Last updated: 2020-09-25

Checks: 6 1

Knit directory: causal-TWAS/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: uncommitted changes

The R Markdown file has unstaged changes. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20191103)

The command set.seed(20191103) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: f55918c

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version f55918c. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:


Ignored files:
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    code/workflow/.ipynb_checkpoints/
    Ignored:    data/

Untracked files:
    Untracked:  code/susie_filter.R

Unstaged changes:
    Modified:   analysis/simulation-multi-ukbchr17to22-gtex.adipose_susieprior.Rmd
    Modified:   code/run_UKB_process.R
    Modified:   code/run_simulate_data.R
    Modified:   code/run_test_susieI_m.R
    Modified:   code/workflow/workflow-ashtest4.ipynb

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/simulation-multi-ukbchr17to22-gtex.adipose_susieprior.Rmd) and HTML (docs/simulation-multi-ukbchr17to22-gtex.adipose_susieprior.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	f55918c	simingz	2020-09-22	susie prior
html	f55918c	simingz	2020-09-22	susie prior
Rmd	e9d56aa	simingz	2020-09-20	causal susie prior
html	e9d56aa	simingz	2020-09-20	causal susie prior
Rmd	cab7999	simingz	2020-09-19	susie cali truth
html	cab7999	simingz	2020-09-19	susie cali truth
Rmd	13cad06	simingz	2020-09-14	susieI plots
html	13cad06	simingz	2020-09-14	susieI plots
Rmd	c1c6bcc	simingz	2020-09-14	susieI plots
Rmd	4d39fd6	simingz	2020-09-14	susieI plots
html	4d39fd6	simingz	2020-09-14	susieI plots
Rmd	bb0bce0	simingz	2020-09-03	susie prior
html	bb0bce0	simingz	2020-09-03	susie prior
Rmd	193a8df	simingz	2020-09-01	increase gene pve
html	193a8df	simingz	2020-09-01	increase gene pve
Rmd	86681eb	simingz	2020-08-28	susieI all regions
html	86681eb	simingz	2020-08-28	susieI all regions

Run susie with different priors and see how much prior affects results.
Data: ukb chr 17 to chr 22 combined. SNPs are downsampled to 1/10, eQTLs defined by FUSION-TWAS (Adipose/GTEx) lasso effect size > 0 were kept, p= 86k, n = 20k.
For each simulation scenarios(1-x, 3-x), we picked two few parameter sets (priors for gene and snps) estimated by susieI and check if PIPs for genes are calibrated.

library(mr.ash.alpha)
library(data.table)
suppressMessages({library(plotly)})
library(tidyr)
library(plyr)
library(stringr)
library(kableExtra)
source("analysis/summarize_twas_plots.R")

Test true prior

Regions rPIP > 0.5, simulation 1-x

simdatadir <- "~/causalTWAS/simulations/simulation_ashtest_20200721/"
outputdir <- "~/causalTWAS/simulations/simulation_ashtest_20200721/"
susiedir <- "/home/simingz/causalTWAS/simulations/simulation_susietest_20200721/20200721-1-fixprior_rpip0.5/"

We run 50 simulations and run susie using the true parameter (gene and snp pi1) as prior. We run susie for regions with regional sum of pip from mr.ash2s for both genes and snps > 0.5. run susie with L=1.

Truth

The true parameters we used to simulate data:

par <- read.table(paste0(susiedir, "20200721-1-fixedprior_2.txt"), header = T)
t(par[,2, drop = F])

        gene.pi1    gene.pve     snp.pi1    snp.pve
truth 0.05021174 0.008497975 0.002498094 0.05003487

