Last updated: 2024-04-30

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Rmd 243ff3b mxcai 2024-04-30 add LD construction
html 243ff3b mxcai 2024-04-30 add LD construction

We demonstrate how to fit XMAP with reference genotypes from publicly available reference genotypes. For illustration, we consider the LDL GWAS summary statistics from the full example, while using the 1000 Genomes data to construct EUR and AFR LD matrices. In the following, we introduce how to construct LD matrices from the 1000 Genomes data and fit XMAP with the constructed LD matrices step by step.

Step 1: Download and preprocess 1000 Genomes genotypes

We first obtain GRCH37 1000 Genomes genotype files from plink and convert them to plink1 format.

plink2 \\
  --autosome \\
  --make-bed \\
  --max-alleles 2 \\
  --out all_phase3_noQC \\
  --pfile all_phase3 \\
  --rm-dup exclude-all \\
  --snps-only just-acgt \\
  --var-filter

Next, we perform QC, extract EUR and AFR samples, and obtain the genotype files for the target chromosome (chromosome 8 here).

plink2 \\
  --bfile all_phase3_noQC \\
  --chr 8 \\
  --hwe 1e-10 \\
  --keep iid_EUR.txt \\
  --maf 0.005 \\
  --make-bed \\
  --out all_phase3_EUR_chr8 \\
  --var-filter
  
plink2 \\
  --bfile all_phase3_noQC \\
  --chr 8 \\
  --hwe 1e-10 \\
  --keep iid_AFR.txt \\
  --maf 0.005 \\
  --make-bed \\
  --out all_phase3_AFR_chr8 \\
  --var-filter

Step 2: Construct LD matrices for EUR and AFR in the target region

Once the plink files are ready, we can read summary statistics and extract SNPs from the summary data and reference genotype in the target region. Here, we focus on 20000001-23000001 in chromosome 8, which is present in our manuscript.

library(XMAP)
library(susieR)
library(data.table)
library(Matrix)
library(snpStats)

# read sumstats
sumstat_EUR <- fread("/Users/cmx/Documents/Research/Project/Fine_Mapping/sumstats/LDL_allSNPs_UKBNealLab_summary_format.txt")
sumstat_AFR <- fread("/Users/cmx/Documents/Research/Project/Fine_Mapping/sumstats/LDL_AFR_GLGC_summary_format.txt")

# read genotype information
bim_EUR <- fread("/Users/cmx/Documents/Research/Data/GWAS/Genotype/all_phase3_EUR_chr8.bim")
bim_AFR <- fread("/Users/cmx/Documents/Research/Data/GWAS/Genotype/all_phase3_AFR_chr8.bim")

# detect allele ambiguous SNPs
idx_amb_EUR <- (bim_EUR$V5 == comple(bim_EUR$V6))
idx_amb_AFR <- (bim_AFR$V5 == comple(bim_AFR$V6))

# identify overlapping SNPs on region chr8_20000001_23000001
snps <- Reduce(intersect, list(bim_AFR$V2[!idx_amb_AFR & bim_AFR$V4 > 20000001 & bim_AFR$V4 < 23000001], bim_EUR$V2[!idx_amb_EUR], sumstat_AFR$SNP, sumstat_EUR$SNP))

# read genotype matrices
geno_EUR <- read.plink("/Users/cmx/Documents/Research/Data/GWAS/Genotype/all_phase3_EUR_chr8.bed")
X_EUR <- as(geno_EUR$genotypes, "numeric")
X_EUR_i <- X_EUR[,match(snps, bim_EUR$V2)]

geno_AFR <- read.plink("/Users/cmx/Documents/Research/Data/GWAS/Genotype/all_phase3_AFR_chr8.bed")
X_AFR <- as(geno_AFR$genotypes, "numeric")
X_AFR_i <- X_AFR[,match(snps, bim_AFR$V2)]

bim_EUR_i <- bim_EUR[match(snps, bim_EUR$V2), ]
bim_AFR_i <- bim_AFR[match(snps, bim_AFR$V2), ]

# align genotype alleles using EUR as reference
idx_flip <- which(bim_AFR_i$V5!=bim_EUR_i$V5 & bim_AFR_i$V5!=comple(bim_EUR_i$V5))
# flip allels in AFR genotypes
X_AFR_i[,idx_flip] <- 2 - X_AFR_i[,idx_flip]

# compute LD matrices
R_EUR <- cor(X_EUR_i)
R_AFR <- cor(X_AFR_i)

The R_EUR and R_AFR constructed above will be used for fitting XMAP in the subsequent analysis. We also extract SNPs from summary data for the target region and flip alleles in the summary data to match the reference genotype.

