Last updated: 2023-01-23

Checks: 5 2

Knit directory: cTWAS_analysis/

This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.


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.

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.

The command set.seed(20211220) 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.

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

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

Using absolute paths to the files within your workflowr project makes it difficult for you and others to run your code on a different machine. Change the absolute path(s) below to the suggested relative path(s) to make your code more reproducible.

absolute relative
/project2/xinhe/shengqian/cTWAS/cTWAS_analysis/data/ data
/project2/xinhe/shengqian/cTWAS/cTWAS_analysis/code/ctwas_config_b38.R code/ctwas_config_b38.R

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 4d68754. 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:    .ipynb_checkpoints/

Untracked files:
    Untracked:  Proposal plots.R
    Untracked:  RGS14.pdf
    Untracked:  RNF186.pdf
    Untracked:  SCZ_annotation.xlsx
    Untracked:  SLC8B1.pdf
    Untracked:  analysis/.ipynb_checkpoints/
    Untracked:  cache/
    Untracked:  code/.ipynb_checkpoints/
    Untracked:  data/.ipynb_checkpoints/
    Untracked:  data/FUMA_output/
    Untracked:  data/GO_Terms/
    Untracked:  data/GTEx_Analysis_v8_eQTL.tar
    Untracked:  data/IBD_ME/
    Untracked:  data/LDL/
    Untracked:  data/LDL_E_S/
    Untracked:  data/LDL_M/
    Untracked:  data/LDL_S/
    Untracked:  data/LDL_multi/
    Untracked:  data/PGC3_SCZ_wave3_public.v2.tsv
    Untracked:  data/SCZ/
    Untracked:  data/SCZ_2014_EUR/
    Untracked:  data/SCZ_2014_EUR_ME/
    Untracked:  data/SCZ_2018/
    Untracked:  data/SCZ_2018_ME/
    Untracked:  data/SCZ_2018_S/
    Untracked:  data/SCZ_2020/
    Untracked:  data/SCZ_S/
    Untracked:  data/Supplementary Table 15 - MAGMA.xlsx
    Untracked:  data/Supplementary Table 20 - Prioritised Genes.xlsx
    Untracked:  data/UKBB/
    Untracked:  data/UKBB_SNPs_Info.text
    Untracked:  data/eqtl/
    Untracked:  data/gene_OMIM.txt
    Untracked:  data/gene_pip_0.8.txt
    Untracked:  data/gwas_sumstats/
    Untracked:  data/magma.genes.out
    Untracked:  data/mashr_Heart_Atrial_Appendage.db
    Untracked:  data/mashr_sqtl/
    Untracked:  data/mqtl/
    Untracked:  data/multigroup/
    Untracked:  data/notes.txt
    Untracked:  data/scz_2018.RDS
    Untracked:  data/summary_known_genes_annotations.xlsx
    Untracked:  temp_LDR/
    Untracked:  top_genes_32.txt
    Untracked:  top_genes_37.txt
    Untracked:  top_genes_43.txt
    Untracked:  top_genes_54.txt
    Untracked:  top_genes_81.txt
    Untracked:  z_snp_pos_SCZ.RData
    Untracked:  z_snp_pos_SCZ_2014_EUR.RData
    Untracked:  z_snp_pos_SCZ_2018.RData
    Untracked:  z_snp_pos_SCZ_2020.RData

Unstaged changes:
    Deleted:    analysis/BMI_S_results.Rmd
    Modified:   analysis/LDL_Liver_GTEX.Rmd
    Modified:   analysis/LDL_Liver_mashr.Rmd
    Modified:   analysis/LDL_Liver_mashr_lite.Rmd
    Deleted:    code/run_IBD_ctwas_rss_LDR_ME.R
    Modified:   code/run_LDL_analysis_single_test.sh
    Modified:   code/run_LDL_ctwas_rss_LDR_single_test.R

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/LDL_Liver_mashr.Rmd) and HTML (docs/LDL_Liver_mashr.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 4d68754 sq-96 2023-01-23 update
html 4d68754 sq-96 2023-01-23 update

Weight QC

[1] 11502
[1] 3520

  1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19  20 
338 238 180 136 147 231 190 122 131 131 214 195  61 110 115 193 215  50 269  99 
 21  22 
 48 107 
[1] 0.7656

Load ctwas results

Check convergence of parameters

Version Author Date
4d68754 sq-96 2023-01-23
#estimated group prior
estimated_group_prior <- estimated_group_prior_all[,ncol(group_prior_rec)]
print(estimated_group_prior)
      SNP      gene 
0.0001653 0.0235915 
#estimated group prior variance
estimated_group_prior_var <- estimated_group_prior_var_all[,ncol(group_prior_var_rec)]
print(estimated_group_prior_var)
  SNP  gene 
11.52 38.25 
#estimated enrichment
estimated_enrichment <- estimated_enrichment_all[ncol(group_prior_var_rec)]
print(estimated_enrichment)
[1] 142.8
#report sample size
print(sample_size)
[1] 343621
#report group size
print(group_size)
    SNP    gene 
8696600    3520 
#estimated group PVE
estimated_group_pve <- estimated_group_pve_all[,ncol(group_prior_rec)]
print(estimated_group_pve)
     SNP     gene 
0.048163 0.009244 
#total PVE
sum(estimated_group_pve)
[1] 0.05741
#attributable PVE
estimated_group_pve/sum(estimated_group_pve)
  SNP  gene 
0.839 0.161 

Genes with highest PIPs

#distribution of PIPs
hist(ctwas_gene_res$susie_pip, xlim=c(0,1), main="Distribution of Gene PIPs")