Use truth as prior

The prior used is:

par <- read.table(paste0(susiedir, "20200721-1-fixedprior_2.txt"), header = T)
t(par[c(1,3),1, drop = F])

            gene.pi1     snp.pi1
estimated 0.05118395 0.002274899

susiedir <- "/home/simingz/causalTWAS/simulations/simulation_susietest_20200721/20200721-1-fixprior_rpip0.5/"

susie results:

pipfs <- Sys.glob(paste0(susiedir,"20200721-1-*.fixedprior2.L1.susieres.expr.txt"))
res <- do.call(rbind, lapply(pipfs, read.table, header = T))
par(mfrow=c(1,3))
cp_plot(res$pip.null, res$ifcausal, main = "SUSIE.uniform PIP")
cp_plot(res$pip, res$ifcausal, main = "SUSIE.weighted PIP")
cp_plot(res$pip.w0, res$ifcausal, main = "SUSIE.weighted-w-null PIP")

Version	Author	Date
f55918c	simingz	2020-09-22
cab7999	simingz	2020-09-19

Regions rPIP > 0.9, simulation 3-x

We run 50 simulations and run susie using the true parameter (gene and snp pi1) as prior. We run susie for regions with regional sum of pip from mr.ash2s for both genes and snps > 0.9. run susie with L= 2.

Truth

The true parameters we used to simulate data:

susiedir <- "/home/simingz/causalTWAS/simulations/simulation_susietest_20200721/20200721-3-fixprior_rpip0.9/"
par <- read.table(paste0(susiedir, "20200721-3-fixedprior_2.txt"), header = T)
t(par[,2, drop = F])

       gene.pi1    gene.pve     snp.pi1    snp.pve
truth 0.0199637 0.007322058 0.002498094 0.05257447

Use truth as prior

The prior used is:

par <- read.table(paste0(susiedir, "20200721-3-fixedprior_2.txt"), header = T)
t(par[c(1,3),1, drop = F])

          gene.pi1 snp.pi1
estimated     0.02  0.0025

susie results:

pipfs <- Sys.glob(paste0(susiedir,"20200721-3-*.fixedprior2.susieres.expr.txt"))
res <- do.call(rbind, lapply(pipfs, read.table, header = T))
par(mfrow=c(1,3))
cp_plot(res$pip.null, res$ifcausal, main = "SUSIE.uniform PIP")
cp_plot(res$pip, res$ifcausal, main = "SUSIE.weighted PIP")
cp_plot(res$pip.w0, res$ifcausal, main = "SUSIE.weighted-w-null PIP")

Causal regions, simulation 3-x

We run susie for regions with causal signals. run susie with L=1.

Truth

The true parameters we used to simulate data:

susiedir <- "/home/simingz/causalTWAS/simulations/simulation_susietest_20200721/20200721-3-fixprior_causal/"
par <- read.table(paste0(susiedir, "20200721-3-fixedprior_2.txt"), header = T)
t(par[,2, drop = F])

       gene.pi1    gene.pve     snp.pi1    snp.pve
truth 0.0199637 0.007322058 0.002498094 0.05257447

Use truth as prior

The prior used is:

par <- read.table(paste0(susiedir, "20200721-3-fixedprior_2.txt"), header = T)
t(par[c(1,3),1, drop = F])

          gene.pi1   snp.pi1
estimated     0.02 0.0030996

susie results:

pipfs <- Sys.glob(paste0(susiedir,"20200721-3-*.fixedprior2.L1.susieres.expr.txt"))
res <- do.call(rbind, lapply(pipfs, read.table, header = T))
par(mfrow=c(1,3)); 
cp_plot(res$pip.null, res$ifcausal, main = "SUSIE.uniform PIP")
cp_plot(res$pip, res$ifcausal, main = "SUSIE.weighted PIP")
cp_plot(res$pip.w0, res$ifcausal, main = "SUSIE.weighted-w-null PIP")

Version	Author	Date
f55918c	simingz	2020-09-22
e9d56aa	simingz	2020-09-20

All regions, simulation 3-x

We run susie for all regions. Run susie with L=1.

