*LDlinkR*: An R Package for Rapidly Calculating Linkage Disequilibrium Statistics in Diverse Populations

knitr::opts_chunk$set(echo = TRUE)
library("LDlinkR")

Description

LDlink is an interactive and powerful suite of web-based tools for querying germline variants in human population groups of interest to generate interactive tables and plots. All population genotype data originates from Phase 3 (Version 5) of the 1000 Genomes Project and variant RS numbers are indexed based on dbSNP 151.

LDlinkR is an R package developed to query and download results (internet access required) generated by LDlink web-based applications from the R console. LDlinkR accelerates genomic research by providing efficient and user-friendly functions to programmatically interrogate pairwise linkage disequilibrium from large lists of genetic variants.

Please see the online LDlink documentation for more information about understanding linkage disequilibrium (LD) and additional details about how LDlink calculates patterns of LD across a variety of ancestral human populations.

Installation

install.packages("LDlinkR")
install.packages("remotes")
remotes::install_github("CBIIT/LDlinkR")

LDlinkR depends on the following packages:

Following installation, attach the LDlinkR package with:

library(LDlinkR)

Personal Access Token - Required

In order to access the LDlink API via LDlinkR, we use a personal access token. This is a common convention followed by many APIs and emulates the more familiar HTTPS username/password or SSH keys.

You will need to:

LDhap(snps = c("rs3", "rs4", "rs148890987"), 
      pop = "YRI", 
      token = "YourTokenHere123")

Optional:
However, the best security practice is to store your personal access token as an environment variable where LDlinkR can find it and use it on your behalf but where it will not be accidentally shared with the public. Note: Modifying R startup files (such as the .Renviron) is for the advanced R user only. Modification of these files in the wrong way could cause problems. Please proceed cautiously. Step-by-step instructions follow:

After retrieving your personal access token from your email, put your token in your .Renviron file. .Renviron is a hidden file that lives in your home directory. The easiest way to both find and edit the .Renviron file is with a function from the usethis package. From the R console, do:

usethis::edit_r_environ()

Your .Renviron file should open in your editor. Add a line that looks like this:

LDLINK_TOKEN=YourTokenHere123

Important, ensure you put a line break at the end by hitting the enter/return key.

Save and close the .Renviron file. Restart R, as environment variables are only loaded from .Renviron at the start of a new R session. Now, check to see that your token is available by entering:

Sys.getenv("LDLINK_TOKEN")
## [1] "YourTokenHere123"

You should see your personal access token print to the screen, as shown above. Now, LDlinkR function calls that use

Sys.getenv("LDLINK_TOKEN")

for the token argument in LDlinkR function calls will use your personal access token in a private and secure way. This method will be used in the extended examples that follow.


Functions and Examples

LDexpress

Function

LDexpress(snps, 
          pop = "CEU", 
          tissue = "ALL", 
          r2d = "r2", 
          r2d_threshold = 0.1, 
          p_threshold = 0.1, 
          win_size = 500000, 
          token = NULL, 
          file = FALSE
         )

Search if a list of genomic variants (or variants in LD with those variants) is associated with gene expression in tissues of interest. Quantitative trait loci data is downloaded from the GTEx Portal.

Arguments

Usage: Single query variant, multiple populations, multiple tissue types using tissue abbreviation

my_output <- LDexpress(snps = "rs4",
                       pop = c("YRI", "CEU"),
                       tissue =  c("ADI_SUB", "ADI_VIS_OME"),
                       win_size = "500000",
                       token = Sys.getenv("LDLINK_TOKEN")
                      )

In the above example, output is a data frame stored in the variable my_output. See below.