Version Author Date
4d68754 sq-96 2023-01-23
#genes with PIP>0.8 or 20 highest PIPs
head(ctwas_gene_res[order(-ctwas_gene_res$susie_pip),report_cols], max(sum(ctwas_gene_res$susie_pip>0.8), 20))
       genename region_tag susie_pip     mu2       PVE       z num_eqtl
4433      PSRC1       1_67    1.0000 1652.10 4.808e-03 -41.687        1
2454    ST3GAL4      11_77    1.0000  172.26 5.013e-04  13.376        2
11327       HPR      16_38    1.0000  160.58 4.673e-04 -17.963        2
3720     INSIG2       2_69    1.0000   68.08 1.981e-04  -8.983        3
5988      FADS1      11_34    0.9999  163.14 4.747e-04  12.926        2
NA.407     <NA>      1_121    0.9995  202.13 5.879e-04 -15.108        1
10612    TRIM39       6_24    0.9990   70.98 2.064e-04   8.840        3
8523       TNKS       8_12    0.9942   74.80 2.164e-04  11.039        2
1597       PLTP      20_28    0.9940   60.45 1.749e-04  -5.732        1
5542      CNIH4      1_114    0.9931   40.46 1.170e-04   6.146        2
4315    ANGPTL3       1_39    0.9929  248.11 7.170e-04  16.132        1
9365       GAS6      13_62    0.9927   70.41 2.034e-04  -8.924        1
1999      PRKD2      19_33    0.9925   29.70 8.579e-05   5.072        2
3754      RRBP1      20_13    0.9924   32.02 9.247e-05   7.008        2
11257    CYP2A6      19_28    0.9842   32.38 9.274e-05   5.407        1
6090    CSNK1G3       5_75    0.9830   83.47 2.388e-04   9.116        1
2092        SP4       7_19    0.9817  101.51 2.900e-04  10.693        1
6387     TTC39B       9_13    0.9777   22.85 6.502e-05  -4.334        3
1114       SRRT       7_62    0.9732   32.63 9.241e-05   5.425        2
6774       PKN3       9_66    0.9504   47.07 1.302e-04  -6.621        1
1009      GSK3B       3_74    0.9351   42.24 1.149e-04   6.475        2
9046    KLHDC7A       1_13    0.9303   20.94 5.670e-05   4.124        1
9054    SPTY2D1      11_13    0.9096   33.33 8.823e-05  -5.557        1
1144      ASAP3       1_16    0.9084   33.50 8.855e-05   5.283        2
6097       ALLC        2_2    0.9041   27.81 7.317e-05   4.919        1
1320    CWF19L1      10_64    0.8853   35.76 9.213e-05   5.741        2
7350       BRI3       7_60    0.8734   28.79 7.319e-05  -5.140        2
11226    CLDN23       8_11    0.8662   23.95 6.038e-05   4.720        2
4680     TBC1D4      13_37    0.8636   20.07 5.043e-05   3.844        2
9827      PALM3      19_11    0.8572   20.18 5.033e-05   3.839        1
7919        PXK       3_40    0.8409   27.67 6.771e-05  -3.792        2
7992   TMEM150A       2_54    0.8393   21.22 5.184e-05   4.079        1
10459     PRMT6       1_66    0.8182   31.85 7.584e-05  -5.324        1
1846       CTSH      15_37    0.8061   20.48 4.805e-05   3.796        2

Genes with largest effect sizes

#plot PIP vs effect size
plot(ctwas_gene_res$susie_pip, ctwas_gene_res$mu2, xlab="PIP", ylab="mu^2", main="Gene PIPs vs Effect Size")

#genes with 20 largest effect sizes
head(ctwas_gene_res[order(-ctwas_gene_res$mu2),report_cols],20)
       genename region_tag susie_pip     mu2       PVE       z num_eqtl
5797    SLC22A3      6_104  0.000000 6791.95 0.000e+00  -6.593        1
10399       LPA      6_104  0.000000 2114.15 0.000e+00   8.120        1
4433      PSRC1       1_67  1.000000 1652.10 4.808e-03 -41.687        1
5429      SYPL2       1_67  0.028366  647.10 5.342e-05 -25.890        2
NA.135     <NA>      6_104  0.000000  560.48 0.000e+00  -7.335        2
6966    ATXN7L2       1_67  0.033411  320.32 3.115e-05 -18.080        2
5375     GEMIN7      19_31  0.000000  276.41 0.000e+00  14.093        2
4315    ANGPTL3       1_39  0.992947  248.11 7.170e-04  16.132        1
NA.181     <NA>       8_83  0.008164  241.66 5.742e-06  14.404        1
NA.407     <NA>      1_121  0.999476  202.13 5.879e-04 -15.108        1
2454    ST3GAL4      11_77  1.000000  172.26 5.013e-04  13.376        2
781         PVR      19_31  0.000000  165.48 0.000e+00  -6.113        2
5988      FADS1      11_34  0.999902  163.14 4.747e-04  12.926        2
11327       HPR      16_38  1.000000  160.58 4.673e-04 -17.963        2
4047    NECTIN2      19_31  0.000000  108.03 0.000e+00   6.273        2
2092        SP4       7_19  0.981672  101.51 2.900e-04  10.693        1
9251     ZNF329      19_39  0.060635   97.71 1.724e-05   9.498        2
9910       RHCE       1_18  0.169841   97.63 4.826e-05  10.120        2
9428    TMEM50A       1_18  0.127944   97.18 3.619e-05  10.082        1
9438    EMILIN3      20_25  0.044392   94.40 1.220e-05   9.589        2