Truth

The true parameters we used to simulate data:

susiedir <- "/project2/mstephens/causalTWAS/simulations/simulation_susietest_20200721/20200721-3-fixprior_allregions/"
par <- read.table(paste0(susiedir, "20200721-3-fixedprior_2.txt"), header = T)
t(par[,2, drop = F])

       gene.pi1    gene.pve     snp.pi1    snp.pve
truth 0.0199637 0.007322058 0.002498094 0.05257447

Use truth as prior

The prior used is:

par <- read.table(paste0(susiedir, "20200721-3-fixedprior_2.txt"), header = T)
t(par[c(1,3),1, drop = F])

          gene.pi1   snp.pi1
estimated     0.02 0.0030996

susie results:

pipfs <- Sys.glob(paste0(susiedir,"20200721-3-*.fixedprior2.L1.susieres.expr.txt"))
res <- do.call(rbind, lapply(pipfs, read.table, header = T))
par(mfrow=c(1,3)); 
cp_plot(res$pip.null, res$ifcausal, main = "SUSIE.uniform PIP")
cp_plot(res$pip, res$ifcausal, main = "SUSIE.weighted PIP")
cp_plot(res$pip.w0, res$ifcausal, main = "SUSIE.weighted-w-null PIP")

Test over and underestimated gene pi1

We intentionally use over or underestimated gene pi1 and use the same snp pi1 (close to truth) when running SUSIE. Run susie L=1.

Causal regions, simulation 3-x

The true parameters we used to simulate data:

susiedir <- "/home/simingz/causalTWAS/simulations/simulation_susietest_20200721/20200721-3-fixprior_causal/"
par <- read.table(paste0(susiedir, "20200721-3-fixedprior_2.txt"), header = T)
t(par[,2, drop = F])

       gene.pi1    gene.pve     snp.pi1    snp.pve
truth 0.0199637 0.007322058 0.002498094 0.05257447

Use overestimated gene pi1

The prior used is:

par <- read.table(paste0(susiedir, "20200721-3-fixedprior_1.txt"), header = T)
t(par[c(1,3),1, drop = F])

           gene.pi1   snp.pi1
estimated 0.1054679 0.0030996

susie results:

pipfs <- Sys.glob(paste0(susiedir,"20200721-3-*.fixedprior1.L1.susieres.expr.txt"))
res <- do.call(rbind, lapply(pipfs, read.table, header = T))
par(mfrow=c(1,3))
cp_plot(res$pip.null, res$ifcausal, main = "SUSIE.uniform PIP")
cp_plot(res$pip, res$ifcausal, main = "SUSIE.weighted PIP")
cp_plot(res$pip.w0, res$ifcausal, main = "SUSIE.weighted-w-null PIP")

Version	Author	Date
f55918c	simingz	2020-09-22

Use underrestimated gene pi1

The prior used is:

par <- read.table(paste0(susiedir, "20200721-3-fixedprior_3.txt"), header = T)
t(par[c(1,3),1, drop = F])

          gene.pi1   snp.pi1
estimated     0.01 0.0030996

susie results:

pipfs <- Sys.glob(paste0(susiedir,"20200721-3-*.fixedprior3.L1.susieres.expr.txt"))
res <- do.call(rbind, lapply(pipfs, read.table, header = T))
par(mfrow=c(1,3))
cp_plot(res$pip.null, res$ifcausal, main = "SUSIE.uniform PIP")
cp_plot(res$pip, res$ifcausal, main = "SUSIE.weighted PIP")
cp_plot(res$pip.w0, res$ifcausal, main = "SUSIE.weighted-w-null PIP")

Version	Author	Date
f55918c	simingz	2020-09-22

Test susieI results

Simulation 1-x, setting 1

simdatadir <- "~/causalTWAS/simulations/simulation_ashtest_20200721/"
outputdir <- "~/causalTWAS/simulations/simulation_ashtest_20200721/"
susiedir <- "~/causalTWAS/simulations/simulation_susietest_20200721/20200721-1-fixprior_rpip0.3_causal/"

We run 100 simulations and run susie using priors obtained from susieI. Two examples are shown. The setting is the same as in here. Regions are rpip from mr.ash2 > 0.3 and causal. We take simulation setting 1-x.

Truth

The true parameters we used to simulate data:

par <- read.table(paste0(susiedir, "20200721-1-fixedprior_1.txt"), header = T)
t(par[,2, drop = F])

        gene.pi1    gene.pve     snp.pi1    snp.pve
truth 0.05021174 0.008497975 0.002498094 0.05003487

Use prior from susie estimates 1.

