Simulations (mix normal prior), ctwas_rss, 45k samples

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Last updated: 2021-07-22

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Knit directory: causal-TWAS/

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Rmd	6dee9b0	simingz	2021-07-22	bonferroni Fusion, description
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html	fe0e8f8	simingz	2021-05-09	added smr-heidi results, scripts matching v0.1.25
Rmd	f9eedf9	simingz	2021-04-21	focus results

---

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/analysis/summarize_focus_plots.R')
source('~/causalTWAS/causal-TWAS/analysis/summarize_smr_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_20210418/" # /
comparedir = "/home/simingz/causalTWAS/simulations/simulation_ctwas_rss_20210418_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 genes

Data

• 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 n = 45087 samples. It is the same as used here

  • 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 = "")

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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

1. Get z scores for gene expression. We used expression models and LD reference to get z scores for gene expression.

2. Run ctwas_rss We used LDetect to define regions. ctwas_rss algorithm 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 = 0.4888906. 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 = 0.114035. 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 = 0.114035. 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)

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exprgwas <- fread(exprgwasf, header =T)
gg_qqplot(exprgwas$PVALUE)

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• For SNPs. \(\pi_1 =\) 2.510^{-4} , effect size = 0.0283342, PVE = 0.4888906. 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)

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snpgwas <- fread(snpgwasf, header =T)
gg_qqplot(snpgwas$PVALUE, thin = 0.1)

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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 have also used Bonferroni correction for p values. we ran coloc for all genes with TWAS p < 1e-4. We use PP4 (SNP associate with both traits). We ran SMR+HEIDI, using eQTL summary statistics GTEx v.7. We filter the results by requiring p_HEIDI > 0.05. The plots are based on SMR p value adjusted by BH method to get expected FDP.

We have tried to run MR-JTI. The results have higher false postive rate than TWAS. MR-JTI requires the SNPs be pruned before the analysis. It also requires that a gene has at least 20 eQTLs. This resulted in very few genes going into the analysis. Most genes left are in polymorphism dense regions, such as the MHC regions. I ran MR-JTI for top genes in TWAS, around 30-40% of them should be real. However, only a few genes pass MR-JTI’s 20 eQTL requirements and only 1 or 2 (5%) genes are real. We are showing MR-JTI results on this page.

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 <- caliFUSIONbon_plot(phenofs, fusioncolocfs)
    f4 <- ncausalFUSIONbon_plot(phenofs, fusioncolocfs)
    f5 <- caliPP4_plot(phenofs, fusioncolocfs, twas.p = 0.05/J)
    f6 <- ncausalPP4_plot(phenofs, fusioncolocfs, twas.p = 0.05/J)
    gridExtra::grid.arrange(f1, f2, ncol=2)
    gridExtra::grid.arrange(f3, f4, ncol=2)
    gridExtra::grid.arrange(f5, f6, ncol=2)
}

plot_focus <- function(configtag, runtag,  simutags){
    phenofs <- paste0(outputdir, runtag, "_simu", simutags, "-pheno.Rd")
    focusfs <- paste0(comparedir, runtag, "_simu", simutags, ".Adipose_Subcutaneous.focus.tsv")
    
    f1 <- califocusPIP_plot(phenofs, focusfs)
    f2 <- ncausalfocusPIP_plot(phenofs, focusfs)
    gridExtra::grid.arrange(f1, f2, ncol=2)
}

plot_smr <- function(configtag, runtag,  simutags){
    phenofs <- paste0(outputdir, runtag, "_simu", simutags, "-pheno.Rd")
    smrfs <- paste0(comparedir, runtag, "_simu", simutags, ".Adipose_Subcutaneous.smr")
    
    f1 <- caliSMRp_plot(phenofs, smrfs)
    f2 <- ncausalSMRp_plot(phenofs, smrfs)
    gridExtra::grid.arrange(f1, f2, ncol=2)
}

configtag <- 1
runtag = "ukb-s80.45-adi"

simutags <- paste(1, c(1:5), sep = "-")
plot_par(configtag, runtag, simutags)

simulations  1-1 1-2 1-3 1-4 1-5 : mean gene PVE: 0.1006508 , mean SNP PVE: 0.5042172 

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plot_PIP(configtag, runtag, simutags)

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plot_fusion_coloc(configtag, runtag, simutags)

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plot_focus(configtag, runtag, simutags)

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plot_smr(configtag, runtag, simutags)

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simutags <- paste(2, c(1:5), sep = "-")
plot_par(configtag, runtag, simutags)

simulations  2-1 2-2 2-3 2-4 2-5 : mean gene PVE: 0.09102239 , mean SNP PVE: 0.5096063 

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plot_PIP(configtag, runtag, simutags)

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plot_fusion_coloc(configtag, runtag, simutags)

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plot_focus(configtag, runtag, simutags)

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plot_smr(configtag, runtag, simutags)

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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: 0.05043839 , mean SNP PVE: 0.5049601 

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plot_PIP(configtag, runtag, simutags)

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plot_fusion_coloc(configtag, runtag, simutags)

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plot_focus(configtag, runtag, simutags)

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plot_smr(configtag, runtag, simutags)

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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: 0.04530086 , mean SNP PVE: 0.5069989 

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plot_PIP(configtag, runtag, simutags)