head(my_output)
##   Query      RS_ID       Position                R2                D'
## 1   rs4 rs10637519 chr13:32430479 0.174249321651574 0.965976331360947
## 2   rs4 rs10637519 chr13:32430479 0.174249321651574 0.965976331360947
## 3   rs4   rs473641 chr13:32431244 0.174249321651574 0.965976331360947
## 4   rs4   rs473641 chr13:32431244 0.174249321651574 0.965976331360947
## 5   rs4   rs671746 chr13:32431263 0.174249321651574 0.965976331360947
## 6   rs4   rs671746 chr13:32431263 0.174249321651574 0.965976331360947
##    Gene_Symbol        Gencode_ID                       Tissue
## 1 RP1-257C22.2 ENSG00000279314.1       Adipose - Subcutaneous
## 2 RP1-257C22.2 ENSG00000279314.1 Adipose - Visceral (Omentum)
## 3 RP1-257C22.2 ENSG00000279314.1       Adipose - Subcutaneous
## 4 RP1-257C22.2 ENSG00000279314.1 Adipose - Visceral (Omentum)
## 5 RP1-257C22.2 ENSG00000279314.1       Adipose - Subcutaneous
## 6 RP1-257C22.2 ENSG00000279314.1 Adipose - Visceral (Omentum)
##   Non_effect_Allele_Freq Effect_Allele_Freq Effect_Size     P_value
## 1                G=0.565          GTC=0.435    0.225642  2.2578e-07
## 2                G=0.565          GTC=0.435    0.207161  1.0227e-05
## 3                A=0.565            G=0.435    0.225642  2.2578e-07
## 4                A=0.565            G=0.435    0.207161  1.0227e-05
## 5                C=0.565            T=0.435    0.226558 1.93289e-07
## 6                C=0.565            T=0.435    0.207161  1.0227e-05

Usage: Multiple query variants, single population, a tissue type using the full LDexpress tissue name, no spaces.

my_output <- LDexpress(snps = c("rs345", "rs456"),
                       pop = "YRI",
                       tissue =  "Adipose_Visceral_Omentum",
                       token = Sys.getenv("LDLINK_TOKEN")
  )

In the above example, output is a data frame stored in the variable my_output. See below.

head(my_output)
##   Query      RS_ID       Position                R2 D'  Gene_Symbol
## 1 rs345 rs12877069 chr13:32430415 0.222088835534214  1 RP1-257C22.2
## 2 rs345 rs10637519 chr13:32430479  0.10989010989011  1 RP1-257C22.2
## 3 rs345   rs473641 chr13:32431244  0.10989010989011  1 RP1-257C22.2
## 4 rs345   rs671746 chr13:32431263  0.10989010989011  1 RP1-257C22.2
## 5 rs345  rs9315146 chr13:32432193 0.222088835534214  1 RP1-257C22.2
## 6 rs345   rs657190 chr13:32432232 0.107871720116618  1 RP1-257C22.2
##          Gencode_ID                       Tissue Non_effect_Allele_Freq
## 1 ENSG00000279314.1 Adipose - Visceral (Omentum)                C=0.685
## 2 ENSG00000279314.1 Adipose - Visceral (Omentum)                G=0.519
## 3 ENSG00000279314.1 Adipose - Visceral (Omentum)                A=0.519
## 4 ENSG00000279314.1 Adipose - Visceral (Omentum)                C=0.519
## 5 ENSG00000279314.1 Adipose - Visceral (Omentum)                A=0.685
## 6 ENSG00000279314.1 Adipose - Visceral (Omentum)                T=0.514
##   Effect_Allele_Freq Effect_Size     P_value
## 1            T=0.315    0.355769 6.11598e-05
## 2          GTC=0.481    0.207161  1.0227e-05
## 3            G=0.481    0.207161  1.0227e-05
## 4            T=0.481    0.207161  1.0227e-05
## 5            G=0.315    0.276884 2.20517e-08
## 6            C=0.486    0.207916 9.95318e-06
tail(my_output)
##    Query     RS_ID       Position                R2                D'
## 63 rs345 rs5802624 chr13:32499700 0.154247745609872                 1
## 64 rs345 rs2521196 chr13:32514328 0.414333042720139 0.824864864864865
## 65 rs345  rs367507 chr13:32516037 0.414333042720139 0.824864864864865
## 66 rs345  rs203417 chr13:32525482 0.490379008746356 0.828571428571429
## 67 rs345  rs916756 chr13:32527529  0.14623556197823 0.785430463576159
## 68 rs456 rs2529051  chr7:24594306 0.129422301836095 0.586206896551724
##     Gene_Symbol         Gencode_ID                       Tissue
## 63 RP1-257C22.2  ENSG00000279314.1 Adipose - Visceral (Omentum)
## 64 RP1-257C22.2  ENSG00000279314.1 Adipose - Visceral (Omentum)
## 65 RP1-257C22.2  ENSG00000279314.1 Adipose - Visceral (Omentum)
## 66 RP1-257C22.2  ENSG00000279314.1 Adipose - Visceral (Omentum)
## 67 RP1-257C22.2  ENSG00000279314.1 Adipose - Visceral (Omentum)
## 68        DFNA5 ENSG00000105928.13 Adipose - Visceral (Omentum)
##    Non_effect_Allele_Freq Effect_Allele_Freq Effect_Size     P_value
## 63                C=0.398           CT=0.602   -0.252523  2.7687e-07
## 64                G=0.144            C=0.856   -0.418973 3.26939e-05
## 65                C=0.144            A=0.856   -0.418973 3.26939e-05
## 66                T=0.125            C=0.875   -0.413808 4.17407e-05
## 67                A=0.301            G=0.699   -0.460883 5.90627e-16
## 68                A=0.083            G=0.917   -0.134336 1.09618e-05

