Simulations (mix normal prior), ctwas_rss, 45k samples
Last updated: 2021-04-04
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Knit directory: causal-TWAS/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 3dd8cb2 | simingz | 2021-02-19 | mix normal simulate phenotype |
| html | 3dd8cb2 | simingz | 2021-02-19 | mix normal simulate phenotype |
library(ctwas)
library(data.table)
suppressMessages({library(plotly)})
library(tidyr)
library(plyr)
library(stringr)
source("~/causalTWAS/causal-TWAS/analysis/summarize_ctwas_plots.R")
source('~/causalTWAS/causal-TWAS/analysis/summarize_twas-coloc_plots.R')
source('~/causalTWAS/causal-TWAS/code/qqplot.R')pgenfn = "/home/simingz/causalTWAS/ukbiobank/ukb_pgen_s80.45/ukb-s80.45_pgenfs.txt"
ld_pgenfn = "/home/simingz/causalTWAS/ukbiobank/ukb_pgen_s80.45/ukb-s80.45.2_pgenfs.txt"
outputdir = "/home/simingz/causalTWAS/simulations/simulation_ctwas_rss_20210216/" # /
comparedir = "/home/simingz/causalTWAS/simulations/simulation_ctwas_rss_20210216_compare/"
runtag = "ukb-s80.45-adi"
configtags = 1
simutags = paste(rep(1:2, each = length(1:5)), 1:5, sep = "-")
pgenfs <- read.table(pgenfn, header = F, stringsAsFactors = F)[,1]
pvarfs <- sapply(pgenfs, prep_pvar, outputdir = outputdir)
ld_pgenfs <- read.table(ld_pgenfn, header = F, stringsAsFactors = F)[,1]
ld_pvarfs <- sapply(ld_pgenfs, prep_pvar, outputdir = outputdir)
pgens <- lapply(1:length(pgenfs), function(x) prep_pgen(pgenf = pgenfs[x],pvarf = pvarfs[x]))Analysis description
n.ori <- 80000 # number of samples
n <- pgenlibr::GetRawSampleCt(pgens[[1]])
p <- sum(unlist(lapply(pgens, pgenlibr::GetVariantCt))) # number of SNPs
J <- 8021 # number of genesData
- GWAS summary statistics we simulated summary statistics data with different causal gene/SNP proportion and PVE. To simulate this data, we need the following:
genotype data we used is from UKB biobank, randomly selecting 80000 samples. We then filtered samples based on relatedness, ethics and other qc metrics, that ended up with n = 45087 samples. We use SNP genotype data from chr 1 to chr 22 combined from UKB. There are total = 6228664 SNPs.
Expression models The one we used in this analysis is GTEx Adipose tissue v7 dataset. This dataset contains ~ 380 samples, 8021 genes with expression model. FUSION/TWAS were used to train expression model and we used their lasso results. SNPs included in eQTL anlaysis are restricted to cis-locus 500kb on either side of the gene boundary. eQTLs are defined as SNPs with abs(effectize) > 1e-8 in lasso results.
To simulate phenotype data, first we impute gene expression based on expression models, then we set gene/SNP pi1 and PVE, get rough effect size for causal SNPs and genes and simulate phenotype under the mixture of normal distributions.
The prior distribution for causal effect:
nmix <- 4
pi_k <- rep(1/nmix, nmix)
ra <- 2**(1: length(pi_k))
sa2_kr <- ra / (pi_k * sum(ra))
cat("number of mixtures: ", nmix, "\n")number of mixtures: 4 cat("variance of each component:", sa2_kr, "\n")variance of each component: 0.2666667 0.5333333 1.066667 2.133333 hist(mixtools::rnormmix(2000, lambda = pi_k,
mu = rep(0, length(pi_k)),
sigma = sa2_kr), breaks = 100, main = "prior distribution", xlab = "")
| Version | Author | Date |
|---|---|---|
| 3dd8cb2 | simingz | 2021-02-19 |
Then we performed GWAS for all SNPs and get z scores for each by univariate linear regression. LD genotype reference We randomly selected 2000 samples to serve as the LD reference.
Expression models
We used GTEx Adipose tissue v7 dataset, the same as used for simulating phenotypes.
Analysis
Get z scores for gene expression. We used expression models and LD reference to get z scores for gene expression.
Run ctwas_rss We used LDetect to define regions.
ctwas_rssalgorithm first runs on all regions to get rough estimate for gene and SNP prior. Then run on small regions (having small probablities of having > 1 causal signals based on rough estimates) to get more accurate estimate. To lower computational burden, we downsampled SNPs (0.1), estimate parameters. We run susie for all regions using estimated parameters, select regions with strong gene signals (max gene PIP > 0.8) and rerun using full SNPs for these regions.
