Last updated: 2020-08-19

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File	Version	Author	Date	Message
Rmd	d299f60	simingz	2020-08-19	susieI
html	d299f60	simingz	2020-08-19	susieI
Rmd	846fb96	simingz	2020-08-14	susieI
html	846fb96	simingz	2020-08-14	susieI
Rmd	fd909a1	simingz	2020-08-14	susieI

Test susieI for 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.

susieI is an iterative version of susie, learning parameters (prior weights) iteratively using the top region from mr.ash for genes only.

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

simdatadir <- "~/causalTWAS/simulations/simulation_ashtest_20200721/"
outputdir <- "~/causalTWAS/simulations/simulation_susieI_20200813/"
susiedir <- "~/causalTWAS/simulations/simulation_susieI_20200813/"
tag <- '20200813-1-1'
Niter <- 10

MR.ASH pip distribution

Use mr.ash for gene only:

rpipf <- paste0(outputdir, tag, "-mr.ash.rPIP.txt")

a <- read.table(rpipf, header = T)
a.c <- a[a$nCausal > 0, ]
ax <- pretty(0:max(a$rPIP), n = 30)

par(mfrow=c(2,1))
h1 <- hist(a$rPIP, breaks = 100, xlab = "PIP", main = "PIP distribution-all", col = "grey"); grid()
h2 <- hist(a.c$rPIP, breaks = h1$breaks, xlab = "PIP", main = "PIP distribution-causal", col = "salmon");grid()

Version	Author	Date
d299f60	simingz	2020-08-19
846fb96	simingz	2020-08-14

MR.ash2s results for the same simulated data

rpipf <- "~/causalTWAS/simulations/simulation_ashtest_20200721/20200721-1-1-mr.ash2s.lassoes-es.rPIP.txt"

a <- read.table(rpipf, header = T)
a.c <- a[a$nCausal > 0 ,]
ax <- pretty(0:max(a$rPIP), n = 30) # Make a neat vector for the breakpoints

par(mfrow=c(2,1))
h1 <- hist(a$rPIP, breaks = 100, xlab = "PIP", main = "PIP distribution-all", col = "grey"); grid()
h2 <- hist(a.c$rPIP, breaks = h1$breaks, xlab = "PIP", main = "PIP distribution-causal", col = "salmon");grid()

Version	Author	Date
d299f60	simingz	2020-08-19
846fb96	simingz	2020-08-14

susieI genes

Regions: We use regional PIP of both SNP and gene from mr.ash2s > 0.3 , and also require at least one causal gene or SNP is present, also, at least one gene.

Prior change for each iteration:

load(paste0(outputdir, tag, ".susieIres.Rd"))
df <- cbind(prior.gene_rec, prior.SNP_rec)
rownames(df) <- paste("Iteration", 1:Niter)
colnames(df) <- c("Prior.gene", "Prior.SNP")
df

               Prior.gene Prior.SNP
Iteration 1  4.287261e-02 0.9571274
Iteration 2  6.954929e-03 0.9930451
Iteration 3  1.351683e-03 0.9986483
Iteration 4  2.713631e-04 0.9997286
Iteration 5  5.482880e-05 0.9999452
Iteration 6  1.109243e-05 0.9999889
Iteration 7  2.244698e-06 0.9999978
Iteration 8  4.542679e-07 0.9999995
Iteration 9  9.193288e-08 0.9999999
Iteration 10 1.860504e-08 1.0000000

Results for each region:

regionlist <- readRDS(paste0(outputdir, tag, "_regionlist.rds"))
ng <- unlist(lapply(lapply(regionlist, '[[', "anno"), function(x) dim(x[x$type == "gene", , drop = F ])[1]))
ng.c <- unlist(lapply(lapply(regionlist, '[[', "anno"), function(x) dim(x[x$type == "gene" & x$ifcausal == 1, , drop = F])[1]))
ns.c <- unlist(lapply(lapply(regionlist, '[[', "anno"), function(x) dim(x[x$type == "SNP" & x$ifcausal == 1, , drop = F])[1]))
ngdf <- data.frame("ngene" = ng, "ncausalgene" = ng.c, "ncausalSNP" = ns.c)
for (i in 1:Niter){
  resf <- paste0(outputdir, tag, ".", i, ".susieIres.Rd") 
  load(resf)
  ngdf[, paste("Prior.gene", i)] <- unlist(gene.rpiplist)
}
ngdf

