T2D - Adipose Visceral Omentum
sheng Qian
2021-2-6
Last updated: 2022-02-26
Checks: 6 1
Knit directory: cTWAS_analysis/
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Weight QC
#number of imputed weights
nrow(qclist_all)[1] 8279#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
860 632 533 357 434 491 417 287 316 367 525 508 179 281 284 320 451 139 400 239
21 22
83 176 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 4625#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.5586Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0185530 0.0003497 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
7.211 8.954 #report sample size
print(sample_size)[1] 62892#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 8279 5017190#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01761 0.24979 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.1023 1.4004Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
8882 POLR2J3 7_63 0.9604 25.74 0.0003930 4.987 1
7980 LMOD1 1_102 0.9456 24.43 0.0003672 -4.907 1
249 ANGEL1 14_36 0.9355 21.80 0.0003243 4.558 2
3617 ARG1 6_87 0.9094 29.87 0.0004319 -5.606 1
1848 CTSZ 20_34 0.8805 20.18 0.0002824 -3.896 1
7655 PTH1R 3_33 0.8780 28.47 0.0003974 -5.646 1
3290 GRB14 2_100 0.8747 25.00 0.0003478 5.163 1
14287 RP5-899E9.1 7_49 0.8132 19.91 0.0002575 -4.333 1
4668 ZNF236 18_45 0.7874 20.14 0.0002522 -4.378 1
7322 NTAN1 16_15 0.7754 19.37 0.0002388 4.192 1
9579 DMRT2 9_1 0.7497 19.40 0.0002313 -4.318 1
10241 ZNF664 12_75 0.7398 41.11 0.0004836 -6.452 1
9990 SEC24C 10_49 0.7330 26.90 0.0003135 -4.862 1
2306 DNASE2 19_10 0.7296 18.47 0.0002143 -3.744 1
11753 TMEM229B 14_31 0.7256 18.31 0.0002112 -3.658 1
1508 CWF19L1 10_64 0.6962 33.33 0.0003690 -5.810 1
5258 C2orf49 2_62 0.6887 26.88 0.0002943 5.235 1
13192 LINC01184 5_78 0.6805 18.64 0.0002016 3.793 1
6264 MRPS5 2_57 0.6164 20.20 0.0001980 -3.737 1
6029 SCYL1 11_36 0.6145 22.23 0.0002172 -4.814 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
7057 JAZF1 7_23 0.277121 130.42 5.747e-04 -12.662 1
2722 WFS1 4_7 0.158490 64.77 1.632e-04 11.434 1
3379 THADA 2_27 0.063978 59.34 6.037e-05 8.667 1
14565 LINC01126 2_27 0.050344 52.18 4.177e-05 -8.377 1
10525 UBE2E2 3_17 0.084142 48.51 6.491e-05 7.166 2
11136 KCNJ11 11_12 0.028462 45.08 2.040e-05 7.075 2
11227 NCR3LG1 11_12 0.035887 44.08 2.515e-05 -6.854 2
3273 NRBP1 2_16 0.057481 43.86 4.008e-05 -6.625 1
7493 UBE2Z 17_28 0.607571 43.82 4.234e-04 -7.392 1
3656 CCDC92 12_75 0.135611 42.88 9.246e-05 -5.887 2