Genes with highest PVE

#genes with 20 highest pve
head(ctwas_gene_res[order(-ctwas_gene_res$PVE),report_cols],20)
       genename region_tag susie_pip     mu2       PVE       z num_eqtl
4433      PSRC1       1_67    1.0000 1652.10 4.808e-03 -41.687        1
4315    ANGPTL3       1_39    0.9929  248.11 7.170e-04  16.132        1
NA.407     <NA>      1_121    0.9995  202.13 5.879e-04 -15.108        1
2454    ST3GAL4      11_77    1.0000  172.26 5.013e-04  13.376        2
5988      FADS1      11_34    0.9999  163.14 4.747e-04  12.926        2
11327       HPR      16_38    1.0000  160.58 4.673e-04 -17.963        2
2092        SP4       7_19    0.9817  101.51 2.900e-04  10.693        1
6090    CSNK1G3       5_75    0.9830   83.47 2.388e-04   9.116        1
8523       TNKS       8_12    0.9942   74.80 2.164e-04  11.039        2
10612    TRIM39       6_24    0.9990   70.98 2.064e-04   8.840        3
9365       GAS6      13_62    0.9927   70.41 2.034e-04  -8.924        1
3720     INSIG2       2_69    1.0000   68.08 1.981e-04  -8.983        3
1597       PLTP      20_28    0.9940   60.45 1.749e-04  -5.732        1
6774       PKN3       9_66    0.9504   47.07 1.302e-04  -6.621        1
5542      CNIH4      1_114    0.9931   40.46 1.170e-04   6.146        2
1009      GSK3B       3_74    0.9351   42.24 1.149e-04   6.475        2
10708    NYNRIN       14_3    0.7674   47.44 1.060e-04   7.010        3
11257    CYP2A6      19_28    0.9842   32.38 9.274e-05   5.407        1
3754      RRBP1      20_13    0.9924   32.02 9.247e-05   7.008        2
1114       SRRT       7_62    0.9732   32.63 9.241e-05   5.425        2

Genes with largest z scores

#genes with 20 largest z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20)
       genename region_tag susie_pip     mu2       PVE       z num_eqtl
4433      PSRC1       1_67  1.000000 1652.10 4.808e-03 -41.687        1
5429      SYPL2       1_67  0.028366  647.10 5.342e-05 -25.890        2
6966    ATXN7L2       1_67  0.033411  320.32 3.115e-05 -18.080        2
11327       HPR      16_38  1.000000  160.58 4.673e-04 -17.963        2
4315    ANGPTL3       1_39  0.992947  248.11 7.170e-04  16.132        1
NA.407     <NA>      1_121  0.999476  202.13 5.879e-04 -15.108        1
NA.181     <NA>       8_83  0.008164  241.66 5.742e-06  14.404        1
5375     GEMIN7      19_31  0.000000  276.41 0.000e+00  14.093        2
2454    ST3GAL4      11_77  1.000000  172.26 5.013e-04  13.376        2
5988      FADS1      11_34  0.999902  163.14 4.747e-04  12.926        2
8523       TNKS       8_12  0.994247   74.80 2.164e-04  11.039        2
2092        SP4       7_19  0.981672  101.51 2.900e-04  10.693        1
9910       RHCE       1_18  0.169841   97.63 4.826e-05  10.120        2
9428    TMEM50A       1_18  0.127944   97.18 3.619e-05  10.082        1
2309      KPNB1      17_27  0.177130   93.33 4.811e-05  -9.790        2
9438    EMILIN3      20_25  0.044392   94.40 1.220e-05   9.589        2
9251     ZNF329      19_39  0.060635   97.71 1.724e-05   9.498        2
10475    TBKBP1      17_27  0.038167   87.99 9.774e-06  -9.319        2
9718   CEACAM19      19_31  0.000000   60.43 0.000e+00  -9.294        2
6090    CSNK1G3       5_75  0.983020   83.47 2.388e-04   9.116        1

Comparing z scores and PIPs

#set nominal signifiance threshold for z scores
alpha <- 0.05

#bonferroni adjusted threshold for z scores
sig_thresh <- qnorm(1-(alpha/nrow(ctwas_gene_res)/2), lower=T)

#Q-Q plot for z scores
obs_z <- ctwas_gene_res$z[order(ctwas_gene_res$z)]
exp_z <- qnorm((1:nrow(ctwas_gene_res))/nrow(ctwas_gene_res))

plot(exp_z, obs_z, xlab="Expected z", ylab="Observed z", main="Gene z score Q-Q plot")
abline(a=0,b=1)

#plot z score vs PIP
plot(abs(ctwas_gene_res$z), ctwas_gene_res$susie_pip, xlab="abs(z)", ylab="PIP")
abline(v=sig_thresh, col="red", lty=2)