Power estimation
simutag <- "1-1"
niter <- 1000
snp.p <- 5e-8
gene.p <- 1e-5
source(paste0(outputdir, "simu", simutag, "_param.R"))
load(paste0(outputdir, runtag, "_simu", simutag, "-pheno.Rd"))We select run 1-1 as an example.
- For SNPs. \(\pi_1 =\) 2.510^{-4} , effect size = 0.0283342, PVE = 0.493565. Power at 5e-08 p value cutoff:
load("data/power_s80.45.Rd")
# p1 <- pow(niter, n, phenores[["batch"]][[1]][["sigma_theta"]], snp.p)
print(p1)[1] 0.159- For genes. \(\pi_1 = 0.05\) , effect size = 0.0248146, PVE = 0.1156162. Power at 1e-05 p value cutoff:
# p2 <- pow(niter, n, phenores[["batch"]][[1]][["sigma_beta"]], gene.p)
print(p2)[1] 0.2# save(p1,p2, file = "data/power_s80.45.Rd")GWAS/TWAS p value distribution
simutag <- "1-1"
chrom <- 1
source(paste0(outputdir, "simu", simutag, "_param.R"))
load(paste0(outputdir, runtag, "_simu", simutag, "-pheno.Rd"))We select run 1-1 as an example.
- For genes. \(\pi_1 = 0.05\) , effect size = 0.0248146, PVE = 0.1156162. TWAS p values and qqplot:
exprgwasf <- paste0(outputdir, runtag, "_simu", simutag, ".exprgwas.txt.gz")
exprvarf <- paste0(outputdir, runtag, "_chr", chrom, ".exprvar")
exprid <- read_exprvar(exprvarf)[, "id"]
cau <- as.matrix(exprid[phenores[["batch"]][[chrom]][["idx.cgene"]]])
pdist_plot(exprgwasf, chrom, cau)
| Version | Author | Date |
|---|---|---|
| 3dd8cb2 | simingz | 2021-02-19 |
exprgwas <- fread(exprgwasf, header =T)
gg_qqplot(exprgwas$PVALUE)
| Version | Author | Date |
|---|---|---|
| 3dd8cb2 | simingz | 2021-02-19 |
- For SNPs. \(\pi_1 =\) 2.510^{-4} , effect size = 0.0283342, PVE = 0.493565. GWAS p values and qqplot:
snpgwasf <- paste0(outputdir, runtag, "_simu", simutag, ".snpgwas.txt.gz")
pvarf <- pvarfs[chrom]
snpid <- read_pvar(pvarf)[, "id"]
cau <- as.matrix(snpid[phenores[["batch"]][[chrom]][["idx.cSNP"]]])
pdist_plot(snpgwasf, chrom, cau, thin = 0.1)
| Version | Author | Date |
|---|---|---|
| 3dd8cb2 | simingz | 2021-02-19 |
snpgwas <- fread(snpgwasf, header =T)
gg_qqplot(snpgwas$PVALUE, thin = 0.1)
| Version | Author | Date |
|---|---|---|
| 3dd8cb2 | simingz | 2021-02-19 |
ctwas results + other comparators
Results: Each row shows parameter estimation results from 5 simulation runs with similar settings (i.e. pi1 and PVE for genes and SNPs). Results from each run were represented by one dot, dots with the same color come from the same run. truth: the true parameters, selected_truth: the truth in selected regions that were used to estimate parameters, ctwas: ctwas estimated parameters (using summary statistics as input).
We run FUSION following default settings and adjust p values by BH method to get expected FDP. we ran coloc for all genes with TWAS p < 1e-4. We use PP4 (SNP associate with both traits).