   ngene ncausalgene ncausalSNP Prior.gene 1 Prior.gene 2 Prior.gene 3
1      1           1          2 1.121460e-03 5.028411e-05 7.862516e-06
2      1           0          1 1.399491e-02 6.352934e-04 9.938449e-05
3      7           1          0 8.045843e-02 3.903998e-03 6.124289e-04
4      4           0          2 5.787296e-03 2.600733e-04 4.066915e-05
5      7           0          1 1.531458e-02 6.954553e-04 1.087977e-04
6     10           1          1 8.959135e-01 2.740352e-01 5.524818e-02
7      4           0          1 1.447474e-03 6.489746e-05 1.014746e-05
8      1           0          1 1.064940e-04 4.770425e-06 7.458842e-07
10     6           1          0 6.342556e-02 3.023811e-03 4.739961e-04
11    11           2          1 3.533597e-02 1.620890e-03 2.536763e-04
12     2           0          2 1.244325e-05 5.573741e-07 8.714860e-08
13    12           2          0 7.923951e-02 3.814249e-03 5.981279e-04
14     7           0          2 2.726904e-03 1.224366e-04 1.914545e-05
18     2           1          0 7.112726e-02 3.410262e-03 5.346999e-04
19     2           1          0 1.457055e-02 6.615222e-04 1.034883e-04
22     1           0          1 4.761905e-03 2.142747e-04 3.350911e-05
24     6           0          1 2.882048e-02 1.308218e-03 2.046617e-04
25     4           1          0 2.395151e-02 1.097919e-03 1.718245e-04
26     4           0          1 5.466104e-07 2.448433e-08 3.828263e-09
27     2           0          1 2.910038e-02 1.337827e-03 2.093938e-04
28     4           0          1 1.818182e-02 8.288126e-04 1.296802e-04
29     4           0          1 1.077585e-02 4.869235e-04 7.615968e-05
30     3           0          1 1.047407e-02 4.738295e-04 7.411505e-05
31     4           1          0 1.913876e-02 8.732467e-04 1.366377e-04
32     7           1          0 8.049348e-02 3.905484e-03 6.126605e-04
33     2           0          2 3.224433e-02 1.488844e-03 2.330731e-04
34     9           0          1 6.567384e-07 2.941730e-08 4.599560e-09
35     8           0          1 3.555556e-02 1.648633e-03 2.581320e-04
36     6           0          1 5.595469e-05 2.506448e-06 3.918976e-07
37    11           0          1 5.729167e-02 2.714837e-03 4.254544e-04
39     1           0          1 7.830050e-05 3.507492e-06 5.484171e-07
43     1           0          2 2.392592e-03 1.073443e-04 1.678496e-05
44     3           0          1 3.994818e-03 1.794867e-04 2.806715e-05
46     4           0          1 2.743553e-11 1.228920e-12 1.921485e-13
47     4           1          0 2.905861e-02 1.322857e-03 2.069573e-04
53     5           0          3 4.122229e-03 1.851506e-04 2.895246e-05
54     3           0          1 2.459016e-02 1.127962e-03 1.765311e-04
55     6           0          1 1.090818e-02 4.921150e-04 7.696678e-05
56     2           0          1 1.048977e-02 4.737297e-04 7.409437e-05
57    10           0          2 4.586726e-02 2.148023e-03 3.364609e-04
58     5           0          1 2.272727e-02 1.040614e-03 1.628488e-04
59    14           0          1 1.011921e-01 5.008892e-03 7.864291e-04
60     4           0          1 2.424242e-02 1.111633e-03 1.739732e-04
61     4           0          1 6.507764e-03 2.921321e-04 4.568042e-05
62     3           0          2 2.202772e-03 9.882051e-05 1.545208e-05
63     3           0          1 1.002353e-02 4.511915e-04 7.056011e-05
64    12           1          0 8.518575e-02 4.153127e-03 6.516437e-04
   Prior.gene 4 Prior.gene 5 Prior.gene 6 Prior.gene 7 Prior.gene 8
1  1.519507e-06 3.047261e-07 6.155645e-08 1.245296e-08 2.519997e-09
2  1.920842e-05 3.852164e-06 7.781618e-07 1.574234e-07 3.185639e-08
3  1.184156e-04 2.374961e-05 4.797649e-06 9.705751e-07 1.964070e-07
4  7.859809e-06 1.576232e-06 3.184082e-07 6.441445e-08 1.303499e-08
5  2.102779e-05 4.217033e-06 8.518678e-07 1.723342e-07 3.487377e-08
6  1.115319e-02 2.255889e-03 4.564865e-04 9.237997e-05 1.869544e-05
7  1.961093e-06 3.932830e-07 7.944546e-08 1.607193e-08 3.252337e-09
8  1.441485e-07 2.890790e-08 5.839564e-09 1.181352e-09 2.390599e-10
10 9.163871e-05 1.837880e-05 3.712679e-06 7.510826e-07 1.519901e-07