#number of significant z scores
sum(abs(ctwas_gene_res$z) > sig_thresh)
[1] 113
#proportion of significant z scores
mean(abs(ctwas_gene_res$z) > sig_thresh)
[1] 0.0321
#genes with most significant z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20)
       genename region_tag susie_pip     mu2       PVE       z num_eqtl
4433      PSRC1       1_67  1.000000 1652.10 4.808e-03 -41.687        1
5429      SYPL2       1_67  0.028366  647.10 5.342e-05 -25.890        2
6966    ATXN7L2       1_67  0.033411  320.32 3.115e-05 -18.080        2
11327       HPR      16_38  1.000000  160.58 4.673e-04 -17.963        2
4315    ANGPTL3       1_39  0.992947  248.11 7.170e-04  16.132        1
NA.407     <NA>      1_121  0.999476  202.13 5.879e-04 -15.108        1
NA.181     <NA>       8_83  0.008164  241.66 5.742e-06  14.404        1
5375     GEMIN7      19_31  0.000000  276.41 0.000e+00  14.093        2
2454    ST3GAL4      11_77  1.000000  172.26 5.013e-04  13.376        2
5988      FADS1      11_34  0.999902  163.14 4.747e-04  12.926        2
8523       TNKS       8_12  0.994247   74.80 2.164e-04  11.039        2
2092        SP4       7_19  0.981672  101.51 2.900e-04  10.693        1
9910       RHCE       1_18  0.169841   97.63 4.826e-05  10.120        2
9428    TMEM50A       1_18  0.127944   97.18 3.619e-05  10.082        1
2309      KPNB1      17_27  0.177130   93.33 4.811e-05  -9.790        2
9438    EMILIN3      20_25  0.044392   94.40 1.220e-05   9.589        2
9251     ZNF329      19_39  0.060635   97.71 1.724e-05   9.498        2
10475    TBKBP1      17_27  0.038167   87.99 9.774e-06  -9.319        2
9718   CEACAM19      19_31  0.000000   60.43 0.000e+00  -9.294        2
6090    CSNK1G3       5_75  0.983020   83.47 2.388e-04   9.116        1

