
Info_APOE %>%
mutate(p=as.character(format(p, scientific=T, digits=3)),
adjusted_p = as.character(format(adjusted_p, scientific=T, digits=3))) -> Info_APOE
##
##
## snp chrm posh19 LD_R2 LD_alleles trait p adjusted_p effect gene url
## ------------- ----- --------- ------ -------------------------- -------------------------------------------------------------------- ------- ----------- -------------------------------------------- -------------------- -------------------------------------
## rs429358-? 19 45411941 NA NA Advanced age-related macular degeneration 2e-42 NA APOE www.ncbi.nlm.nih.gov/pubmed/26691988
## rs429358-? 19 45411941 NA NA Alzheimer's disease 2e-91 NA APOE www.ncbi.nlm.nih.gov/pubmed/30636644
## rs429358-C 19 45411941 NA NA Alzheimer's disease biomarkers 5e-14 NA APOE www.ncbi.nlm.nih.gov/pubmed/23419831
## rs429358-? 19 45411941 NA NA Alzheimer's disease progression score 4e-33 NA APOE www.ncbi.nlm.nih.gov/pubmed/29860282
## rs429358-C 19 45411941 NA NA Blood protein levels 0e+00 NA [1.3-1.38] unit decrease APOE www.ncbi.nlm.nih.gov/pubmed/29875488
## rs429358-? 19 45411941 NA NA Brain imaging 1e-09 NA APOE www.ncbi.nlm.nih.gov/pubmed/20100581
## rs429358-T 19 45411941 NA NA Cerebral amyloid deposition (PET imaging) 8e-32 NA [NR] unit decrease APOE www.ncbi.nlm.nih.gov/pubmed/26252872
## rs429358-C 19 45411941 NA NA Cerebral amyloid deposition positivity (PET imaging) 5e-20 NA [4.39-5.05] APOE www.ncbi.nlm.nih.gov/pubmed/26252872
## rs429358-? 19 45411941 NA NA Cerebrospinal AB1-42 levels in Alzheimer's disease dementia 4e-17 NA [0.30-0.50] unit decrease APOE www.ncbi.nlm.nih.gov/pubmed/25027320
## rs429358-? 19 45411941 NA NA Cognitive decline (age-related) 2e-14 NA unit decrease APOE www.ncbi.nlm.nih.gov/pubmed/28078323
## rs429358-? 19 45411941 NA NA Cortical amyloid beta load 3e-50 NA APOE www.ncbi.nlm.nih.gov/pubmed/29860282
## rs429358-C 19 45411941 NA NA Dementia with Lewy bodies 3e-64 NA [2.22-2.74] APOE www.ncbi.nlm.nih.gov/pubmed/29263008
## rs429358-T 19 45411941 NA NA HDL cholesterol 1e-14 NA [0.048-0.084] s.d. increase APOE www.ncbi.nlm.nih.gov/pubmed/25961943
## rs429358-? 19 45411941 NA NA Hippocampal volume 1e-10 NA APOE www.ncbi.nlm.nih.gov/pubmed/29860282
## rs429358-C 19 45411941 NA NA Lewy body disease 1e-12 NA [0.36-0.64] unit decrease APOE www.ncbi.nlm.nih.gov/pubmed/25188341
## rs429358-T 19 45411941 NA NA Longevity (age >90th survival percentile) 1e-61 NA [1.52-1.82] APOE www.ncbi.nlm.nih.gov/pubmed/31413261
## rs429358-T 19 45411941 NA NA Longevity (age >99th survival percentile) 1e-36 NA [1.64-2] APOE www.ncbi.nlm.nih.gov/pubmed/31413261
## rs429358-T 19 45411941 NA NA Moderate to vigorous physical activity levels 6e-13 NA unit decrease APOE www.ncbi.nlm.nih.gov/pubmed/29899525
## rs429358-C 19 45411941 NA NA Mortality 1e-20 NA APOE www.ncbi.nlm.nih.gov/pubmed/27029810
## rs429358-C 19 45411941 NA NA Neuritic plaque 9e-25 NA [1-1.47] unit increase APOE www.ncbi.nlm.nih.gov/pubmed/31497858
## rs429358-? 19 45411941 NA NA Neuritic plaques or cerebral amyloid angiopathy (pleiotropy) 5e-40 NA APOE www.ncbi.nlm.nih.gov/pubmed/29458411
## rs429358-? 19 45411941 NA NA Neuritic plaques or neurofibrillary tangles (pleiotropy) 3e-47 NA APOE www.ncbi.nlm.nih.gov/pubmed/29458411
## rs429358-C 19 45411941 NA NA Neurofibrillary tangles 3e-31 NA [1.25-1.75] unit increase APOE www.ncbi.nlm.nih.gov/pubmed/31497858
## rs429358-? 19 45411941 NA NA Neurofibrillary tangles or cerebral amyloid angiopathy (pleiotropy) 6e-35 NA APOE www.ncbi.nlm.nih.gov/pubmed/29458411
## rs429358-C 19 45411941 NA NA Parental lifespan 1e-27 NA years decrease APOE www.ncbi.nlm.nih.gov/pubmed/29030599
## rs4420638-? 19 45422946 0.686 rs4420638(G)/rs429358(T) Age-related disease endophenotypes 2e-25 6.10e-18 APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/27790247
## rs4420638-? 