11 4.903205e-05 9.833281e-06 1.986391e-06 4.018501e-07 8.131892e-08
12 1.684220e-08 3.377578e-09 6.822903e-10 1.380283e-10 2.793159e-11
13 1.156439e-04 2.319348e-05 4.685296e-06 9.478453e-07 1.918073e-07
14 3.700071e-06 7.420238e-07 1.498932e-07 3.032361e-08 6.136324e-09
18 1.033783e-04 2.073341e-05 4.188336e-06 8.473092e-07 1.714627e-07
19 2.000159e-05 4.011232e-06 8.102946e-07 1.639239e-07 3.317185e-08
22 6.476093e-06 1.298739e-06 2.623531e-07 5.307442e-08 1.074021e-08
24 3.955591e-05 7.932771e-06 1.602471e-06 3.241824e-07 6.560197e-08
25 3.321113e-05 6.660421e-06 1.345451e-06 2.721869e-07 5.508010e-08
26 7.398441e-10 1.483702e-10 2.997164e-11 6.063307e-12 1.226978e-12
27 4.047340e-05 8.116880e-06 1.639667e-06 3.317073e-07 6.712473e-08
28 2.506441e-05 5.026582e-06 1.015403e-06 2.054176e-07 4.156858e-08
29 1.471927e-05 2.951868e-06 5.962957e-07 1.206315e-07 2.441115e-08
30 1.432420e-05 2.872644e-06 5.802921e-07 1.173940e-07 2.375600e-08
31 2.640930e-05 5.296300e-06 1.069888e-06 2.164400e-07 4.379908e-08
32 1.184603e-04 2.375857e-05 4.799460e-06 9.709415e-07 1.964811e-07
33 4.505156e-05 9.035071e-06 1.825149e-06 3.692309e-07 7.471805e-08
34 8.889038e-10 1.782630e-10 3.601017e-11 7.284909e-12 1.474183e-12
35 4.989658e-05 1.000679e-05 2.021445e-06 4.089419e-07 8.275403e-08
36 7.573753e-08 1.518860e-08 3.068185e-09 6.206982e-10 1.256053e-10
37 8.225088e-05 1.649589e-05 3.332308e-06 6.741326e-07 1.364184e-07
39 1.059863e-07 2.125476e-08 4.293584e-09 8.685983e-10 1.757707e-10
43 3.243865e-06 6.505342e-07 1.314117e-07 2.658478e-08 5.379731e-09
44 5.424311e-06 1.087809e-06 2.197439e-07 4.445451e-08 8.995871e-09
46 3.713433e-14 7.447012e-15 1.504340e-15 3.043301e-16 6.158462e-17
47 3.999972e-05 8.021780e-06 1.620451e-06 3.278199e-07 6.633806e-08
53 5.595398e-06 1.122119e-06 2.266746e-07 4.585662e-08 9.279603e-09
54 3.412099e-05 6.842897e-06 1.382312e-06 2.796441e-07 5.658915e-08
55 1.487511e-05 2.983117e-06 6.026078e-07 1.219085e-07 2.466956e-08
56 1.432006e-05 2.871807e-06 5.801228e-07 1.173597e-07 2.374907e-08
57 6.504144e-05 1.304425e-05 2.635041e-06 5.330738e-07 1.078735e-07
58 3.147604e-05 6.312444e-06 1.275157e-06 2.579663e-07 5.220240e-08
59 1.520787e-04 3.050189e-05 6.161706e-06 1.246528e-06 2.522492e-07
60 3.362650e-05 6.743726e-06 1.362279e-06 2.755913e-07 5.576902e-08
61 8.828240e-06 1.770442e-06 3.576398e-07 7.235105e-08 1.464105e-08
62 2.986272e-06 5.988757e-07 1.209764e-07 2.447370e-08 4.952530e-09
63 1.363673e-05 2.734761e-06 5.524381e-07 1.117591e-07 2.261571e-08
64 1.260018e-04 2.527125e-05 5.105043e-06 1.032762e-06 2.089911e-07
   Prior.gene 9 Prior.gene 10
1  5.099802e-10  1.032077e-10
2  6.446886e-09  1.304694e-09
3  3.974754e-08  8.043942e-09
4  2.637935e-09  5.338542e-10
5  7.057523e-09  1.428272e-09
6  3.783513e-06  7.656938e-07
7  6.581863e-10  1.332010e-10
8  4.837936e-11  9.790813e-12
10 3.075875e-08  6.224828e-09
11 1.645678e-08  3.330455e-09
12 5.652610e-12  1.143951e-12
13 3.881670e-08  7.855562e-09
14 1.241829e-09  2.513161e-10
18 3.469948e-08  7.022337e-09
19 6.713099e-09  1.358569e-09
22 2.173532e-09  4.398703e-10
24 1.327609e-08  2.686760e-09
25 1.114675e-08  2.255832e-09
26 2.483078e-13  5.025149e-14
27 1.358426e-08  2.749126e-09
28 8.412373e-09  1.702461e-09
29 4.940167e-09  9.997704e-10
30 4.807581e-09  9.729382e-10
31 8.863769e-09  1.793812e-09
32 3.976255e-08  8.046979e-09
33 1.512095e-08  3.060114e-09
34 2.983355e-13  6.037589e-14
35 1.674721e-08  3.389231e-09
36 2.541916e-11  5.144224e-12
37 2.760746e-08  5.587082e-09
39 3.557130e-11  7.198771e-12
43 1.088714e-09  2.203294e-10
44 1.820525e-09  3.684302e-10
46 1.246309e-17  2.522228e-18
47 1.342506e-08  2.716907e-09
53 1.877945e-09  3.800506e-10
54 1.145214e-08  2.317636e-09
55 4.992461e-09  1.010353e-09
56 4.806179e-09  9.726544e-10
57 2.183073e-08  4.418013e-09
58 1.056438e-08  2.137974e-09
59 5.104854e-08  1.033099e-08
60 1.128617e-08  2.284047e-09
61 2.962959e-09  5.996312e-10
62 1.002260e-09  2.028332e-10
63 4.576817e-09  9.262371e-10
64 4.229425e-08  8.559334e-09