LDhap

Function

LDhap(snps, pop="CEU", token=NULL, file = FALSE)

Calculates population specific haplotype frequencies of all haplotypes observed for a list of query variants. Input is a list of variant RS numbers (concatenated list) and a population group.

Arguments

Usage: Multiple query variants, single population

LDhap(snps = c("rs3", "rs4", "rs148890987"), 
      pop = "CEU", 
      token = Sys.getenv("LDLINK_TOKEN")
     )
##   rs148890987 rs3 rs4 Count Frequency
## 1           C   C   A   176    0.8889
## 2           T   T   G    11    0.0556
## 3           T   C   A     7    0.0354
## 4           C   T   G     4    0.0202


Usage: Multiple query variants, multiple populations

LDhap(snps = c("rs3", "rs4", "rs148890987"),
      pop = c("YRI", "CEU"),
      token = Sys.getenv("LDLINK_TOKEN")
     )
##   rs148890987 rs3 rs4 Count Frequency
## 1           C   C   A   355    0.8575
## 2           C   T   G    41     0.099
## 3           T   T   G    11    0.0266
## 4           T   C   A     7    0.0169

Output is a table of alleles, haplotype count and haplotype frequencies.


LDmatrix

Function

LDmatrix(snps, pop = "CEU", r2d = "r2", token = NULL, file = FALSE)

Generates a data frame of pairwise linkage disequilibrium statistics. Input is a list of between 2 to 1000 variants. Desired output can be based on estimates of R^2^ or D'.

Arguments

Usage: Multiple query variants, single population, R^2^

LDmatrix(snps = c("rs496202", "rs11147477", "rs201578600"), 
         pop = "YRI", r2d = "r2", 
         token = Sys.getenv("LDLINK_TOKEN")
        )
##     RS_number rs496202 rs201578600 rs11147477
## 1    rs496202    1.000       0.660      0.504
## 2 rs201578600    0.660       1.000      0.786
## 3  rs11147477    0.504       0.786      1.000


Usage: Multiple query variants (rsID & genomic coordinates), multiple populations, D'

LDmatrix(snps = c("chr13:32444611", "rs11147477", "rs201578600"), 
         pop = c("YRI", "CEU"), r2d = "d", 
         token = Sys.getenv("LDLINK_TOKEN")
        )
##     RS_number rs496202 rs201578600 rs11147477
## 1    rs496202    1.000       0.973      0.738
## 2 rs201578600    0.973       1.000      0.971
## 3  rs11147477    0.738       0.971      1.000


Usage: Multiple query variants read from text file, multiple populations, D'

my_variants <- read.table("variant_list.txt")
my_variants

Then, call LDmatrix with:

LDmatrix(snps = my_variants[,1], 
         pop = c("YRI", "CEU"), r2d = "d", 
         token = Sys.getenv("LDLINK_TOKEN")
        )
##    RS_number rs60676332 rs7805287 rs127 rs456 rs10239961 rs114
## 1 rs60676332      1.000     0.094 0.180 0.151      0.363 0.148
## 2  rs7805287      0.094     1.000 0.818 0.789      0.464 0.710
## 3      rs127      0.180     0.818 1.000 0.929      0.912 0.886
## 4      rs456      0.151     0.789 0.929 1.000      1.000 0.963
## 5 rs10239961      0.363     0.464 0.912 1.000      1.000 0.459
## 6      rs114      0.148     0.710 0.886 0.963      0.459 1.000

Output is a table with rows and columns equal to the number of query variants and pairwise linkage disequilibrium statistics.


LDpair

Function

LDpair(var1, var2, pop = "CEU", token = NULL, output = "table", file = FALSE)

Investigates potentially correlated alleles for a pair of variants. Input is two query variants and a 1000 Genomes Project reference population(s) of interest.

Arguments

Usage: With output argument set to "text"

LDpair(var1 = "rs496202", 
       var2 = "rs11147477", 
       pop = "YRI", 
       token = Sys.getenv("LDLINK_TOKEN"), 
       output = "text"
      )
## Query SNPs:
## rs496202 (chr13:32444611)
## rs11147477 (chr13:32509120)
## 
## YRI Haplotypes:
##                rs11147477
##                C       T
##              -----------------
##            C | 11    | 26    | 37    (0.171)
## rs496202     -----------------
##            G | 173   | 6     | 179   (0.829)
##              -----------------
##                184     32      216
##               (0.852) (0.148)
## 
##           G_C: 173 (0.801)
##           C_T: 26 (0.12)
##           C_C: 11 (0.051)
##           G_T: 6 (0.028)
## 
##           D': 0.7737
##           R2: 0.5037
##       Chi-sq: 108.8005
##      p-value: <0.0001
## 
## rs496202(C) allele is correlated with rs11147477(T) allele
## rs496202(G) allele is correlated with rs11147477(C) allele


Usage: With no output argument option specified, using default "table"

LDpair(var1 = "rs496202", 
       var2 = "rs11147477", 
       pop = "YRI", 
       token = Sys.getenv("LDLINK_TOKEN")
      )
##       var1       var2 pops       var1_pos       var2_pos var1_a1 var1_a2
## 1 rs496202 rs11147477  YRI chr13:32444611 chr13:32509120       C       G
##   var1_a1_freq var1_a2_freq var2_a1 var2_a2 var2_a1_freq var2_a2_freq
## 1        0.171        0.829       C       T        0.852        0.148
##   d_prime     r2    chisq p_val
## 1  0.7737 0.5037 108.8005 1e-04
##                                           corr_alleles
## 1 rs496202(C)-rs11147477(T), rs496202(G)-rs11147477(C)

Output of the output argument "text" option is a two-by-two contingency table displaying haplotype counts and allele frequencies of the two query variants. Also displayed are calculated metrics of linkage disequilibrium including: D prime (D'), R square (R^2^), and goodness-of-fit (Chi-square and p-value). Goodness-of-fit tests for deviations of expected haplotype frequencies based on allele frequencies. Correlated alleles are reported if linkage disequilibrium is present (R^2^ > 0.1). If linkage equilibrium, no alleles are reported.

Output from the output argument "table" option converts the data from the two-by-two contingency table into a data frame.


LDpop

Function

LDpop(var1, var2, pop = "CEU", r2d = "r2", token = NULL, file = FALSE)

Investigates allele frequencies and linkage disequilibrium patterns across 1000G populations.

Arguments

Usage

LDpop(var1 = "rs496202", 
      var2 = "rs11147477", 
      pop = "YRI", 
      r2d = "r2", 
      token = Sys.getenv("LDLINK_TOKEN")
     )
##   Population   N rs496202_Allele_Freq rs11147477_Allele_Freq     R2     D'
## 1        YRI 108 G: 82.87%, C: 17.13%   C: 85.19%, T: 14.81% 0.5037 0.7737

LDproxy

Function

LDproxy(snp, pop = "CEU", r2d = "r2", token = NULL, file = FALSE)

Explore proxy and putative functional variants for a single query variant. Input is a single RS number and a population group. Depending on the number of query populations, this function could take some time to run.