19 45422946 0.686 rs4420638(G)/rs429358(T) Age-related diseases, mortality and associated endophenotypes 1e-22 4.41e-16 APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/27790247
## rs4420638-A 19 45422946 0.686 rs4420638(G)/rs429358(T) Age-related macular degeneration 2e-20 1.70e-14 [1.24-1.36] APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/23455636
## rs56131196-A 19 45422846 0.686 rs56131196(G)/rs429358(T) Alzheimer disease and age of onset 3e-24 3.94e-17 unit increase APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/26830138
## rs4420638-? 19 45422946 0.686 rs4420638(G)/rs429358(T) Alzheimer's disease (age of onset) 1e-12 3.51e-09 unit increase APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/22005931
## rs4420638-? 19 45422946 0.686 rs4420638(G)/rs429358(T) Alzheimer's disease (late onset) 1e-39 8.78e-28 [NR] APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/17474819
## rs1081105-C 19 45412955 0.173 rs1081105(C)/rs429358(C) Alzheimer's disease or family history of Alzheimer's disease 1e-231 1.09e-41 NR z-unit increase AC011481.4 www.ncbi.nlm.nih.gov/pubmed/30617256
## rs77301115-? 19 45396973 0.171 rs77301115(G)/rs429358(T) Alzheimer's disease or HDL levels (pleiotropy) 7e-74 5.80e-14 AC011481.1 www.ncbi.nlm.nih.gov/pubmed/30805717
## rs4420638-G 19 45422946 0.686 rs4420638(G)/rs429358(T) C-reactive protein 5e-27 4.81e-19 [18.1-25.3] % decrease APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/19567438
## rs4420638-A 19 45422946 0.686 rs4420638(G)/rs429358(T) C-reactive protein levels 9e-139 8.16e-96 [0.22-0.26] unit increase APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/21300955
## rs4420638-A 19 45422946 0.686 rs4420638(G)/rs429358(T) C-reactive protein levels or HDL-cholesterol levels (pleiotropy) 2e-164 2.00e-113 APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/27286809
## rs4420638-A 19 45422946 0.686 rs4420638(G)/rs429358(T) C-reactive protein levels or LDL-cholesterol levels (pleiotropy) 1e-283 2.66e-195 APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/27286809
## rs4420638-A 19 45422946 0.686 rs4420638(G)/rs429358(T) C-reactive protein levels or total cholesterol levels (pleiotropy) 4e-249 1.48e-171 APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/27286809
## rs4420638-A 19 45422946 0.686 rs4420638(G)/rs429358(T) C-reactive protein levels or triglyceride levels (pleiotropy) 2e-171 3.14e-118 APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/27286809
## rs6857-T 19 45392254 0.690 rs6857(T)/rs429358(C) Cerebral amyloid angiopathy 3e-21 3.87e-15 [0.53-0.81] unit increase AC011481.2, NECTIN2 www.ncbi.nlm.nih.gov/pubmed/25188341
## rs769449-A 19 45410002 0.766 rs769449(G)/rs429358(T) Cerebrospinal fluid AB1-42 levels 5e-94 1.81e-72 [0.091-0.111] unit decrease APOE www.ncbi.nlm.nih.gov/pubmed/28247064
## rs4420638-? 19 45422946 0.686 rs4420638(G)/rs429358(T) Cerebrospinal fluid p-Tau181p:AB1-42 ratio 3e-27 3.38e-19 [NR] unit decrease APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/30319691
## rs4420638-? 19 45422946 0.686 rs4420638(G)/rs429358(T) Cerebrospinal fluid t-tau levels 2e-13 1.15e-09 APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/28641921
## rs769449-? 19 45410002 0.766 rs769449(G)/rs429358(T) Cerebrospinal fluid t-tau:AB1-42 ratio 4e-26 2.18e-20 [NR] unit decrease APOE www.ncbi.nlm.nih.gov/pubmed/30319691
## rs769449-A 19 45410002 0.766 rs769449(G)/rs429358(T) Cerebrospinal P-tau181p levels 5e-33 1.09e-25 [0.067-0.091] unit increase APOE www.ncbi.nlm.nih.gov/pubmed/28247064
## rs769449-A 19 45410002 0.766 rs769449(G)/rs429358(T) Cerebrospinal T-tau levels 4e-29 1.08e-22 [0.064-0.092] unit increase APOE www.ncbi.nlm.nih.gov/pubmed/28247064
## rs4420638-G 19 45422946 0.686 rs4420638(G)/rs429358(T) Cholesterol, total 1e-149 2.47e-103 [NR] unit increase APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/24097068
## rs4420638-? 19 45422946 0.686 rs4420638(G)/rs429358(T) Cingulate cortical amyloid beta load 5e-21 6.53e-15 APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/26421299
## rs4420638-? 