plot_par <- function(configtag, runtag, simutags){
source(paste0(outputdir, "config", configtag, ".R"))
phenofs <- paste0(outputdir, runtag, "_simu", simutags, "-pheno.Rd")
susieIfs <- paste0(outputdir, runtag, "_simu", simutags, "_config", configtag, ".s2.susieIrssres.Rd")
susieIfs2 <- paste0(outputdir, runtag, "_simu",simutags, "_config", configtag,".s2.susieIrss.txt")
mtx <- show_param(phenofs, susieIfs, susieIfs2, thin = thin)
par(mfrow=c(1,3))
cat("simulations ", paste(simutags, sep=",") , ": ")
cat("mean gene PVE:", mean(mtx[, "PVE.gene_truth"]), ",", "mean SNP PVE:", mean(mtx[, "PVE.SNP_truth"]), "\n")
plot_param(mtx)
}
plot_PIP <- function(configtag, runtag, simutags){
phenofs <- paste0(outputdir, "ukb-s80.45-adi", "_simu", simutags, "-pheno.Rd")
susieIfs <- paste0(outputdir, runtag, "_simu",simutags, "_config", configtag,".susieIrss.txt")
f1 <- caliPIP_plot(phenofs, susieIfs)
f2 <- ncausal_plot(phenofs, susieIfs)
gridExtra::grid.arrange(f1, f2, ncol =2)
}
plot_fusion_coloc <- function(configtag, runtag, simutags){
phenofs <- paste0(outputdir, runtag, "_simu", simutags, "-pheno.Rd")
fusioncolocfs <- paste0(comparedir, runtag, "_simu", simutags, ".Adipose_Subcutaneous.coloc.result")
f1 <- caliFUSIONp_plot(phenofs, fusioncolocfs)
f2 <- ncausalFUSIONp_plot(phenofs, fusioncolocfs)
f3 <- caliPP4_plot(phenofs, fusioncolocfs)
f4 <- ncausalPP4_plot(phenofs, fusioncolocfs)
gridExtra::grid.arrange(f1, f2, ncol=2)
gridExtra::grid.arrange(f3, f4, ncol=2)
}configtag <- 1
runtag = "ukb-s80.45-adi"
simutags <- paste(1, 1:5, sep = "-")
plot_par(configtag, runtag, simutags)simulations 1-1 1-2 1-3 1-4 1-5 : mean gene PVE: 0.101655 , mean SNP PVE: 0.5054682 
| Version | Author | Date |
|---|---|---|
| 3dd8cb2 | simingz | 2021-02-19 |
plot_PIP(configtag, runtag, simutags)
| Version | Author | Date |
|---|---|---|
| 3dd8cb2 | simingz | 2021-02-19 |
plot_fusion_coloc(configtag, runtag, simutags)
| Version | Author | Date |
|---|---|---|
| 3dd8cb2 | simingz | 2021-02-19 |
| Version | Author | Date |
|---|---|---|
| 3dd8cb2 | simingz | 2021-02-19 |
simutags <- paste(2, 1:5, sep = "-")
plot_par(configtag, runtag, simutags)simulations 2-1 2-2 2-3 2-4 2-5 : mean gene PVE: 0.09087206 , mean SNP PVE: 0.5077459 
| Version | Author | Date |
|---|---|---|
| 3dd8cb2 | simingz | 2021-02-19 |
plot_PIP(configtag, runtag, simutags)
| Version | Author | Date |
|---|---|---|
| 3dd8cb2 | simingz | 2021-02-19 |
plot_fusion_coloc(configtag, runtag, simutags)
| Version | Author | Date |
|---|---|---|
| 3dd8cb2 | simingz | 2021-02-19 |
| Version | Author | Date |
|---|---|---|
| 3dd8cb2 | simingz | 2021-02-19 |
simutags <- paste(3, 1:5, sep = "-")
plot_par(configtag, runtag, simutags)simulations 3-1 3-2 3-3 3-4 3-5 : mean gene PVE: 0.05090841 , mean SNP PVE: 0.5059264 
| Version | Author | Date |
|---|---|---|
| 3dd8cb2 | simingz | 2021-02-19 |
plot_PIP(configtag, runtag, simutags)
| Version | Author | Date |
|---|---|---|
| 3dd8cb2 | simingz | 2021-02-19 |
plot_fusion_coloc(configtag, runtag, simutags)
| Version | Author | Date |
|---|---|---|
| 3dd8cb2 | simingz | 2021-02-19 |
| Version | Author | Date |
|---|---|---|
| 3dd8cb2 | simingz | 2021-02-19 |
simutags <- paste(4, 1:5, sep = "-")
plot_par(configtag, runtag, simutags)simulations 4-1 4-2 4-3 4-4 4-5 : mean gene PVE: 0.04527873 , mean SNP PVE: 0.5057163 
| Version | Author | Date |
|---|---|---|
| 3dd8cb2 | simingz | 2021-02-19 |
plot_PIP(configtag, runtag, simutags)
| Version | Author | Date |
|---|---|---|
| 3dd8cb2 | simingz | 2021-02-19 |