susieI shuffle genes

Shuffle genes to different regions.

Prior change for each iteration:

tag <- '20200813-1-1-shuffleg'
load(paste0(outputdir, tag, ".susieIres.Rd"))
df <- cbind(prior.gene_rec, prior.SNP_rec)
rownames(df) <- paste("Iteration", 1:Niter)
colnames(df) <- c("Prior.gene", "Prior.SNP")
df

             Prior.gene Prior.SNP
Iteration 1  0.06123103 0.9387690
Iteration 2  0.02398287 0.9760171
Iteration 3  0.02227873 0.9777213
Iteration 4  0.02220286 0.9777971
Iteration 5  0.02219948 0.9778005
Iteration 6  0.02219933 0.9778007
Iteration 7  0.02219933 0.9778007
Iteration 8  0.02219933 0.9778007
Iteration 9  0.02219933 0.9778007
Iteration 10 0.02219933 0.9778007

Results for each region:

regionlist <- readRDS(paste0(outputdir, tag, "_regionlist.rds"))
ng <- unlist(lapply(lapply(regionlist, '[[', "anno"), function(x) dim(x[x$type == "gene", , drop = F ])[1]))
ng.c <- unlist(lapply(lapply(regionlist, '[[', "anno"), function(x) dim(x[x$type == "gene" & x$ifcausal == 1, , drop = F])[1]))
ns.c <- unlist(lapply(lapply(regionlist, '[[', "anno"), function(x) dim(x[x$type == "SNP" & x$ifcausal == 1, , drop = F])[1]))
ngdf <- data.frame("ngene" = ng, "ncausalgene" = ng.c, "ncausalSNP" = ns.c)
for (i in 1:Niter){
  resf <- paste0(outputdir, tag, ".", i, ".susieIres.Rd") 
  load(resf)
  ngdf[, paste("Prior.gene", i)] <- unlist(gene.rpiplist)
}
ngdf