19 45422946 0.686 rs4420638(G)/rs429358(T) Cognitive decline 4e-27 4.12e-19 APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/22054870
## rs6857-C 19 45392254 0.690 rs6857(T)/rs429358(C) Cognitive impairment 4e-12 8.33e-09 [1.38-2.53] AC011481.2, NECTIN2 www.ncbi.nlm.nih.gov/pubmed/31201950
## rs769449-? 19 45410002 0.766 rs769449(G)/rs429358(T) Cognitive impairment test score 2e-14 2.14e-11 [NR] unit decrease APOE www.ncbi.nlm.nih.gov/pubmed/30319691
## rs6857-T 19 45392254 0.690 rs6857(T)/rs429358(C) Dementia and core Alzheimer's disease neuropathologic changes 2e-62 1.29e-43 [1.42-1.8] unit increase AC011481.2, NECTIN2 www.ncbi.nlm.nih.gov/pubmed/25188341
## rs75627662-T 19 45413576 0.450 rs75627662(T)/rs429358(C) Family history of Alzheimer's disease 1e-295 3.69e-134 NR z-score increase AC011481.4 www.ncbi.nlm.nih.gov/pubmed/30617256
## rs4420638-A 19 45422946 0.686 rs4420638(G)/rs429358(T) HDL cholesterol levels 6e-34 8.28e-24 [0.054-0.08] unit increase (EA Beta values) APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/28334899
## rs4420638-G 19 45422946 0.686 rs4420638(G)/rs429358(T) LDL cholesterol 2e-178 4.93e-123 [NR] unit increase APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/24097068
## rs4420638-A 19 45422946 0.686 rs4420638(G)/rs429358(T) LDL cholesterol levels 2e-82 3.99e-57 [4.94-6.06] unit decrease APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/30926973
## rs4420638-G 19 45422946 0.686 rs4420638(G)/rs429358(T) Lipid traits 1e-14 1.45e-10 unit increase APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/24023261
## rs4420638-A 19 45422946 0.686 rs4420638(G)/rs429358(T) Lipoprotein-associated phospholipase A2 activity and mass 5e-30 4.13e-21 [0.044-0.064] unit decrease APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/22003152
## rs4420638-? 19 45422946 0.686 rs4420638(G)/rs429358(T) Longevity 2e-16 9.76e-12 APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/21740922
## rs4420638-? 19 45422946 0.686 rs4420638(G)/rs429358(T) Longevity (85 years and older) 2e-26 1.25e-18 [1.18-1.27] APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/24688116
## rs4420638-? 19 45422946 0.686 rs4420638(G)/rs429358(T) Longevity (90 years and older) 3e-36 2.16e-25 [1.32-1.47] APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/24688116
## rs769449-? 19 45410002 0.766 rs769449(G)/rs429358(T) Low density lipoprotein cholesterol levels 3e-22 2.07e-17 [4.86-7.31] unit increase APOE www.ncbi.nlm.nih.gov/pubmed/31217584
## rs157582-? 19 45396219 0.524 rs157582(T)/rs429358(T) Psychosis and Alzheimer's disease 9e-52 6.12e-28 [NR] TOMM40 www.ncbi.nlm.nih.gov/pubmed/22005930
## rs4420638-G 19 45422946 0.686 rs4420638(G)/rs429358(T) Red cell distribution width 2e-25 6.10e-18 [0.041-0.061] unit decrease APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/28957414
## rs769449-? 19 45410002 0.766 rs769449(G)/rs429358(T) Total cholesterol levels 5e-19 6.18e-15 [4.88-7.63] unit increase APOE www.ncbi.nlm.nih.gov/pubmed/31217584
## rs438811-T 19 45416741 0.634 rs438811(T)/rs429358(T) Triglycerides 9e-37 6.16e-24 [0.08-0.112] s.d. increase AC011481.3 www.ncbi.nlm.nih.gov/pubmed/25961943
## rs4420638-G 19 45422946 0.686 rs4420638(G)/rs429358(T) Verbal declarative memory 1e-16 6.05e-12 APOC1 - APOC1P1 www.ncbi.nlm.nih.gov/pubmed/25648963
## rs769449-A 19 45410002 0.766 rs769449(G)/rs429358(T) Verbal memory performance (residualized delayed recall level) 3e-12 1.02e-09 unit decrease APOE www.ncbi.nlm.nih.gov/pubmed/28800603
The variant near APOE is significant in the conventional GWAS and using BF but not posterior effects. It has a very strong effect in the conventional GWAS (z=19.32) but only a part of its effect on lifespan is going through the risk factors used to create the prior (moderate prior effect, mu=1.854). Some part of the observed effect is likely to be explained by some risk factors not included here (Alzheimer, dementia, cognitive decline, C-reactive protein…), or some direct effects.
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