# extract target region snps from sumstats data
sumstat_AFR_i <- sumstat_AFR[match(snps, sumstat_AFR$SNP), ]
sumstat_EUR_i <- sumstat_EUR[match(snps, sumstat_EUR$SNP), ]

# flip alleles in sumstats
z_afr <- sumstat_AFR_i$beta / sumstat_AFR_i$se
z_eur <- sumstat_EUR_i$Z

idx_flip <- which(sumstat_AFR_i$A1 != bim_EUR_i$V5 & sumstat_AFR_i$A1 != comple(bim_EUR_i$V5))
z_afr[idx_flip] <- -z_afr[idx_flip]

idx_flip <- which(sumstat_EUR_i$A1 != bim_EUR_i$V5 & sumstat_EUR_i$A1 != comple(bim_EUR_i$V5))
z_eur[idx_flip] <- -z_eur[idx_flip]

Step 3: Fit XMAP with the constructed LD matrices

With the LD matrices R_EUR and R_AFR, and the summary statistics z_eur and z_afr, we can fit XMAP to obtain the causal SNPs and credible sets. Here, we set the number of causal SNPs to 10. We set the C1 and C2 values to the estimated LDSC intercepts. Details can be found at the step 1 in the full example.

# LDSC intercepts
c1 <- 1.066501
c2 <- 1.095526

# fit XMAP
xmap <- XMAP(simplify2array(list(R_EUR, R_AFR)), cbind(z_eur, z_afr), n=c(median(sumstat_EUR_i$N), median(sumstat_AFR_i$N)),
             K = 10, Omega = OmegaHat, Sig_E = c(c1, c2), tol = 1e-6,
             maxIter = 200, estimate_residual_variance = F, estimate_prior_variance = T,
             estimate_background_variance = F)
cs1 <- get_CS(xmap, Xcorr = R_AFR, coverage = 0.9, min_abs_corr = 0.1)
cs2 <- get_CS(xmap, Xcorr = R_EUR, coverage = 0.9, min_abs_corr = 0.1)
cs_xmap <- cs1$cs[intersect(names(cs1$cs), names(cs2$cs))]
pip_xmap <- get_pip(xmap$gamma)
plot_CS(pip_xmap, cs_xmap, main = "XMAP",b = (bim_EUR_i$V2 == "rs900776"))

Version Author Date
243ff3b mxcai 2024-04-30

For comparison, we also fit SuSiE with AFR and EUR data separately and plot the credible sets.

susie_EUR <- susie_rss(z=z_eur,R=R_EUR,n=median(sumstat_EUR_i$N))
susie_plot(susie_EUR,y="PIP",b = (bim_EUR_i$V2 == "rs900776"))

susie_AFR <- susie_rss(z=z_afr,R=R_AFR,n=median(sumstat_AFR_i$N))
susie_plot(susie_AFR,y="PIP",b = (bim_EUR_i$V2 == "rs900776"))

Version Author Date
243ff3b mxcai 2024-04-30

Version Author Date
243ff3b mxcai 2024-04-30

Caution on out-sample LD reference

In this example, we used publicly available data from 1000 Genomes Project to construct the LD matrices for EUR and AFR populations. However, the LD matrices constructed from the reference data may produce unreliable results in fine-mapping. In practice, we recommend using the in-sample genotype data to construct the LD matrices for the target population. One can follow the above steps to construct their own LD matrices and fit XMAP with the in-sample genotype data.


sessionInfo()
R version 4.1.2 (2021-11-01)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS 13.6.3

Matrix products: default
BLAS:   /opt/homebrew/Cellar/openblas/0.3.19/lib/libopenblasp-r0.3.19.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] susieR_0.12.35 XMAP_1.0      

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.0   xfun_0.32          bslib_0.4.0        lattice_0.20-45   
 [5] colorspace_2.0-2   vctrs_0.6.3        generics_0.1.1     htmltools_0.5.4   
 [9] yaml_2.3.5         utf8_1.2.2         rlang_1.1.1        mixsqp_0.3-43     
[13] jquerylib_0.1.4    later_1.3.0        pillar_1.9.0       glue_1.6.2        
[17] matrixStats_0.61.0 lifecycle_1.0.3    plyr_1.8.6         stringr_1.4.1     
[21] munsell_0.5.0      gtable_0.3.0       workflowr_1.7.0    evaluate_0.16     
[25] knitr_1.40         fastmap_1.1.0      httpuv_1.6.5       irlba_2.3.5       
[29] fansi_0.5.0        highr_0.9          Rcpp_1.0.10        promises_1.2.0.1  
[33] scales_1.1.1       cachem_1.0.6       jsonlite_1.8.0     fs_1.5.2          
[37] ggplot2_3.3.5      digest_0.6.29      stringi_1.7.8      dplyr_1.1.2       
[41] rprojroot_2.0.2    grid_4.1.2         cli_3.6.1          tools_4.1.2       
[45] magrittr_2.0.3     sass_0.4.2         tibble_3.2.1       crayon_1.5.1      
[49] whisker_0.4        pkgconfig_2.0.3    Matrix_1.5-4.1     rmarkdown_2.16    
[53] reshape_0.8.8      rstudioapi_0.13    R6_2.5.1           git2r_0.31.0      
[57] compiler_4.1.2