   ngene ncausalgene ncausalSNP Prior.gene 1 Prior.gene 2 Prior.gene 3
1      7           1          2 3.640772e-06 2.374692e-07 8.946192e-08
2      2           0          1 5.172067e-01 5.068131e-02 1.901263e-02
3     12           1          0 5.134364e-02 3.488501e-03 1.316602e-03
4     12           2          2 3.972331e-02 2.676677e-03 1.009835e-03
5      4           1          1 1.072053e-03 6.997004e-05 2.636060e-05
6      7           0          1 3.428273e-04 2.236700e-05 8.426433e-06
7      1           0          1 1.048032e-04 6.836104e-06 2.575375e-06
8      4           0          1 4.568247e-04 2.980264e-05 1.122767e-05
10     4           0          0 8.644879e-03 5.665639e-04 2.134865e-04
11     3           0          1 4.677641e-02 3.190439e-03 1.204330e-03
12     5           0          2 6.213836e-05 4.053107e-06 1.526931e-06
13     6           0          0 3.045126e-02 2.040523e-03 7.696418e-04
14     1           1          2 4.180617e-02 2.837295e-03 1.070782e-03
18     8           0          0 2.025237e-03 1.322451e-04 4.982321e-05
19     9           0          0 2.453778e-01 1.945630e-02 7.386114e-03
22     3           0          1 1.415094e-02 9.353656e-04 3.525864e-04
24     6           0          1 2.882048e-02 1.904373e-03 7.178662e-04
25     2           1          0 1.978178e-02 1.295218e-03 4.880060e-04
26     6           1          1 8.219039e-06 5.360880e-07 2.019608e-07
27     6           0          1 1.841520e-02 1.211739e-03 4.566756e-04
28     4           0          1 1.818182e-02 1.206410e-03 4.548334e-04
29     4           1          1 7.903698e-03 5.184353e-04 1.953585e-04
30    11           2          1 2.700498e-02 1.800193e-03 6.788365e-04
31     2           0          0 9.661836e-03 6.359350e-04 2.396711e-04
32     4           0          0 6.902375e-03 4.524640e-04 1.704942e-04
33    14           0          2 2.722003e-03 1.778702e-04 6.701451e-05
34     4           0          1 6.669268e-09 4.350017e-10 1.638784e-10
35     1           0          1 4.587156e-03 3.004849e-04 1.132230e-04
36     1           0          1 5.745820e-05 3.747834e-06 1.411926e-06
37     1           0          1 5.494505e-03 3.602283e-04 1.357395e-04
39     4           0          1 6.290717e-05 4.103240e-06 1.545818e-06
43     4           0          2 1.703180e-02 1.127440e-03 4.250160e-04
44     3           0          1 4.462480e-04 2.911312e-05 1.096791e-05
46     3           0          1 5.181058e-11 3.379335e-12 1.273099e-12
47     1           0          0 6.821373e-03 4.476233e-04 1.686778e-04
53     3           0          3 1.090432e-03 7.116088e-05 2.680909e-05
54     2           0          1 1.652893e-02 1.095015e-03 4.128073e-04
55     5           0          1 7.515356e-03 4.922952e-04 1.854973e-04
56     4           1          1 6.822762e-03 4.463276e-04 1.681669e-04
57    11           0          2 2.857178e-02 1.902394e-03 7.173381e-04
58    10           1          1 9.999774e-01 9.996534e-01 9.990802e-01
59     7           1          1 1.749496e-01 1.230379e-02 4.641869e-03
60     2           1          1 2.687543e-01 8.095902e-04 3.051513e-04
61     2           0          1 2.204919e-02 1.467468e-03 5.533302e-04
62    10           0          2 8.621726e-03 5.659487e-04 2.132695e-04
63     7           0          1 1.519468e-01 9.621419e-03 3.600205e-03
64     4           0          0 1.757789e-02 1.151651e-03 4.339492e-04
   Prior.gene 4 Prior.gene 5 Prior.gene 6 Prior.gene 7 Prior.gene 8
1  8.296021e-08 8.267128e-08 8.265842e-08 8.265785e-08 8.265782e-08
2  1.762498e-02 1.756333e-02 1.756059e-02 1.756047e-02 1.756046e-02
3  1.221014e-03 1.216766e-03 1.216577e-03 1.216568e-03 1.216568e-03
4  9.365040e-04 9.332450e-04 9.331000e-04 9.330935e-04 9.330932e-04
5  2.444485e-05 2.435972e-05 2.435593e-05 2.435576e-05 2.435575e-05
6  7.814040e-06 7.786826e-06 7.785615e-06 7.785561e-06 7.785559e-06
7  2.388208e-06 2.379890e-06 2.379520e-06 2.379504e-06 2.379503e-06
8  1.041169e-05 1.037543e-05 1.037382e-05 1.037375e-05 1.037374e-05
10 1.979730e-04 1.972836e-04 1.972529e-04 1.972516e-04 1.972515e-04