1401 PABPC4 1_24 0.103952 42.78 7.072e-05 -6.817 1
7491 ATP5G1 17_28 0.090095 41.35 5.923e-05 6.400 1
10241 ZNF664 12_75 0.739843 41.11 4.836e-04 -6.452 1
1158 COBLL1 2_100 0.072551 39.99 4.613e-05 -5.375 1
12780 CYP21A2 6_26 0.055455 39.91 3.519e-05 6.453 1
7494 SNF8 17_28 0.077698 39.44 4.873e-05 6.300 1
9611 PEAK1 15_36 0.478500 39.02 2.969e-04 -6.885 1
6467 CDKAL1 6_15 0.004169 37.81 2.506e-06 -8.192 1
7378 AP3S2 15_41 0.369116 36.18 2.124e-04 6.356 1
5126 P2RX4 12_74 0.263755 35.42 1.485e-04 4.096 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
7057 JAZF1 7_23 0.2771 130.42 0.0005747 -12.662 1
10241 ZNF664 12_75 0.7398 41.11 0.0004836 -6.452 1
3617 ARG1 6_87 0.9094 29.87 0.0004319 -5.606 1
7493 UBE2Z 17_28 0.6076 43.82 0.0004234 -7.392 1
7655 PTH1R 3_33 0.8780 28.47 0.0003974 -5.646 1
8882 POLR2J3 7_63 0.9604 25.74 0.0003930 4.987 1
1508 CWF19L1 10_64 0.6962 33.33 0.0003690 -5.810 1
7980 LMOD1 1_102 0.9456 24.43 0.0003672 -4.907 1
3290 GRB14 2_100 0.8747 25.00 0.0003478 5.163 1
249 ANGEL1 14_36 0.9355 21.80 0.0003243 4.558 2
9990 SEC24C 10_49 0.7330 26.90 0.0003135 -4.862 1
9611 PEAK1 15_36 0.4785 39.02 0.0002969 -6.885 1
5258 C2orf49 2_62 0.6887 26.88 0.0002943 5.235 1
1848 CTSZ 20_34 0.8805 20.18 0.0002824 -3.896 1
14287 RP5-899E9.1 7_49 0.8132 19.91 0.0002575 -4.333 1
4668 ZNF236 18_45 0.7874 20.14 0.0002522 -4.378 1
7322 NTAN1 16_15 0.7754 19.37 0.0002388 4.192 1
4021 KBTBD4 11_29 0.6073 24.30 0.0002346 -5.098 1
9579 DMRT2 9_1 0.7497 19.40 0.0002313 -4.318 1
6029 SCYL1 11_36 0.6145 22.23 0.0002172 -4.814 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
7057 JAZF1 7_23 0.277121 130.42 5.747e-04 -12.662 1
2722 WFS1 4_7 0.158490 64.77 1.632e-04 11.434 1
3379 THADA 2_27 0.063978 59.34 6.037e-05 8.667 1
14565 LINC01126 2_27 0.050344 52.18 4.177e-05 -8.377 1
6467 CDKAL1 6_15 0.004169 37.81 2.506e-06 -8.192 1
7493 UBE2Z 17_28 0.607571 43.82 4.234e-04 -7.392 1
10525 UBE2E2 3_17 0.084142 48.51 6.491e-05 7.166 2
11136 KCNJ11 11_12 0.028462 45.08 2.040e-05 7.075 2
9611 PEAK1 15_36 0.478500 39.02 2.969e-04 -6.885 1
11227 NCR3LG1 11_12 0.035887 44.08 2.515e-05 -6.854 2
1401 PABPC4 1_24 0.103952 42.78 7.072e-05 -6.817 1
3273 NRBP1 2_16 0.057481 43.86 4.008e-05 -6.625 1
12062 MICB 6_25 0.392658 34.75 2.170e-04 6.462 2
12780 CYP21A2 6_26 0.055455 39.91 3.519e-05 6.453 1
10241 ZNF664 12_75 0.739843 41.11 4.836e-04 -6.452 1
7491 ATP5G1 17_28 0.090095 41.35 5.923e-05 6.400 1
7378 AP3S2 15_41 0.369116 36.18 2.124e-04 6.356 1
7494 SNF8 17_28 0.077698 39.44 4.873e-05 6.300 1