SNPs with highest PIPs

#snps with PIP>0.8 or 20 highest PIPs
head(ctwas_snp_res[order(-ctwas_snp_res$susie_pip),report_cols_snps],
max(sum(ctwas_snp_res$susie_pip>0.8), 20))
                 id region_tag susie_pip      mu2       PVE        z
14656     rs2495502       1_34    1.0000   305.87 8.901e-04   6.2922
69603     rs1042034       2_13    1.0000   238.93 6.953e-04  16.5730
69609      rs934197       2_13    1.0000   414.85 1.207e-03  33.0609
71339      rs780093       2_16    1.0000   169.08 4.920e-04 -14.1426
366947   rs12208357      6_103    1.0000   246.37 7.170e-04  12.2823
367050   rs60425481      6_104    1.0000 37673.16 1.096e-01  -7.1125
755541  rs113408695      17_39    1.0000   147.08 4.280e-04  12.7688
789399   rs73013176       19_9    1.0000   242.04 7.044e-04 -16.2327
799236   rs62117204      19_31    1.0000   825.94 2.404e-03 -44.6722
799254  rs111794050      19_31    1.0000   773.68 2.252e-03 -33.5996
799287     rs814573      19_31    1.0000  2239.73 6.518e-03  55.5379
799289  rs113345881      19_31    1.0000   783.78 2.281e-03 -34.3186
799292   rs12721109      19_31    1.0000  1359.81 3.957e-03 -46.3258
791930    rs3794991      19_15    1.0000   441.36 1.284e-03 -21.4921
755567    rs8070232      17_39    1.0000   154.27 4.490e-04  -8.0915
69554    rs11679386       2_12    1.0000   134.22 3.906e-04  11.9094
69689     rs1848922       2_13    1.0000   232.19 6.757e-04  25.4123
69612      rs548145       2_13    1.0000   667.51 1.943e-03  33.0860
493942    rs2437818       9_53    1.0000    70.49 2.051e-04   6.3340
502159  rs115478735       9_70    1.0000   309.16 8.997e-04  19.0118
1077630   rs1800961      20_28    1.0000    72.87 2.121e-04  -8.8970
799627  rs150262789      19_32    1.0000    78.48 2.284e-04 -10.8985
754625    rs1801689      17_38    1.0000    81.36 2.368e-04   9.3964
798950   rs73036721      19_30    1.0000    58.35 1.698e-04  -7.7879
441359    rs4738679       8_45    1.0000   108.88 3.169e-04 -11.6999
274124    rs1499279       5_30    1.0000    62.33 1.814e-04  -8.3746
77404    rs72800939       2_28    1.0000    56.23 1.636e-04  -7.8457
789437  rs137992968       19_9    1.0000   115.08 3.349e-04 -10.7526
14667    rs10888896       1_34    1.0000   135.17 3.934e-04  11.8938
7471     rs79598313       1_18    1.0000    47.06 1.369e-04   7.0246
461020   rs13252684       8_83    1.0000   230.52 6.708e-04  11.9644
439964  rs140753685       8_42    1.0000    55.62 1.619e-04   7.7992
798995   rs62115478      19_30    1.0000   183.37 5.336e-04 -14.3262
54531     rs2807848      1_112    1.0000    55.51 1.616e-04  -7.8828
1052221   rs9302635      16_38    1.0000   167.35 4.870e-04 -13.8393
14626    rs11580527       1_34    1.0000    88.74 2.583e-04 -11.1672
14674      rs471705       1_34    1.0000   211.19 6.146e-04  16.2630
348384    rs9496567       6_67    1.0000    39.03 1.136e-04  -6.3402
319169   rs11376017       6_13    0.9999    65.73 1.913e-04  -8.5079
791961  rs113619686      19_15    0.9999    57.37 1.669e-04   0.5939
789463    rs4804149      19_10    0.9999    46.38 1.350e-04   6.5194
77268   rs139029940       2_27    0.9997    39.25 1.142e-04   6.8150
367138  rs374071816      6_104    0.9996  6963.72 2.026e-02  16.2541
789428    rs1569372       19_9    0.9994   281.19 8.178e-04  10.0055
539902   rs17875416      10_71    0.9993    37.97 1.104e-04  -6.2663
323255     rs454182       6_22    0.9993    36.04 1.048e-04   4.7791
789516     rs322144      19_10    0.9992    57.41 1.669e-04   3.9466
605886    rs7397189      12_36    0.9991    34.33 9.982e-05  -5.7710
493915    rs2297400       9_53    0.9989    41.09 1.195e-04   6.6057
789423    rs3745677       19_9    0.9988    91.51 2.660e-04   9.3358
789420  rs147985405       19_9    0.9987  2298.38 6.680e-03 -48.9352
791570    rs2302209      19_14    0.9982    43.16 1.254e-04   6.6360
429691    rs1495743       8_20    0.9978    40.93 1.189e-04  -6.5160
280576    rs7701166       5_45    0.9971    33.67 9.771e-05  -2.4848
734645    rs2255451      16_48    0.9960    37.96 1.100e-04  -6.3628
582737    rs3135506      11_70    0.9957   148.12 4.292e-04  12.3730
582742   rs75542613      11_70    0.9956    35.81 1.038e-04  -6.5344
441327   rs56386732       8_45    0.9954    34.58 1.002e-04  -7.0123
814738   rs76981217      20_24    0.9953    35.33 1.023e-04   7.6925
323692    rs3130253       6_23    0.9942    29.84 8.632e-05   5.6415
621878     rs653178      12_67    0.9927    94.27 2.723e-04  11.0501
610252  rs148481241      12_44    0.9919    27.45 7.924e-05   5.0955
387191     rs217396       7_32    0.9914    68.19 1.968e-04  -9.4286
280517   rs10062361       5_45    0.9877   205.81 5.916e-04  20.3206
138652     rs709149        3_9    0.9857    35.96 1.032e-04  -6.7820
729318    rs4396539      16_37    0.9834    27.34 7.826e-05  -5.2329
145662    rs9834932       3_24    0.9807    65.78 1.878e-04  -8.4816
814689    rs6029132      20_24    0.9801    39.31 1.121e-04  -6.7625
625967   rs11057830      12_76    0.9792    25.82 7.357e-05   4.9296
814742   rs73124945      20_24    0.9782    32.16 9.156e-05  -7.7754
403009    rs3197597       7_61    0.9762    28.92 8.215e-05  -5.0452
461009   rs79658059       8_83    0.9711   273.10 7.718e-04 -16.0220
243931  rs114756490      4_100    0.9707    26.19 7.400e-05   4.9889
387241  rs141379002       7_33    0.9696    25.62 7.230e-05   4.8970
799610   rs34942359      19_32    0.9655    62.49 1.756e-04  -7.0096
822743   rs62219001       21_2    0.9607    26.10 7.297e-05  -4.9484
221202    rs1458038       4_54    0.9604    52.56 1.469e-04  -7.4179
476267    rs1556516       9_16    0.9570    73.35 2.043e-04  -8.9921
591369   rs11048034       12_9    0.9550    35.79 9.947e-05   6.1337
758700    rs4969183      17_44    0.9529    48.75 1.352e-04   7.1693
469072    rs7024888        9_3    0.9507    25.98 7.187e-05  -5.0558
624832    rs1169300      12_74    0.9503    68.16 1.885e-04   8.6855
322716   rs75080831       6_19    0.9460    56.94 1.568e-04  -7.9067
77284     rs4076834       2_27    0.9336   428.47 1.164e-03 -20.1086
566366    rs6591179      11_36    0.9327    24.99 6.784e-05   4.8933
619971    rs1196760      12_63    0.9310    25.89 7.014e-05  -4.8667
77281    rs13430143       2_27    0.9274    78.02 2.106e-04  -3.3445
351120   rs12199109       6_73    0.9238    24.76 6.657e-05   4.8570
1054611   rs2908806       17_7    0.9222    37.24 9.996e-05  -6.0264
192827    rs5855544      3_120    0.9199    23.87 6.390e-05  -4.5937
69606    rs78610189       2_13    0.9172    59.43 1.586e-04  -8.3855
366941    rs9456502      6_103    0.9146    33.18 8.833e-05   5.9640
745084  rs117859452      17_17    0.9081    24.26 6.411e-05  -3.8517
14657     rs1887552       1_34    0.9062   349.25 9.211e-04  -9.8686
799527  rs377297589      19_32    0.9008    50.78 1.331e-04  -6.7865
194614   rs36205397        4_4    0.8982    38.68 1.011e-04   6.1594
725426     rs821840      16_31    0.8959   163.96 4.275e-04 -13.4753
507109   rs10905277       10_8    0.8956    27.89 7.270e-05   5.1258
806012   rs74273659       20_5    0.8953    24.51 6.386e-05   4.6468
539613   rs12244851      10_70    0.8912    36.60 9.492e-05  -4.8831
803822   rs34003091      19_39    0.8891   103.45 2.677e-04 -10.4237
789504     rs322125      19_10    0.8883   102.55 2.651e-04  -7.4704
196839    rs2002574       4_10    0.8837    24.56 6.315e-05  -4.5583
744993    rs3032928      17_17    0.8829    33.85 8.698e-05   6.1119
493935    rs2777788       9_53    0.8791    58.23 1.490e-04  -5.7370
579006  rs201912654      11_59    0.8724    40.09 1.018e-04  -6.3056
634854    rs1012130      13_10    0.8719    39.28 9.967e-05  -2.7810
323663   rs28986304       6_23    0.8676    41.74 1.054e-04   7.3825
818241   rs10641149      20_32    0.8666    27.13 6.842e-05   5.0758
829984    rs2835302      21_17    0.8646    25.71 6.469e-05  -4.6537
120749    rs7569317      2_120    0.8576    43.89 1.095e-04   7.9007
69406     rs6531234       2_12    0.8531    42.19 1.047e-04  -7.1708
484253   rs11144506       9_35    0.8502    26.96 6.671e-05   5.0427
789473   rs58495388      19_10    0.8495    33.86 8.371e-05   5.5313
814707    rs6102034      20_24    0.8438    96.82 2.377e-04 -11.1900
280540    rs3843482       5_45    0.8401   402.71 9.845e-04  25.0344
357323    rs9321207       6_86    0.8319    30.70 7.433e-05   5.4016
813483   rs11167269      20_21    0.8302    57.10 1.380e-04  -7.7950
750212    rs4793601      17_28    0.8237    30.61 7.338e-05  -6.2095
755552    rs9303012      17_39    0.8172   146.84 3.492e-04   2.2591
534403   rs10882161      10_59    0.8167    29.87 7.100e-05  -5.4756
634846    rs1799955      13_10    0.8162    70.55 1.676e-04  -6.6936