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plot_fusion_coloc(configtag, runtag, simutags)

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plot_focus(configtag, runtag, simutags)

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plot_smr(configtag, runtag, simutags)

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simutags <- paste(5, c(1, 3:5), sep = "-")
plot_par(configtag, runtag, simutags)

simulations  5-1 5-3 5-4 5-5 : mean gene PVE: 0.1995579 , mean SNP PVE: 0.5033148 

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plot_PIP(configtag, runtag, simutags)

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plot_fusion_coloc(configtag, runtag, simutags)

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plot_focus(configtag, runtag, simutags)

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plot_smr(configtag, runtag, simutags)

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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: 0.1836896 , mean SNP PVE: 0.5147486 

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plot_PIP(configtag, runtag, simutags)

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plot_fusion_coloc(configtag, runtag, simutags)

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plot_focus(configtag, runtag, simutags)

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plot_smr(configtag, runtag, simutags)

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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: 0.1035296 , mean SNP PVE: 0.2997482 

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plot_PIP(configtag, runtag, simutags)

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plot_fusion_coloc(configtag, runtag, simutags)

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plot_focus(configtag, runtag, simutags)

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plot_smr(configtag, runtag, simutags)

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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: 0.09977568 , mean SNP PVE: 0.3049247 

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plot_PIP(configtag, runtag, simutags)

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plot_fusion_coloc(configtag, runtag, simutags)

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plot_focus(configtag, runtag, simutags)

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plot_smr(configtag, runtag, simutags)

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fe0e8f8	simingz	2021-05-09

simutags <- paste(9, 1:4, sep = "-")
plot_par(configtag, runtag, simutags)

simulations  9-1 9-2 9-3 9-4 : mean gene PVE: 0.0197939 , mean SNP PVE: 0.5055757 

Past versions of param2-57.png

Version	Author	Date
fe0e8f8	simingz	2021-05-09

plot_PIP(configtag, runtag, simutags)

Past versions of param2-58.png

Version	Author	Date
fe0e8f8	simingz	2021-05-09

plot_fusion_coloc(configtag, runtag, simutags)

Past versions of param2-59.png

Version	Author	Date
fe0e8f8	simingz	2021-05-09

Past versions of param2-60.png

Version	Author	Date
fe0e8f8	simingz	2021-05-09

plot_focus(configtag, runtag, simutags)

plot_smr(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: 0.01806684 , mean SNP PVE: 0.5053575 

plot_PIP(configtag, runtag, simutags)

plot_fusion_coloc(configtag, runtag, simutags)

plot_focus(configtag, runtag, simutags)

plot_smr(configtag, runtag, simutags)

Session information

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     stringr_1.4.0    
 [5] plyr_1.8.4        tidyr_1.1.0       plotly_4.9.0      ggplot2_3.3.3    
 [9] data.table_1.13.2 ctwas_0.1.28     

loaded via a namespace (and not attached):
 [1] segmented_1.0-0   fs_1.3.1          httr_1.4.2        rprojroot_1.3-2  
 [5] ggsci_2.9         tools_3.6.1       backports_1.1.4   utf8_1.1.4       
 [9] R6_2.4.0          DBI_1.1.0         lazyeval_0.2.2    colorspace_1.4-1 
[13] withr_2.4.1       tidyselect_1.1.0  gridExtra_2.3     curl_3.3         
[17] compiler_3.6.1    git2r_0.26.1      logging_0.10-108  labeling_0.3     
[21] scales_1.1.0      digest_0.6.20     foreign_0.8-71    mixtools_1.2.0   
[25] rmarkdown_2.9     R.utils_2.9.0     rio_0.5.16        pkgconfig_2.0.2  
[29] htmltools_0.3.6   highr_0.8         htmlwidgets_1.3   rlang_0.4.10     
[33] readxl_1.3.1      generics_0.0.2    farver_2.1.0      jsonlite_1.6     
[37] dplyr_1.0.5       zip_2.0.3         car_3.0-5         R.oo_1.22.0      
[41] magrittr_1.5      Matrix_1.2-18     Rcpp_1.0.5        munsell_0.5.0    
[45] fansi_0.4.0       abind_1.4-5       lifecycle_1.0.0   R.methodsS3_1.7.1
[49] stringi_1.4.3     whisker_0.3-2     yaml_2.2.0        carData_3.0-2    
[53] debugme_1.1.0     MASS_7.3-51.4     grid_3.6.1        promises_1.0.1   
[57] forcats_0.4.0     crayon_1.3.4      lattice_0.20-38   haven_2.3.1      
[61] splines_3.6.1     hms_0.5.3         knitr_1.33        pillar_1.5.1     
[65] ggsignif_0.5.0    codetools_0.2-16  glue_1.4.2        evaluate_0.14    
[69] vctrs_0.3.7       httpuv_1.6.1      foreach_1.4.4     cellranger_1.1.0 
[73] gtable_0.3.0      purrr_0.3.4       pgenlibr_0.2      kernlab_0.9-27   
[77] xfun_0.24         openxlsx_4.1.0.1  broom_0.7.5       rstatix_0.7.0    
[81] later_0.8.0       survival_2.44-1.1 viridisLite_0.3.0 tibble_3.1.0     
[85] iterators_1.0.10  workflowr_1.6.2   ellipsis_0.2.0.1