13095 ARPIN 15_41 0.224871 34.65 1.239e-04 6.250 1
3656 CCDC92 12_75 0.135611 42.88 9.246e-05 -5.887 2Comparing z scores and PIPs
[1] 0.008093 genename region_tag susie_pip mu2 PVE z num_eqtl
7057 JAZF1 7_23 0.277121 130.42 5.747e-04 -12.662 1
2722 WFS1 4_7 0.158490 64.77 1.632e-04 11.434 1
3379 THADA 2_27 0.063978 59.34 6.037e-05 8.667 1
14565 LINC01126 2_27 0.050344 52.18 4.177e-05 -8.377 1
6467 CDKAL1 6_15 0.004169 37.81 2.506e-06 -8.192 1
7493 UBE2Z 17_28 0.607571 43.82 4.234e-04 -7.392 1
10525 UBE2E2 3_17 0.084142 48.51 6.491e-05 7.166 2
11136 KCNJ11 11_12 0.028462 45.08 2.040e-05 7.075 2
9611 PEAK1 15_36 0.478500 39.02 2.969e-04 -6.885 1
11227 NCR3LG1 11_12 0.035887 44.08 2.515e-05 -6.854 2
1401 PABPC4 1_24 0.103952 42.78 7.072e-05 -6.817 1
3273 NRBP1 2_16 0.057481 43.86 4.008e-05 -6.625 1
12062 MICB 6_25 0.392658 34.75 2.170e-04 6.462 2
12780 CYP21A2 6_26 0.055455 39.91 3.519e-05 6.453 1
10241 ZNF664 12_75 0.739843 41.11 4.836e-04 -6.452 1
7491 ATP5G1 17_28 0.090095 41.35 5.923e-05 6.400 1
7378 AP3S2 15_41 0.369116 36.18 2.124e-04 6.356 1
7494 SNF8 17_28 0.077698 39.44 4.873e-05 6.300 1
13095 ARPIN 15_41 0.224871 34.65 1.239e-04 6.250 1
3656 CCDC92 12_75 0.135611 42.88 9.246e-05 -5.887 2GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 30Uploading data to Enrichr... Done.
Querying GO_Biological_Process_2021... Done.
Querying GO_Cellular_Component_2021... Done.
Querying GO_Molecular_Function_2021... Done.
Parsing results... Done.
[1] "GO_Biological_Process_2021"
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)
[1] "GO_Cellular_Component_2021"
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)
[1] "GO_Molecular_Function_2021"
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)DisGeNET enrichment analysis for genes with PIP>0.5
Description FDR Ratio BgRatio
30 Ataxia, Spinocerebellar 0.006521 2/13 34/9703
35 Jansen type metaphyseal chondrodysplasia 0.006521 1/13 1/9703
45 Spinocerebellar Ataxia Type 1 0.006521 2/13 34/9703
46 Spinocerebellar Ataxia Type 2 0.006521 2/13 34/9703
47 Spinocerebellar Ataxia Type 4 0.006521 2/13 35/9703
48 Spinocerebellar Ataxia Type 5 0.006521 2/13 34/9703
49 Spinocerebellar Ataxia Type 6 (disorder) 0.006521 2/13 34/9703
50 Spinocerebellar Ataxia Type 7 0.006521 2/13 34/9703
52 Calcium pyrophosphate arthropathy 0.006521 1/13 1/9703
58 Eiken Skeletal Dysplasia 0.006521 1/13 1/9703WebGestalt enrichment analysis for genes with PIP>0.5
Loading the functional categories...
Loading the ID list...
Loading the reference list...