11 1.116902e-03 1.113016e-03 1.112843e-03 1.112836e-03 1.112835e-03
12 1.415961e-06 1.411029e-06 1.410810e-06 1.410800e-06 1.410800e-06
13 7.137449e-04 7.112607e-04 7.111502e-04 7.111453e-04 7.111450e-04
14 9.930394e-04 9.895843e-04 9.894306e-04 9.894237e-04 9.894234e-04
18 4.620237e-05 4.604147e-05 4.603430e-05 4.603399e-05 4.603397e-05
19 6.851600e-03 6.827838e-03 6.826781e-03 6.826734e-03 6.826732e-03
22 3.269703e-04 3.258319e-04 3.257812e-04 3.257790e-04 3.257789e-04
24 6.657124e-04 6.633947e-04 6.632915e-04 6.632869e-04 6.632867e-04
25 4.525421e-04 4.509661e-04 4.508960e-04 4.508929e-04 4.508927e-04
26 1.872831e-07 1.866309e-07 1.866018e-07 1.866005e-07 1.866005e-07
27 4.234936e-04 4.220190e-04 4.219534e-04 4.219504e-04 4.219503e-04
28 4.217920e-04 4.203236e-04 4.202583e-04 4.202554e-04 4.202552e-04
29 1.811626e-04 1.805318e-04 1.805037e-04 1.805024e-04 1.805024e-04
30 6.295280e-04 6.273367e-04 6.272392e-04 6.272349e-04 6.272347e-04
31 2.222567e-04 2.214828e-04 2.214484e-04 2.214469e-04 2.214468e-04
32 1.581049e-04 1.575543e-04 1.575298e-04 1.575288e-04 1.575287e-04
33 6.214440e-05 6.192798e-05 6.191834e-05 6.191792e-05 6.191790e-05
34 1.519685e-10 1.514392e-10 1.514156e-10 1.514146e-10 1.514145e-10
35 1.049953e-04 1.046297e-04 1.046134e-04 1.046127e-04 1.046127e-04
36 1.309313e-06 1.304753e-06 1.304550e-06 1.304541e-06 1.304541e-06
37 1.258757e-04 1.254374e-04 1.254179e-04 1.254170e-04 1.254170e-04
39 1.433474e-06 1.428482e-06 1.428260e-06 1.428250e-06 1.428250e-06
43 3.941388e-04 3.927667e-04 3.927056e-04 3.927029e-04 3.927027e-04
44 1.017082e-05 1.013539e-05 1.013382e-05 1.013375e-05 1.013374e-05
46 1.180575e-12 1.176464e-12 1.176281e-12 1.176273e-12 1.176272e-12
47 1.564208e-04 1.558761e-04 1.558519e-04 1.558508e-04 1.558508e-04
53 2.486075e-05 2.477417e-05 2.477031e-05 2.477014e-05 2.477014e-05
54 3.828177e-04 3.814850e-04 3.814257e-04 3.814230e-04 3.814229e-04
55 1.720176e-04 1.714186e-04 1.713919e-04 1.713907e-04 1.713907e-04
56 1.559461e-04 1.554031e-04 1.553789e-04 1.553778e-04 1.553778e-04
57 6.652314e-04 6.629158e-04 6.628127e-04 6.628081e-04 6.628079e-04
58 9.990082e-01 9.990047e-01 9.990046e-01 9.990045e-01 9.990045e-01
59 4.304767e-03 4.289786e-03 4.289119e-03 4.289089e-03 4.289088e-03
60 2.829805e-04 2.819952e-04 2.819514e-04 2.819494e-04 2.819493e-04
61 5.131365e-04 5.113503e-04 5.112708e-04 5.112673e-04 5.112671e-04
62 1.977724e-04 1.970837e-04 1.970530e-04 1.970517e-04 1.970516e-04
63 3.337509e-03 3.325839e-03 3.325320e-03 3.325297e-03 3.325296e-03
64 4.024152e-04 4.010138e-04 4.009515e-04 4.009487e-04 4.009486e-04
   Prior.gene 9 Prior.gene 10
1  8.265782e-08  8.265782e-08
2  1.756046e-02  1.756046e-02
3  1.216568e-03  1.216568e-03
4  9.330932e-04  9.330932e-04
5  2.435575e-05  2.435575e-05
6  7.785559e-06  7.785559e-06
7  2.379503e-06  2.379503e-06
8  1.037374e-05  1.037374e-05
10 1.972515e-04  1.972515e-04
11 1.112835e-03  1.112835e-03
12 1.410800e-06  1.410800e-06
13 7.111450e-04  7.111450e-04
14 9.894234e-04  9.894234e-04
18 4.603397e-05  4.603397e-05
19 6.826732e-03  6.826732e-03
22 3.257789e-04  3.257789e-04
24 6.632867e-04  6.632867e-04
25 4.508927e-04  4.508927e-04
26 1.866005e-07  1.866005e-07
27 4.219503e-04  4.219503e-04
28 4.202552e-04  4.202552e-04
29 1.805024e-04  1.805024e-04
30 6.272347e-04  6.272347e-04
31 2.214468e-04  2.214468e-04
32 1.575287e-04  1.575287e-04
33 6.191790e-05  6.191790e-05
34 1.514145e-10  1.514145e-10
35 1.046127e-04  1.046127e-04
36 1.304541e-06  1.304541e-06
37 1.254170e-04  1.254170e-04
39 1.428250e-06  1.428250e-06
43 3.927027e-04  3.927027e-04
44 1.013374e-05  1.013374e-05
46 1.176272e-12  1.176272e-12
47 1.558508e-04  1.558508e-04
53 2.477013e-05  2.477013e-05
54 3.814229e-04  3.814229e-04
55 1.713907e-04  1.713907e-04
56 1.553778e-04  1.553778e-04
57 6.628079e-04  6.628079e-04
58 9.990045e-01  9.990045e-01
59 4.289088e-03  4.289088e-03
60 2.819493e-04  2.819493e-04
61 5.112671e-04  5.112671e-04
62 1.970516e-04  1.970516e-04
63 3.325296e-03  3.325296e-03
64 4.009486e-04  4.009486e-04