Arguments

Usage: single reference population

my_proxies <- LDproxy(snp = "rs456", 
                      pop = "YRI", 
                      r2d = "r2", 
                      token = Sys.getenv("LDLINK_TOKEN")
                     )

Output is a data frame stored in the variable my_proxies with 2455 rows and 10 columns with data.

head(my_proxies)
##    RS_Number         Coord Alleles    MAF Distance Dprime     R2
## 1      rs456 chr7:24962419   (G/C) 0.1944        0      1 1.0000
## 2      rs457 chr7:24962426   (T/C) 0.1944        7      1 1.0000
## 3 rs28475742 chr7:24964633   (G/T) 0.1944     2214      1 1.0000
## 4      rs123 chr7:24966446   (C/A) 0.1944     4027      1 1.0000
## 5      rs125 chr7:24959703   (C/T) 0.2037    -2716      1 0.9436
## 6      rs128 chr7:24958977   (C/T) 0.2037    -3442      1 0.9436
##   Correlated_Alleles RegulomeDB Function
## 1            G=G,C=C          5     <NA>
## 2            G=T,C=C          5     <NA>
## 3            G=G,C=T          4     <NA>
## 4            G=C,C=A         1f     <NA>
## 5            G=C,C=T          5     <NA>
## 6            G=C,C=T          7     <NA>

Includes information on all variants -/+ 500 Kb of the query variant with a pairwise R^2^ value greater than 0.01.


LDproxy_batch

Function

LDproxy_batch(snp, pop = "CEU", r2d = "r2", token = NULL, append = FALSE)

Query LDproxy using a list of query variants. LDproxy_batch will make sequential queries, one query per variant. Concurrent queries are not permitted by the LDlink API. Output is saved as text file(s) to the current working directory. Depending on the number of query variants and reference populations selected, this function could time some time to run.

Arguments

Usage: multiple variants, default pop and r2d

The list of query variants passed to LDproxy_batch can be stored as a character string.

LDproxy_batch(snp = c("rs456", "rs114", "rs127"), 
              token = Sys.getenv("LDLINK_TOKEN")
             )

Or, a longer list of variants can be read into a data frame from a text file and passed into LDproxy_batch. The list should be in a simple text file, one query variant per line. For example:

my_variants <- read.table("variant_list.txt")
my_variants

Then, call LDproxy_batch with:

LDproxy_batch(snp = my_variants, 
              token = Sys.getenv("LDLINK_TOKEN")
             )

Output not displayed. All output from LDproxy_batch is saved to a text file(s) in the current working directory.


LDtrait

Function

LDtrait(snps,
        pop = "CEU",
        r2d = "r2",
        r2d_threshold = 0.1,
        win_size = 500000,
        token = NULL,
        file = FALSE
        )

Search if a list of variants (or variants in LD with those variants) have been previously associated with a trait or disease. Trait and disease data is updated nightly from the GWAS Catalog.

Arguments

Usage: Single query variant and multiple reference populations

LDtrait(snps = "rs456",
        pop = c("YRI", "CEU"),
        token = Sys.getenv("LDLINK_TOKEN")
       )

The following is the output from the above function call.

##   Query                      GWAS_Trait  RS_Number Position_GRCh37
## 1 rs456 Highest math class taken (MTAG) rs10248878   chr7:24908737
## 2 rs456   Educational attainment (MTAG)      rs457   chr7:24962426
##            Alleles                R2                D' Risk_Allele
## 1 C=0.174, T=0.826 0.412346020761246 0.920415224913495      0.5967
## 2 C=0.698, T=0.302                 1                 1      0.4495
##   Effect_Size_95_CI    Beta_or_OR P_value
## 1            0.0104 0.0071-0.0137   7e-10
## 2            0.0072 0.0047-0.0097   4e-08

Usage: Multiple query variants, multiple reference populations and win_size set to 750000 basepairs (bp).