Performing the enrichment analysis...Warning in oraEnrichment(interestGeneList, referenceGeneList, geneSet, minNum =
minNum, : No significant gene set is identified based on FDR 0.05!NULLPIP Manhattan Plot
Sensitivity, specificity and precision for silver standard genes
#number of genes in known annotations
print(length(known_annotations))[1] 72#number of genes in known annotations with imputed expression
print(sum(known_annotations %in% ctwas_gene_res$genename))[1] 23#significance threshold for TWAS
print(sig_thresh)[1] 4.525#number of ctwas genes
length(ctwas_genes)[1] 30#number of TWAS genes
length(twas_genes)[1] 67#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z num_eqtl
6264 MRPS5 2_57 0.6164 20.20 0.0001980 -3.737 1
11469 PRPF40A 2_91 0.5260 19.92 0.0001666 -3.944 2
10022 ZBTB38 3_86 0.5340 17.12 0.0001454 -3.072 1
7953 DCAF16 4_17 0.5632 20.45 0.0001831 -4.139 3
5664 G3BP2 4_51 0.5373 20.37 0.0001740 -4.457 1
7088 ANKH 5_12 0.5580 21.14 0.0001876 3.655 1
13192 LINC01184 5_78 0.6805 18.64 0.0002016 3.793 1
5358 NIPSNAP3A 9_52 0.5688 19.29 0.0001745 -3.837 2
6679 EIF3M 11_22 0.5118 17.81 0.0001449 -3.890 1
11753 TMEM229B 14_31 0.7256 18.31 0.0002112 -3.658 1
7322 NTAN1 16_15 0.7754 19.37 0.0002388 4.192 1
4668 ZNF236 18_45 0.7874 20.14 0.0002522 -4.378 1
2306 DNASE2 19_10 0.7296 18.47 0.0002143 -3.744 1
14287 RP5-899E9.1 7_49 0.8132 19.91 0.0002575 -4.333 1
9579 DMRT2 9_1 0.7497 19.40 0.0002313 -4.318 1
1848 CTSZ 20_34 0.8805 20.18 0.0002824 -3.896 1#sensitivity / recall
print(sensitivity) ctwas TWAS
0.01389 0.04167 #specificity
print(specificity) ctwas TWAS
0.9965 0.9922 #precision / PPV
print(precision) ctwas TWAS
0.03333 0.04478
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] readxl_1.3.1 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7
[5] purrr_0.3.4 readr_2.1.1 tidyr_1.1.4 tidyverse_1.3.1
[9] tibble_3.1.6 WebGestaltR_0.4.4 disgenet2r_0.99.2 enrichR_3.0
[13] cowplot_1.0.0 ggplot2_3.3.5 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] fs_1.5.2 lubridate_1.8.0 bit64_4.0.5 doParallel_1.0.17
[5] httr_1.4.2 rprojroot_2.0.2 tools_3.6.1 backports_1.4.1
[9] doRNG_1.8.2 utf8_1.2.2 R6_2.5.1 vipor_0.4.5
[13] DBI_1.1.2 colorspace_2.0-2 withr_2.4.3 ggrastr_1.0.1
[17] tidyselect_1.1.1 bit_4.0.4 curl_4.3.2 compiler_3.6.1
[21] git2r_0.26.1 rvest_1.0.2 cli_3.1.0 Cairo_1.5-12.2
[25] xml2_1.3.3 labeling_0.4.2 scales_1.1.1 apcluster_1.4.8
[29] digest_0.6.29 rmarkdown_2.11 svglite_1.2.2 pkgconfig_2.0.3
[33] htmltools_0.5.2 dbplyr_2.1.1 fastmap_1.1.0 highr_0.9
[37] rlang_1.0.1 rstudioapi_0.13 RSQLite_2.2.8 jquerylib_0.1.4
[41] farver_2.1.0 generics_0.1.1 jsonlite_1.7.2 vroom_1.5.7
[45] magrittr_2.0.2 Matrix_1.2-18 ggbeeswarm_0.6.0 Rcpp_1.0.8
[49] munsell_0.5.0 fansi_1.0.2 gdtools_0.1.9 lifecycle_1.0.1
[53] stringi_1.7.6 whisker_0.3-2 yaml_2.2.1 plyr_1.8.6
[57] grid_3.6.1 blob_1.2.2 ggrepel_0.9.1 parallel_3.6.1
[61] promises_1.0.1 crayon_1.5.0 lattice_0.20-38 haven_2.4.3
[65] hms_1.1.1 knitr_1.36 pillar_1.6.4 igraph_1.2.10
[69] rjson_0.2.20 rngtools_1.5.2 reshape2_1.4.4 codetools_0.2-16
[73] reprex_2.0.1 glue_1.6.2 evaluate_0.14 data.table_1.14.2
[77] modelr_0.1.8 vctrs_0.3.8 tzdb_0.2.0 httpuv_1.5.1
[81] foreach_1.5.2 cellranger_1.1.0 gtable_0.3.0 assertthat_0.2.1
[85] cachem_1.0.6 xfun_0.29 broom_0.7.10 later_0.8.0
[89] iterators_1.0.14 beeswarm_0.2.3 memoise_2.0.1 ellipsis_0.3.2