The prior used is:

par <- read.table(paste0(susiedir, "20200721-1-fixedprior_1.txt"), header = T)
t(par[c(1,3),1, drop = F])

          gene.pi1  snp.pi1
estimated  0.07617 0.004203

susie results:

pipfs <- Sys.glob(paste0(susiedir,"20200721-1-*.fixedprior1.susieres.expr.txt"))
res <- do.call(rbind, lapply(pipfs, read.table, header = T))
par(mfrow=c(1,3))
cp_plot(res$pip.null, res$ifcausal, main = "SUSIE.uniform PIP")
cp_plot(res$pip, res$ifcausal, main = "SUSIE.weighted PIP")
cp_plot(res$pip.w0, res$ifcausal, main = "SUSIE.weighted-w-null PIP")

Version	Author	Date
f55918c	simingz	2020-09-22
4d39fd6	simingz	2020-09-14
bb0bce0	simingz	2020-09-03
193a8df	simingz	2020-09-01
86681eb	simingz	2020-08-28

Use prior from susie estimates 2.

The prior used is:

par <- read.table(paste0(susiedir, "20200721-1-fixedprior_2.txt"), header = T)
t(par[c(1,3),1, drop = F])

          gene.pi1 snp.pi1
estimated  0.01642 0.00577

pipfs <- Sys.glob(paste0(susiedir,"20200721-1-*.fixedprior2.susieres.expr.txt"))
res <- do.call(rbind, lapply(pipfs, read.table, header = T))
par(mfrow=c(1,3))
cp_plot(res$pip.null, res$ifcausal, main = "SUSIE.uniform PIP")
cp_plot(res$pip, res$ifcausal, main = "SUSIE.weighted PIP")
cp_plot(res$pip.w0, res$ifcausal, main = "SUSIE.weighted-w-null PIP")

Version	Author	Date
f55918c	simingz	2020-09-22
4d39fd6	simingz	2020-09-14
bb0bce0	simingz	2020-09-03
193a8df	simingz	2020-09-01
86681eb	simingz	2020-08-28

simulation 1-x, setting 2

susiedir <- "~/causalTWAS/simulations/simulation_susietest_20200721/20200721-1-fixprior_rpip0.3/"

We run 100 simulations and run susie using priors obtained from susieI. Two examples are shown. The setting is the same as in here. Regions are rpip from mr.ash2 > 0.3. We take simulation setting 1-x.

Truth

The true parameters we used to simulate data:

par <- read.table(paste0(susiedir, "20200721-1-fixedprior_1.txt"), header = T)
t(par[,2, drop = F])

        gene.pi1    gene.pve     snp.pi1    snp.pve
truth 0.05021174 0.008497975 0.002498094 0.05003487

Use prior from susie estimates 1.

The prior used is:

par <- read.table(paste0(susiedir, "20200721-1-fixedprior_1.txt"), header = T)
t(par[c(1,3),1, drop = F])

          gene.pi1   snp.pi1
estimated 0.002185 0.0007039

susie results:

pipfs <- Sys.glob(paste0(susiedir,"20200721-1-*.fixedprior1.susieres.expr.txt"))
res <- do.call(rbind, lapply(pipfs, read.table, header = T))
par(mfrow=c(1,3))
cp_plot(res$pip.null, res$ifcausal, main = "SUSIE.uniform PIP")
cp_plot(res$pip, res$ifcausal, main = "SUSIE.weighted PIP")
cp_plot(res$pip.w0, res$ifcausal, main = "SUSIE.weighted-w-null PIP")

Version	Author	Date
f55918c	simingz	2020-09-22
4d39fd6	simingz	2020-09-14
bb0bce0	simingz	2020-09-03

Use prior from susie estimates 2.