LDtrait(snps = c("rs114", "rs496202", "rs345"),
        pop = c("YRI", "CHB", "CEU"),
        win_size = "750000",
        token = Sys.getenv("LDLINK_TOKEN")
       )

Output of the above function is below.

##      Query                                        GWAS_Trait  RS_Number
## 1    rs114                   Highest math class taken (MTAG) rs10248878
## 2    rs114                     Educational attainment (MTAG)      rs457
## 3 rs496202                                  Refractive error      rs353
## 4    rs345            DNA methylation variation (age effect)   rs203425
## 5    rs345 Facial morphology (factor 14, intercanthal width)   rs799522
##   Position_GRCh37          Alleles                R2                D'
## 1   chr7:24908737 C=0.123, T=0.877 0.200231693692643 0.897255733792921
## 2   chr7:24962426 C=0.748, T=0.252  0.56312684849231 0.969967060647161
## 3  chr13:32454349 A=0.902, G=0.098                 1                 1
## 4  chr13:32468087 A=0.074, T=0.926 0.954994192799071                 1
## 5  chr13:32514028 C=0.769, T=0.231 0.236284178064096 0.918763102725367
##   Risk_Allele Effect_Size_95_CI    Beta_or_OR P_value
## 1      0.5967            0.0104 0.0071-0.0137   7e-10
## 2      0.4495            0.0072 0.0047-0.0097   4e-08
## 3        <NA>              <NA>          <NA>   1e-12
## 4          NR              <NA>          <NA>   2e-08
## 5      0.1263            0.2157     0.12-0.31   6e-06

SNPchip

Function

SNPchip(snps, chip = "ALL", token = NULL, file = FALSE)

Used to find commercial genotyping chip arrays for variants. Input is a list of between 1 - 5000 variants (one per line) and desired commercial chip arrays to search. Input variants do not need to be on the same chromosome.

Arguments

Usage: Multiple variants, search "ALL" available chip arrays

SNPchip(snps = c("rs3", "rs4", "rs148890987"), 
        chip = "ALL", 
        token = Sys.getenv("LDLINK_TOKEN")
       )
## WARNING: The following RS number did not have any platforms found: rs148890987, rs3.

##     RS_Number Position_GRCh37 A_SNP5.0 A_CHB2 A_250S A_SNP6.0
## 1 rs148890987  chr13:32403784        0      0      0        0
## 2         rs3  chr13:32446842        0      0      0        0
## 3         rs4  chr13:32447222        1      1      1        1


Usage: Multiple variants, search two Affymetrix arrays

SNPchip(snps = c("rs3", "rs4", "rs148890987"), 
        chip = c("A_SNP5.0", "A_CHB2"), 
        token = Sys.getenv("LDLINK_TOKEN")
       )
## WARNING: The following RS number did not have any platforms found: rs148890987, rs3.

##     RS_Number Position_GRCh37 A_SNP5.0 A_CHB2
## 1 rs148890987  chr13:32403784        0      0
## 2         rs3  chr13:32446842        0      0
## 3         rs4  chr13:32447222        1      1


Usage: Multiple variants, search all available Affymetrix arrays using, "ALL_Affy"

SNPchip(snps = c("rs3", "rs4", "rs148890987"), 
        chip = "ALL_Affy", 
        token = Sys.getenv("LDLINK_TOKEN")
       )
## WARNING: The following RS number did not have any platforms found: rs148890987, rs3.

##     RS_Number Position_GRCh37 A_SNP5.0 A_CHB2 A_250S A_SNP6.0
## 1 rs148890987  chr13:32403784        0      0      0        0
## 2         rs3  chr13:32446842        0      0      0        0
## 3         rs4  chr13:32447222        1      1      1        1

Output is a data frame of query variant rows (RS number), genomic coordinate (GRCh37) and genotyping chip array columns. The presence of a "1" designates the variant is present on the respective commercial genotyping array and a "0" indicates that it is not present on the genotyping array.


SNPclip

Function

SNPclip(snps, pop = "CEU", r2_threshold = "0.1", maf_threshold = "0.01", token = NULL, file = FALSE)

Prune a list of variants by linkage disequilibrium. Input is a list of variant RS numbers (one per line) and a population group.

