In ugcd/solarius: An R Interface to SOLAR

#opts_chunk$set(eval = TRUE, dev = "png")
#opts_chunk$set(fig.path = "figures-modelsGAIT1/")

library(devtools)
load_all("~/git/ugcd/solarius")
#library(solarius)

library(gait)
library(plyr)

load("~/Results/packages/solarius/GAIT1/gait1.bmi.A.RData")
load("~/Results/packages/solarius/GAIT1/resultsF11HH.Rdata")
load("~/Results/packages/solarius/GAIT1/gait1.L.univar.RData")

load("~/Results/packages/solarius/GAIT1/gait1.L.twopass.RData")
load("~/Results/packages/solarius/GAIT1/gait1.L12.bivar.RData")

A.F11 <- A
L.F11 <- L3

A.bmi <- A2
L.bmi <- L1
L.bmi.twopass <- L1.2

A.aff <- A3
L.aff <- L2

L.aff.bmi <- L12

rm(list = c("A", "A1", "A2", "A3", "L1", "L2", "L3", "L12", "L1.2"))

About

These pages show particular examples to illustrate the 'solarius' package's behaviour with large datasets. In particular, these examples were generated with the GAIT (Genetic Analysis of Idiopathic Thrombophilia) dataset. The GAIT Project included 397 individuals from 21 extended Spanish families (mean pedigree size = 19) (@Souto2000). A genome-wide set of 307,984 SNPs was typed in all of the participants using the Infinium 317 k Beadchip on the Illumina platform (San Diego, CA, USA).

We selected 3 specific examples where we could compare the results obtained with the 'solarius' package with those previously obtained and published in Sabater2012 and Souto2014. The 3 selected phenotypes were the FXI levels in blood, the BMI and the Thrombosis affection.

Load packages

library(solarius)
library(gait)

Load GAIT1 data

We first load our data and properly transform the phenotypes under study.

pdat  <- gait1.phen()
pdat <- mutate(pdat, 
  tr_FXI = FXI_T * 5.1,
  ln_bmi = log(bmi),  
  tr_bmi = 6.1 * ln_bmi)

gait1.snpfiles <- gait1.snpfiles()

mibddir <- gait1.mibddir()

cores <- 64

FXI

We first applied the main models of association and linkage of the package to the FXI levels in blood. The FXI phenotype has already been studied in the same dataset as described in @Sabater2012. This example aims to illustrate the proper behaviour of the solarius package by replicating these former results.

The polygenic model that estimates the heritability of the FXI levels in blood is described by M.F11.

# trait previously transformed, only significant covariates
M.F11 <- solarPolygenic(tr_FXI ~ AGE, pdat, covtest=T)
M.F11

The model of association is described by A.F11

A.F11 <- solarAssoc(tr_FXI ~ AGE, pdat, 
  genocov.files = gait1.snpfiles$genocov.files, 
  snplists.files = gait1.snpfiles$snplists.files, 
  snpmap.files = gait1.snpfiles$snpmap.files, 
  cores = cores)

summary(A.F11)

plot(A.F11)

plot(A.F11, "qq")

We observe that 3 significant SNPs are found. These results are in concordance with those previously reported on the FXI phenotype of the GAIT1 project (@Sabater2012). There are three significant loci: rs710446 and rs4253399 located in the structural F11 gene, and rs4241824, located in the kininogen 1 (KNG1) gene. Both rs710446 and rs4241824 were reported in our previous GWAS published in (@Sabater2012).

L.F11 <- solarMultipoint(tr_FXI ~ AGE, data = pdat, 
  mibdir = mibdir, 
  chr = 1:22, interval = 5, 
  cores = cores, verbose = 1)

The linkage model is described by L.F11.

summary(L.F11)

plot(L.F11)

We observe that no significant loci are found using the linkage model.

BMI

The second example consists on applying the same models to the Body Mass Index (BMI). In this case, we also have a reference publication to compare with (@Souto2014). In contrast with the previous example, in @Souto2014, only linkage signals showed significant peaks for the BMI phenotype.

M.bmi estimates the BMI heritability

# trait previously transformed, only significant covariates
M.bmi <- solarPolygenic(tr_bmi ~ AGE, pdat, covtest = TRUE)
M.bmi

The model of asscociation between GAIT SNPs and the BMI phenotype is described by A.bmi.

A.bmi <- solarAssoc(tr_bmi ~ AGE, pdat, 
  genocov.files = gait1.snpfiles$genocov.files, 
  snplists.files = gait1.snpfiles$snplists.files, 
  snpmap.files = gait1.snpfiles$snpmap.files, 
  cores = cores)

summary(A.bmi)

plot(A.bmi)

plot(A.bmi, "qq")

As expected we do not detect any significantly associated SNPs.

The model of linkage for the BMI phenotype is described by L.bmi.

L.bmi <- solarMultipoint(formula = tr_bmi ~ AGE, data = dat, 
  mibddir = mibddir, 
  chr = 1:22, interval = 5, cores = cores, verbose = 1)

summary(L.bmi)

plot(L.bmi)

We obtain a significant peak of linkage at chromosome 13. This replicates the result reported in @Souto2014 with a linkage multiploint analysis of the bmi in the GAIT1.

Two-pass linkage

In order to evaluate the impact of this finding in related loci, we applied a second univariate linkage analysis, conditioned on the linkage signal obtained at 13q34 locus, as in @Souto2014.

# parallel computation of multi-pass linkage is not implemented in `solarius` yet
L.bmi.twopass <- solarMultipoint(tr_bmi ~ AGE, pdat, 
  mibddir = mibddir, 
  interval = 5, multipoint.options = "3")

plot(L.bmi.twopass, pass = 2)

We observe that in this second linkage analysis, conditioned on the former significant LOD score, the signal on chromosome 13q34 dropped dramatically to 0, as expected.

Throm

In order to illustrate the behaviour of the described models with dichotomous phenotypes, we finally applied them to the Thrombosis affection status.

The heritability of Thrombosis is estimated with M.aff.

M.aff <- solarPolygenic(aff ~ AGE, pdat, covtest = TRUE)
M.aff

The model of association is described by A.aff. ``` {r assoc3, eval = FALSE} A.aff <- solarAssoc(aff ~ AGE, pdat, genocov.files = gait1.snpfiles$genocov.files, snplists.files = gait1.snpfiles$snplists.files, snpmap.files = gait1.snpfiles$snpmap.files, cores = cores)

```r
plot(A.aff)

plot(A.aff, "qq")

The model of linkage is described by L.aff.

L.aff <- solarMultipoint(aff ~ AGE, data = dat, 
  mibdir = mibdir, 
  chr = 1:22, interval = 5, 
  cores = cores, verbose = 1)

summary(L.aff)

plot(L.aff)

We observe that no significant findings are obtained neither in association nor in linkage analyses.

Bivariate analysis of BMI and Throm

We applied the bivariate linkage analysis with BMI and thrombosis affection, under the hypothesis of pleiotropy between BMI and liability to thrombosis.

L.aff.bmi <- solarMultipoint(aff + bmi ~ AGE, pdat, 
  mibddir = mibddir, 
  chr = 1:22, interval = 5, 
  cores = cores, verbose = 1)

summary(L.aff.bmi)

plot(L.aff.bmi)

We observe, again, a significant peak of linkage at the 13q34 locus. This supports the hyposthesis proposed in @Souto2014 of combined linkage between that region and BMI/thrombosis risk.