The prior used is:

par <- read.table(paste0(susiedir, "20200721-1-fixedprior_2.txt"), header = T)
t(par[c(1,3),1, drop = F])

          gene.pi1  snp.pi1
estimated  0.02611 0.008102

pipfs <- Sys.glob(paste0(susiedir,"20200721-1-*.fixedprior2.susieres.expr.txt"))
res <- do.call(rbind, lapply(pipfs, read.table, header = T))
par(mfrow=c(1,3)) 
cp_plot(res$pip.null, res$ifcausal, main = "SUSIE.uniform PIP")
cp_plot(res$pip, res$ifcausal, main = "SUSIE.weighted PIP")
cp_plot(res$pip.w0, res$ifcausal, main = "SUSIE.weighted-w-null PIP")

Version	Author	Date
f55918c	simingz	2020-09-22
4d39fd6	simingz	2020-09-14
bb0bce0	simingz	2020-09-03

Simulation 3-x, setting 1

susiedir <- "~/causalTWAS/simulations/simulation_susietest_20200721/20200721-3-fixprior_rpip0.3_causal/"

Truth

The true parameters we used to simulate data:

par <- read.table(paste0(susiedir, "20200721-3-fixedprior_1.txt"), header = T)
t(par[,2, drop = F])

       gene.pi1    gene.pve     snp.pi1    snp.pve
truth 0.0199637 0.007322058 0.002498094 0.05257447

Use prior from susie estimates 1.

The prior used is:

par <- read.table(paste0(susiedir, "20200721-3-fixedprior_1.txt"), header = T)
t(par[c(1,3),1, drop = F])

           gene.pi1   snp.pi1
estimated 0.1054679 0.0030996

susie results:

pipfs <- Sys.glob(paste0(susiedir,"20200721-3-*.fixedprior1.susieres.expr.txt"))
res <- do.call(rbind, lapply(pipfs, read.table, header = T))
par(mfrow=c(1,3))
cp_plot(res$pip.null, res$ifcausal, main = "SUSIE.uniform PIP")
cp_plot(res$pip, res$ifcausal, main = "SUSIE.weighted PIP")
cp_plot(res$pip.w0, res$ifcausal, main = "SUSIE.weighted-w-null PIP")

Version	Author	Date
f55918c	simingz	2020-09-22
4d39fd6	simingz	2020-09-14
bb0bce0	simingz	2020-09-03

Use prior from susie estimates 2.

The prior used is:

par <- read.table(paste0(susiedir, "20200721-3-fixedprior_2.txt"), header = T)
t(par[c(1,3),1, drop = F])

          gene.pi1  snp.pi1
estimated  0.05083 0.004532

susie results:

pipfs <- Sys.glob(paste0(susiedir,"20200721-3-*.fixedprior2.susieres.expr.txt"))
res <- do.call(rbind, lapply(pipfs, read.table, header = T))
par(mfrow=c(1,3))
cp_plot(res$pip.null, res$ifcausal, main = "SUSIE.uniform PIP")
cp_plot(res$pip, res$ifcausal, main = "SUSIE.weighted PIP")
cp_plot(res$pip.w0, res$ifcausal, main = "SUSIE.weighted-w-null PIP")

Version	Author	Date
f55918c	simingz	2020-09-22
4d39fd6	simingz	2020-09-14
bb0bce0	simingz	2020-09-03

Simulation 3-x, setting 2

susiedir <- "~/causalTWAS/simulations/simulation_susietest_20200721/20200721-3-fixprior_rpip0.3/"

Truth

The true parameters we used to simulate data:

par <- read.table(paste0(susiedir, "20200721-3-fixedprior_1.txt"), header = T)
t(par[,2, drop = F])

       gene.pi1    gene.pve     snp.pi1    snp.pve
truth 0.0199637 0.007322058 0.002498094 0.05257447

Use prior from susie estimates 1.

The prior used is:

par <- read.table(paste0(susiedir, "20200721-3-fixedprior_1.txt"), header = T)
t(par[c(1,3),1, drop = F])

          gene.pi1  snp.pi1
estimated  0.01602 0.004202

susie results:

pipfs <- Sys.glob(paste0(susiedir,"20200721-3-*.fixedprior2.susieres.expr.txt"))
res <- do.call(rbind, lapply(pipfs, read.table, header = T))
par(mfrow=c(1,3))
cp_plot(res$pip.null, res$ifcausal, main = "SUSIE.uniform PIP")
cp_plot(res$pip, res$ifcausal, main = "SUSIE.weighted PIP")
cp_plot(res$pip.w0, res$ifcausal, main = "SUSIE.weighted-w-null PIP")

Version	Author	Date
f55918c	simingz	2020-09-22
4d39fd6	simingz	2020-09-14

Use prior from susie estimates 2.