SNPs with largest effect sizes

#plot PIP vs effect size
#plot(ctwas_snp_res$susie_pip, ctwas_snp_res$mu2, xlab="PIP", ylab="mu^2", main="SNP PIPs vs Effect Size")

#SNPs with 50 largest effect sizes
head(ctwas_snp_res[order(-ctwas_snp_res$mu2),report_cols_snps],50)
                id region_tag susie_pip   mu2       PVE      z
367046   rs3106169      6_104 6.281e-01 37722 6.895e-02 11.139
367047   rs3127598      6_104 4.719e-01 37722 5.180e-02 11.135
367055   rs3106167      6_104 4.793e-01 37721 5.261e-02 11.136
367039  rs11755965      6_104 1.129e-01 37711 1.239e-02 11.140
367050  rs60425481      6_104 1.000e+00 37673 1.096e-01 -7.113
367030  rs12194962      6_104 1.571e-07 37631 1.720e-08 11.106
367048   rs3127597      6_104 2.003e-08 37606 2.192e-09 11.145
367009   rs3119311      6_104 0.000e+00 27265 0.000e+00  8.031
367003   rs3127579      6_104 0.000e+00 19863 0.000e+00  7.568
366997  rs10945658      6_104 0.000e+00 17395 0.000e+00  8.309
366992   rs3103352      6_104 0.000e+00 17354 0.000e+00  8.522
366996   rs3119308      6_104 0.000e+00 17352 0.000e+00  8.274
366988   rs3101821      6_104 0.000e+00 17294 0.000e+00  8.528
366994  rs12205178      6_104 0.000e+00 17254 0.000e+00  8.297
366986 rs148015788      6_104 0.000e+00 17037 0.000e+00  8.351
367097   rs3124784      6_104 0.000e+00 14303 0.000e+00  9.680
367098   rs3127596      6_104 0.000e+00 12984 0.000e+00  9.556
367091   rs3127599      6_104 0.000e+00 12917 0.000e+00  9.259
367061   rs2481030      6_104 0.000e+00 12353 0.000e+00  4.811
367026   rs2504949      6_104 0.000e+00 10166 0.000e+00  2.937
367079    rs388170      6_104 0.000e+00  9414 0.000e+00  3.833
367001    rs316013      6_104 0.000e+00  9022 0.000e+00 -3.002
367002    rs316012      6_104 0.000e+00  8914 0.000e+00 -3.074
367082   rs9355288      6_104 0.000e+00  8736 0.000e+00  6.319
366990    rs610206      6_104 0.000e+00  8238 0.000e+00 -2.944
366991    rs595374      6_104 0.000e+00  8222 0.000e+00 -2.921
366998    rs315995      6_104 0.000e+00  8024 0.000e+00 -3.207
366995    rs543435      6_104 0.000e+00  7994 0.000e+00 -3.250
367044    rs452867      6_104 0.000e+00  7570 0.000e+00 -7.124
367053    rs367334      6_104 0.000e+00  7564 0.000e+00 -7.106
367041    rs589931      6_104 0.000e+00  7563 0.000e+00 -7.116
367042    rs600584      6_104 0.000e+00  7563 0.000e+00 -7.113
367043    rs434953      6_104 0.000e+00  7563 0.000e+00 -7.111
367049    rs380498      6_104 0.000e+00  7563 0.000e+00 -7.115
367017   rs3119312      6_104 0.000e+00  7226 0.000e+00  3.771
367138 rs374071816      6_104 9.996e-01  6964 2.026e-02 16.254
367076   rs2872317      6_104 0.000e+00  6656 0.000e+00  6.746
367073   rs2313453      6_104 0.000e+00  6651 0.000e+00  6.718
367143   rs4252185      6_104 4.313e-04  6425 8.065e-06 15.878
367064 rs146184004      6_104 0.000e+00  6335 0.000e+00  6.534
367067    rs624319      6_104 0.000e+00  6260 0.000e+00 -6.291
367066    rs637614      6_104 0.000e+00  6252 0.000e+00 -6.362
367068    rs486339      6_104 0.000e+00  6209 0.000e+00 -6.311
367013    rs316036      6_104 0.000e+00  6097 0.000e+00 -7.009
367065    rs555754      6_104 0.000e+00  6055 0.000e+00 -6.593
367144  rs12212146      6_104 0.000e+00  4816 0.000e+00 -2.410
367011    rs582280      6_104 0.000e+00  4671 0.000e+00  2.635
367010    rs497039      6_104 0.000e+00  4670 0.000e+00  2.634
367094   rs9346818      6_104 0.000e+00  3837 0.000e+00  7.950
367197   rs1247539      6_104 0.000e+00  3790 0.000e+00 -4.294