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_1.1.0    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] tidyselect_1.1.0  purrr_0.3.4       lattice_0.20-38  
 [4] colorspace_1.3-2  vctrs_0.3.1       generics_0.0.2   
 [7] htmltools_0.3.6   viridisLite_0.3.0 yaml_2.2.0       
[10] rlang_0.4.6       later_0.7.5       pillar_1.4.4     
[13] glue_1.4.1        withr_2.1.2       lifecycle_0.2.0  
[16] munsell_0.5.0     gtable_0.2.0      workflowr_1.6.2  
[19] rvest_0.3.2       htmlwidgets_1.3   evaluate_0.12    
[22] knitr_1.20        httpuv_1.4.5      Rcpp_1.0.4.6     
[25] readr_1.3.1       promises_1.0.1    scales_1.0.0     
[28] backports_1.1.2   webshot_0.5.1     jsonlite_1.6.1   
[31] fs_1.3.1          hms_0.4.2         digest_0.6.25    
[34] stringi_1.3.1     dplyr_1.0.0       grid_3.5.1       
[37] rprojroot_1.3-2   tools_3.5.1       magrittr_1.5     
[40] lazyeval_0.2.1    tibble_3.0.1      crayon_1.3.4     
[43] whisker_0.3-2     pkgconfig_2.0.2   ellipsis_0.3.1   
[46] Matrix_1.2-15     xml2_1.2.0        rmarkdown_1.10   
[49] httr_1.4.1        rstudioapi_0.11   R6_2.3.0         
[52] git2r_0.26.1      compiler_3.5.1

Test susieI using chr17 to 22

MR.ASH pip distribution

susieI genes

susieI shuffle genes