The prior used is:

par <- read.table(paste0(susiedir, "20200721-3-fixedprior_2.txt"), header = T)
t(par[c(1,3),1, drop = F])

          gene.pi1  snp.pi1
estimated  0.03533 0.007996

susie results:

pipfs <- Sys.glob(paste0(susiedir,"20200721-3-*.fixedprior2.susieres.expr.txt"))
res <- do.call(rbind, lapply(pipfs, read.table, header = T))
par(mfrow=c(1,3)) 
cp_plot(res$pip.null, res$ifcausal, main = "SUSIE.uniform PIP")
cp_plot(res$pip, res$ifcausal, main = "SUSIE.weighted PIP")
cp_plot(res$pip.w0, res$ifcausal, main = "SUSIE.weighted-w-null PIP")

Version	Author	Date
f55918c	simingz	2020-09-22
4d39fd6	simingz	2020-09-14

sessionInfo()

R version 3.5.1 (2018-07-02)
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] kableExtra_1.2.1    stringr_1.4.0       plyr_1.8.6         
[4] tidyr_0.8.3         plotly_4.9.2.9000   ggplot2_3.3.1      
[7] data.table_1.12.7   mr.ash.alpha_0.1-34

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.4.6      compiler_3.5.1    pillar_1.4.4     
 [4] later_0.7.5       git2r_0.26.1      workflowr_1.6.2  
 [7] tools_3.5.1       digest_0.6.25     viridisLite_0.3.0
[10] jsonlite_1.6.1    evaluate_0.12     tibble_3.0.1     
[13] lifecycle_0.2.0   gtable_0.2.0      lattice_0.20-38  
[16] pkgconfig_2.0.2   rlang_0.4.6       Matrix_1.2-15    
[19] rstudioapi_0.11   yaml_2.2.0        xml2_1.2.0       
[22] httr_1.4.1        withr_2.1.2       dplyr_1.0.0      
[25] knitr_1.20        htmlwidgets_1.3   generics_0.0.2   
[28] fs_1.3.1          vctrs_0.3.1       webshot_0.5.1    
[31] tidyselect_1.1.0  rprojroot_1.3-2   grid_3.5.1       
[34] glue_1.4.1        R6_2.3.0          rmarkdown_1.10   
[37] purrr_0.3.4       magrittr_1.5      whisker_0.3-2    
[40] backports_1.1.2   scales_1.0.0      promises_1.0.1   
[43] htmltools_0.3.6   ellipsis_0.3.1    rvest_0.3.2      
[46] colorspace_1.3-2  httpuv_1.4.5      stringi_1.3.1    
[49] lazyeval_0.2.1    munsell_0.5.0     crayon_1.3.4

Summarize twas: chr17 to 22

2020-09-25

Test true prior

Regions rPIP > 0.5, simulation 1-x

Truth

Use truth as prior

Regions rPIP > 0.9, simulation 3-x

Truth

Use truth as prior

Causal regions, simulation 3-x

Truth

Use truth as prior

All regions, simulation 3-x

Truth

Use truth as prior

Test over and underestimated gene pi1

Causal regions, simulation 3-x

Use overestimated gene pi1

Use underrestimated gene pi1

Test susieI results

Simulation 1-x, setting 1

Truth

Use prior from susie estimates 1.

Use prior from susie estimates 2.

simulation 1-x, setting 2

Truth

Use prior from susie estimates 1.

Use prior from susie estimates 2.

Simulation 3-x, setting 1

Truth

Use prior from susie estimates 1.

Use prior from susie estimates 2.

Simulation 3-x, setting 2

Truth

Use prior from susie estimates 1.

Use prior from susie estimates 2.