SNPs with highest PVE

#SNPs with 50 highest pve
head(ctwas_snp_res[order(-ctwas_snp_res$PVE),report_cols_snps],50)
                 id region_tag susie_pip      mu2       PVE       z
367050   rs60425481      6_104    1.0000 37673.16 0.1096358  -7.113
367046    rs3106169      6_104    0.6281 37721.87 0.0689541  11.139
367055    rs3106167      6_104    0.4793 37721.42 0.0526133  11.136
367047    rs3127598      6_104    0.4719 37721.52 0.0518047  11.135
367138  rs374071816      6_104    0.9996  6963.72 0.0202570  16.254
367039   rs11755965      6_104    0.1129 37710.55 0.0123867  11.140
789420  rs147985405       19_9    0.9987  2298.38 0.0066798 -48.935
799287     rs814573      19_31    1.0000  2239.73 0.0065180  55.538
799292   rs12721109      19_31    1.0000  1359.81 0.0039573 -46.326
799236   rs62117204      19_31    1.0000   825.94 0.0024036 -44.672
799289  rs113345881      19_31    1.0000   783.78 0.0022809 -34.319
799254  rs111794050      19_31    1.0000   773.68 0.0022516 -33.600
69612      rs548145       2_13    1.0000   667.51 0.0019426  33.086
791930    rs3794991      19_15    1.0000   441.36 0.0012844 -21.492
69609      rs934197       2_13    1.0000   414.85 0.0012073  33.061
77284     rs4076834       2_27    0.9336   428.47 0.0011641 -20.109
280540    rs3843482       5_45    0.8401   402.71 0.0009845  25.034
14657     rs1887552       1_34    0.9062   349.25 0.0009211  -9.869
502159  rs115478735       9_70    1.0000   309.16 0.0008997  19.012
14656     rs2495502       1_34    1.0000   305.87 0.0008901   6.292
789428    rs1569372       19_9    0.9994   281.19 0.0008178  10.006
461009   rs79658059       8_83    0.9711   273.10 0.0007718 -16.022
366947   rs12208357      6_103    1.0000   246.37 0.0007170  12.282
789399   rs73013176       19_9    1.0000   242.04 0.0007044 -16.233
69603     rs1042034       2_13    1.0000   238.93 0.0006953  16.573
69689     rs1848922       2_13    1.0000   232.19 0.0006757  25.412
461020   rs13252684       8_83    1.0000   230.52 0.0006708  11.964
14674      rs471705       1_34    1.0000   211.19 0.0006146  16.263
280517   rs10062361       5_45    0.9877   205.81 0.0005916  20.321
798995   rs62115478      19_30    1.0000   183.37 0.0005336 -14.326
71339      rs780093       2_16    1.0000   169.08 0.0004920 -14.143
1052221   rs9302635      16_38    1.0000   167.35 0.0004870 -13.839
755567    rs8070232      17_39    1.0000   154.27 0.0004490  -8.091
366961    rs3818678      6_103    0.7673   199.28 0.0004450  -9.948
582737    rs3135506      11_70    0.9957   148.12 0.0004292  12.373
755541  rs113408695      17_39    1.0000   147.08 0.0004280  12.769
725426     rs821840      16_31    0.8959   163.96 0.0004275 -13.475
14667    rs10888896       1_34    1.0000   135.17 0.0003934  11.894
69554    rs11679386       2_12    1.0000   134.22 0.0003906  11.909
755552    rs9303012      17_39    0.8172   146.84 0.0003492   2.259
305419   rs12657266       5_92    0.7541   158.13 0.0003470  13.895
789437  rs137992968       19_9    1.0000   115.08 0.0003349 -10.753
1052033  rs77303550      16_38    0.6757   163.45 0.0003214 -13.733
441359    rs4738679       8_45    1.0000   108.88 0.0003169 -11.700
461008    rs2980875       8_83    0.5445   185.03 0.0002932 -22.102
621878     rs653178      12_67    0.9927    94.27 0.0002723  11.050
803822   rs34003091      19_39    0.8891   103.45 0.0002677 -10.424
789423    rs3745677       19_9    0.9988    91.51 0.0002660   9.336
789504     rs322125      19_10    0.8883   102.55 0.0002651  -7.470
14626    rs11580527       1_34    1.0000    88.74 0.0002583 -11.167

SNPs with largest z scores

#histogram of (abs) SNP z scores
hist(abs(ctwas_snp_res$z))