Arguments

Usage: Multiple Variants

SNPclip(snps =  c("rs3", "rs4", "rs148890987", "rs115955931"), 
        pop = "YRI", 
        r2_threshold =  "0.1", 
        maf_threshold = "0.01", 
        token = Sys.getenv("LDLINK_TOKEN")
       )
##     RS_Number       Position          Alleles
## 1         rs3 chr13:32446842 C=0.829, T=0.171
## 2         rs4 chr13:32447222 A=0.829, G=0.171
## 3 rs148890987 chr13:32403784     C=1.0, T=0.0
## 4 rs115955931 chr13:32130008 G=0.954, A=0.046
##                                             Details
## 1                                     Variant kept.
## 2 Variant in LD with rs3 (R2=1.0), variant removed.
## 3              Variant MAF is 0.0, variant removed.
## 4                                     Variant kept.

The output table provides details including query variant RS number, genomic position, alleles, and and details about whether the variant was kept or removed.


Utilities and Examples

list_chips

Function

list_chips()

Provides a data frame listing the names and abbreviation codes for available commercial SNP Chip Arrays from Illumina and Affymetrix.

Usage

list_chips()

list_pop

Function

list_pop()

Provides a data frame listing the available reference populations from the 1000 Genomes Project, continental or super-populations (e.g. European, African, Admixed American) and sub-populations (e.g Finnish, Gambian, Peruvian)

Usage

list_pop()

list_gtex_tissues

Function

list_gtex_tissues()

Provides a data frame listing the GTEx full names, LDexpress full names (without spaces) and acceptable abbreviation codes of the 54 non-diseased tissue sites collected for the GTEx Portal and used as input for the LDexpress function.

Usage

options(width = 100)
list_gtex_tissues()

FAQs (Frequently Asked Questions)

  1. What if my access token doesn't work?

    • Please double check that the token was typed accurately. Then, ensure the format of the function call is correct. For example, if your alphanumeric access token is: 123abc456789, then, use it as:\

    r df <- LDproxy(snp = "rs456", pop = "YRI", token = "123abc456789")


| If you still can not solve the problem, please email us at NCILDlinkWebAdmin\@mail.nih.gov{.email}.


  1. Can I set a threshold or cut-off value for R^2^ or D` values?

    • No. LDlinkR functions do not include 'threshold' as an argument. However, the returned data object can be subset using base R. For example:
df <- LDproxy("rs12027135", pop = "CEU",r2d = "r2", token = "YourTokenHere123")
new_df <- subset(df, R2 >= 0.8)


  1. I need to upload hundreds of variants from a text file into LDmatrix. Why do I get an error with the following code?
test <- read.table("variant_list.txt", header = FALSE)
LDmatrix(snps = test, pop = "CEU", r2d = "r2", token = "YourTokenHere123")

Error in LDmatrix(snps = test, pop = "CEU", r2d = "r2", token = "YourTokenHere123"), : Input is between 2 to 1000 variants.


test <- read.table("variant_list.txt", header = FALSE)
LDmatrix(snps = test[,1], pop = "CEU", r2d = "r2", token = "YourTokenHere123")
##    RS_number rs60676332 rs7805287 rs127 rs456 rs10239961 rs114
## 1 rs60676332      1.000     0.008 0.013 0.017      0.286 0.039
## 2  rs7805287      0.008     1.000 0.980 0.882      0.170 0.614
## 3      rs127      0.013     0.980 1.000 0.900      0.167 0.632
## 4      rs456      0.017     0.882 0.900 1.000      0.177 0.722
## 5 rs10239961      0.286     0.170 0.167 0.177      1.000 0.008
## 6      rs114      0.039     0.614 0.632 0.722      0.008 1.000


  1. What genome build does LDlink use for genomic coordinates?

    • All genomic coordinates are based on GRCh37/hg19.


  1. How can I ask for help?

Session Information

sessionInfo()


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LDlinkR documentation built on Feb. 20, 2021, 1:06 a.m.