plot_fusion_coloc(configtag, runtag, simutags)
| Version | Author | Date |
|---|---|---|
| 3dd8cb2 | simingz | 2021-02-19 |
| Version | Author | Date |
|---|---|---|
| 3dd8cb2 | simingz | 2021-02-19 |
simutags <- paste(5, 1:5, sep = "-")
plot_par(configtag, runtag, simutags)simulations 5-1 5-2 5-3 5-4 5-5 : mean gene PVE: 0.2026065 , mean SNP PVE: 0.5044246 
| Version | Author | Date |
|---|---|---|
| 3dd8cb2 | simingz | 2021-02-19 |
plot_PIP(configtag, runtag, simutags)
| Version | Author | Date |
|---|---|---|
| 3dd8cb2 | simingz | 2021-02-19 |
plot_fusion_coloc(configtag, runtag, simutags)
| Version | Author | Date |
|---|---|---|
| 3dd8cb2 | simingz | 2021-02-19 |
| Version | Author | Date |
|---|---|---|
| 3dd8cb2 | simingz | 2021-02-19 |
simutags <- paste(6, 1:5, sep = "-")
plot_par(configtag, runtag, simutags)simulations 6-1 6-2 6-3 6-4 6-5 : mean gene PVE: 0.183079 , mean SNP PVE: 0.5120282 
plot_PIP(configtag, runtag, simutags)
plot_fusion_coloc(configtag, runtag, simutags)
simutags <- paste(7, 1:5, sep = "-")
plot_par(configtag, runtag, simutags)simulations 7-1 7-2 7-3 7-4 7-5 : mean gene PVE: 0.1033152 , mean SNP PVE: 0.2994475 
plot_PIP(configtag, runtag, simutags)
plot_fusion_coloc(configtag, runtag, simutags)
simutags <- paste(8, 1:5, sep = "-")
plot_par(configtag, runtag, simutags)simulations 8-1 8-2 8-3 8-4 8-5 : mean gene PVE: 0.09923334 , mean SNP PVE: 0.3059087 
plot_PIP(configtag, runtag, simutags)
plot_fusion_coloc(configtag, runtag, simutags)
simutags <- paste(9, 1:5, sep = "-")
plot_par(configtag, runtag, simutags)simulations 9-1 9-2 9-3 9-4 9-5 : mean gene PVE: 0.02037926 , mean SNP PVE: 0.5061201 
plot_PIP(configtag, runtag, simutags)
plot_fusion_coloc(configtag, runtag, simutags)
simutags <- paste(10, 1:5, sep = "-")
plot_par(configtag, runtag, simutags)simulations 10-1 10-2 10-3 10-4 10-5 : mean gene PVE: 0.01807619 , mean SNP PVE: 0.504565 
plot_PIP(configtag, runtag, simutags)
plot_fusion_coloc(configtag, runtag, simutags)
sessionInfo()R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggpubr_0.4.0 plotrix_3.7-6 cowplot_1.0.0
[4] stringr_1.4.0 plyr_1.8.4 tidyr_1.1.0
[7] plotly_4.9.0 ggplot2_3.2.1 data.table_1.13.2
[10] ctwas_0.1.12
loaded via a namespace (and not attached):
[1] mixtools_1.2.0 httr_1.4.2 splines_3.6.1
[4] jsonlite_1.6 viridisLite_0.3.0 foreach_1.4.4
[7] R.utils_2.9.0 pgenlibr_0.2 carData_3.0-2
[10] logging_0.10-108 highr_0.8 cellranger_1.1.0
[13] yaml_2.2.0 pillar_1.5.1 backports_1.1.4
[16] lattice_0.20-38 glue_1.4.2 digest_0.6.20
[19] promises_1.0.1 ggsignif_0.5.0 colorspace_1.4-1
[22] R.oo_1.22.0 htmltools_0.3.6 httpuv_1.5.1
[25] Matrix_1.2-18 pkgconfig_2.0.2 broom_0.7.5
[28] haven_2.3.1 purrr_0.3.4 scales_1.1.0
[31] whisker_0.3-2 openxlsx_4.1.0.1 later_0.8.0
[34] rio_0.5.16 git2r_0.26.1 tibble_3.1.0
[37] farver_2.0.1 generics_0.0.2 car_3.0-5
[40] ellipsis_0.2.0.1 withr_2.4.1 lazyeval_0.2.2
[43] survival_2.44-1.1 magrittr_1.5 crayon_1.3.4
[46] readxl_1.3.1 evaluate_0.14 R.methodsS3_1.7.1
[49] fs_1.3.1 fansi_0.4.0 segmented_1.0-0
[52] MASS_7.3-51.4 rstatix_0.7.0 forcats_0.4.0
[55] foreign_0.8-71 tools_3.6.1 hms_0.5.3
[58] lifecycle_1.0.0 kernlab_0.9-27 munsell_0.5.0
[61] ggsci_2.9 zip_2.0.3 compiler_3.6.1
[64] rlang_0.4.10 debugme_1.1.0 grid_3.6.1
[67] iterators_1.0.10 htmlwidgets_1.3 labeling_0.3
[70] rmarkdown_1.13 gtable_0.3.0 codetools_0.2-16
[73] abind_1.4-5 DBI_1.1.0 curl_3.3
[76] R6_2.4.0 gridExtra_2.3 knitr_1.23
[79] dplyr_1.0.5 utf8_1.1.4 workflowr_1.6.2
[82] rprojroot_1.3-2 stringi_1.4.3 Rcpp_1.0.5
[85] vctrs_0.3.7 tidyselect_1.1.0 xfun_0.8