#SNPs with 50 largest z scores
head(ctwas_snp_res[order(-abs(ctwas_snp_res$z)),report_cols_snps],50)
                id region_tag susie_pip    mu2       PVE      z
799287    rs814573      19_31 1.000e+00 2239.7 6.518e-03  55.54
789420 rs147985405       19_9 9.987e-01 2298.4 6.680e-03 -48.94
789415  rs73015020       19_9 7.913e-04 2286.0 5.264e-06 -48.80
789413 rs138175288       19_9 3.689e-04 2284.2 2.452e-06 -48.78
789414 rs138294113       19_9 8.891e-05 2280.4 5.901e-07 -48.75
789416  rs77140532       19_9 5.376e-05 2280.8 3.568e-07 -48.74
789417 rs112552009       19_9 2.699e-05 2276.9 1.788e-07 -48.71
789418  rs10412048       19_9 1.083e-05 2277.5 7.179e-08 -48.70
789412  rs55997232       19_9 2.344e-09 2257.2 1.540e-11 -48.52
799292  rs12721109      19_31 1.000e+00 1359.8 3.957e-03 -46.33
799236  rs62117204      19_31 1.000e+00  825.9 2.404e-03 -44.67
799223   rs1551891      19_31 0.000e+00  499.4 0.000e+00 -42.27
870728  rs12740374       1_67 7.500e-04 1482.3 3.235e-06 -41.79
870724   rs7528419       1_67 7.548e-04 1478.4 3.248e-06 -41.74
870735    rs646776       1_67 6.337e-04 1476.8 2.724e-06  41.73
870734    rs629301       1_67 5.825e-04 1473.0 2.497e-06  41.69
870746    rs583104       1_67 6.366e-04 1431.8 2.653e-06  41.09
870749   rs4970836       1_67 6.239e-04 1428.8 2.594e-06  41.05
870751   rs1277930       1_67 6.381e-04 1424.0 2.645e-06  40.98
870752    rs599839       1_67 6.588e-04 1423.1 2.728e-06  40.96
789421  rs17248769       19_9 6.725e-08 1730.4 3.387e-10 -40.84
789422   rs2228671       19_9 4.781e-08 1719.4 2.392e-10 -40.70
870732   rs3832016       1_67 4.279e-04 1382.4 1.721e-06  40.40
870729    rs660240       1_67 4.264e-04 1375.1 1.706e-06  40.29
870747    rs602633       1_67 4.856e-04 1353.7 1.913e-06  39.96
789411   rs9305020       19_9 2.909e-14 1313.8 1.112e-16 -34.84
799283    rs405509      19_31 0.000e+00  975.9 0.000e+00 -34.64
870715   rs4970834       1_67 9.850e-04 1021.7 2.929e-06 -34.62
799289 rs113345881      19_31 1.000e+00  783.8 2.281e-03 -34.32
799207  rs62120566      19_31 0.000e+00 1339.0 0.000e+00 -33.74
799254 rs111794050      19_31 1.000e+00  773.7 2.252e-03 -33.60
69612     rs548145       2_13 1.000e+00  667.5 1.943e-03  33.09
799260   rs4802238      19_31 0.000e+00  979.6 0.000e+00  33.08
69609     rs934197       2_13 1.000e+00  414.8 1.207e-03  33.06
799201 rs188099946      19_31 0.000e+00 1283.4 0.000e+00 -33.04
799271   rs2972559      19_31 0.000e+00 1310.8 0.000e+00  32.29
799195 rs201314191      19_31 0.000e+00 1190.0 0.000e+00 -32.07
870736   rs3902354       1_67 4.819e-04  871.8 1.223e-06  32.00
870725  rs11102967       1_67 4.843e-04  868.2 1.224e-06  31.94
870750   rs4970837       1_67 5.616e-04  864.7 1.413e-06  31.86
799262  rs56394238      19_31 0.000e+00  973.2 0.000e+00  31.55
799239   rs2965169      19_31 0.000e+00  359.4 0.000e+00 -31.38
799263   rs3021439      19_31 0.000e+00  866.0 0.000e+00  31.05
870720    rs611917       1_67 4.507e-04  817.3 1.072e-06 -30.98
69639   rs12997242       2_13 2.334e-11  382.1 2.596e-14  30.82
799270  rs12162222      19_31 0.000e+00 1122.3 0.000e+00  30.50
69613     rs478588       2_13 7.657e-11  615.3 1.371e-13  30.49
799200  rs62119327      19_31 0.000e+00 1047.7 0.000e+00 -30.42
69614   rs56350433       2_13 3.217e-12  350.8 3.285e-15  30.23
69619   rs56079819       2_13 3.223e-12  350.0 3.283e-15  30.19

sessionInfo()
R version 4.1.0 (2021-05-18)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.3.13-el7-x86_64/lib/libopenblas_haswellp-r0.3.13.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] cowplot_1.1.1   ggplot2_3.4.0   workflowr_1.7.0

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.0  xfun_0.35         bslib_0.4.1       generics_0.1.3   
 [5] colorspace_2.0-3  vctrs_0.5.1       htmltools_0.5.4   yaml_2.3.6       
 [9] utf8_1.2.2        blob_1.2.3        rlang_1.0.6       jquerylib_0.1.4  
[13] later_1.3.0       pillar_1.8.1      withr_2.5.0       glue_1.6.2       
[17] DBI_1.1.3         bit64_4.0.5       lifecycle_1.0.3   stringr_1.5.0    
[21] munsell_0.5.0     gtable_0.3.1      evaluate_0.19     memoise_2.0.1    
[25] labeling_0.4.2    knitr_1.41        callr_3.7.3       fastmap_1.1.0    
[29] httpuv_1.6.7      ps_1.7.2          fansi_1.0.3       highr_0.9        
[33] Rcpp_1.0.9        promises_1.2.0.1  scales_1.2.1      cachem_1.0.6     
[37] jsonlite_1.8.4    farver_2.1.0      fs_1.5.2          bit_4.0.5        
[41] digest_0.6.31     stringi_1.7.8     processx_3.8.0    dplyr_1.0.10     
[45] getPass_0.2-2     rprojroot_2.0.3   grid_4.1.0        cli_3.4.1        
[49] tools_4.1.0       magrittr_2.0.3    sass_0.4.4        tibble_3.1.8     
[53] RSQLite_2.2.19    whisker_0.4.1     pkgconfig_2.0.3   data.table_1.14.6
[57] assertthat_0.2.1  rmarkdown_2.19    httr_1.4.4        rstudioapi_0.14  
[61] R6_2.5.1          git2r_0.30.1      compiler_4.1.0