T2D - Adipose Subcutaneous
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] 8591#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
874 679 536 364 458 498 421 323 333 362 534 523 182 297 322 342 434 142 429 246
21 22
102 190 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 4545#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.529Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0164053 0.0003491 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
9.542 8.857 #report sample size
print(sample_size)[1] 62892#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 8591 5017200#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.02138 0.24667 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.09756 1.39401Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
7109 LONRF1 8_15 0.9929 29.21 0.0004612 -5.425 2
5112 P2RX4 12_74 0.9615 25.94 0.0003966 5.081 1
250 ANGEL1 14_36 0.9577 22.68 0.0003453 4.529 2
7272 KCNS2 8_68 0.9085 26.21 0.0003787 -5.148 1
7654 PTH1R 3_33 0.8980 30.11 0.0004300 -5.646 1
6918 ARL14EP 11_21 0.8715 23.28 0.0003226 -4.878 2
13239 N4BP2L2 13_11 0.8441 24.34 0.0003267 -4.430 1
10030 FAM89B 11_36 0.7961 24.33 0.0003080 5.011 1
8836 FAM234A 16_1 0.7888 31.19 0.0003912 5.621 1
11712 PARVA 11_9 0.7846 20.35 0.0002539 -3.862 1
7267 GDF6 8_67 0.7821 21.36 0.0002656 -4.372 1
3924 SEPT7 7_26 0.7751 20.71 0.0002553 -4.167 1
3988 ZC3H13 13_19 0.7732 20.56 0.0002527 4.310 1
2323 DNASE2 19_10 0.7718 18.79 0.0002306 -3.744 1
4009 KBTBD4 11_29 0.7474 26.46 0.0003145 -5.098 1
3282 GRB14 2_100 0.7262 21.32 0.0002461 4.745 1
11149 SLIT1 10_62 0.7054 24.45 0.0002742 4.797 2
7490 UBE2Z 17_28 0.7027 47.08 0.0005260 -7.392 1
6245 MRPS5 2_57 0.6981 19.67 0.0002184 -3.810 2
10469 TMEM132E 17_20 0.6954 21.33 0.0002358 -3.693 2Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
7048 JAZF1 7_23 0.010490 138.57 2.311e-05 -12.731 1
14285 RP11-395N3.2 2_133 0.035389 109.24 6.147e-05 -11.370 1
356 ANK1 8_36 0.040526 59.39 3.827e-05 8.667 1
6954 ZFP36L2 2_27 0.039417 55.29 3.465e-05 8.377 1
2723 WFS1 4_7 0.029567 53.23 2.502e-05 10.642 1
8537 C2 6_26 0.219641 52.12 1.820e-04 7.149 1
11174 KCNJ11 11_12 0.022353 49.77 1.769e-05 7.231 1
11270 NCR3LG1 11_12 0.022725 49.31 1.782e-05 -7.169 2
14659 LINC01126 2_27 0.049134 47.85 3.738e-05 -7.520 2
1417 PABPC4 1_24 0.208389 47.85 1.585e-04 -7.054 1
7490 UBE2Z 17_28 0.702743 47.08 5.260e-04 -7.392 1
3368 THADA 2_27 0.037436 45.75 2.723e-05 7.450 1
943 ZZEF1 17_4 0.090687 43.96 6.339e-05 6.917 1
10282 ZNF664 12_75 0.315661 43.32 2.174e-04 -6.452 1
7491 SNF8 17_28 0.064662 43.17 4.438e-05 6.300 1
10726 BMP8A 1_24 0.090841 42.59 6.152e-05 6.868 1
6449 CDKAL1 6_15 0.003752 40.70 2.428e-06 -8.192 1
10555 UBE2E2 3_17 0.498581 39.49 3.131e-04 6.081 1
3647 CCDC92 12_75 0.108518 38.03 6.562e-05 -5.499 5
13467 RP11-419C23.1 8_33 0.506567 37.39 3.012e-04 -6.317 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
7490 UBE2Z 17_28 0.7027 47.08 0.0005260 -7.392 1
7109 LONRF1 8_15 0.9929 29.21 0.0004612 -5.425 2
7654 PTH1R 3_33 0.8980 30.11 0.0004300 -5.646 1
5112 P2RX4 12_74 0.9615 25.94 0.0003966 5.081 1
8836 FAM234A 16_1 0.7888 31.19 0.0003912 5.621 1
7272 KCNS2 8_68 0.9085 26.21 0.0003787 -5.148 1
250 ANGEL1 14_36 0.9577 22.68 0.0003453 4.529 2
13239 N4BP2L2 13_11 0.8441 24.34 0.0003267 -4.430 1
6918 ARL14EP 11_21 0.8715 23.28 0.0003226 -4.878 2
4009 KBTBD4 11_29 0.7474 26.46 0.0003145 -5.098 1
10555 UBE2E2 3_17 0.4986 39.49 0.0003131 6.081 1
10030 FAM89B 11_36 0.7961 24.33 0.0003080 5.011 1
13467 RP11-419C23.1 8_33 0.5066 37.39 0.0003012 -6.317 1
6645 CAMK2G 10_49 0.5495 32.01 0.0002796 5.013 1
11149 SLIT1 10_62 0.7054 24.45 0.0002742 4.797 2
7267 GDF6 8_67 0.7821 21.36 0.0002656 -4.372 1
3924 SEPT7 7_26 0.7751 20.71 0.0002553 -4.167 1
11712 PARVA 11_9 0.7846 20.35 0.0002539 -3.862 1
3988 ZC3H13 13_19 0.7732 20.56 0.0002527 4.310 1
3282 GRB14 2_100 0.7262 21.32 0.0002461 4.745 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
7048 JAZF1 7_23 0.010490 138.57 2.311e-05 -12.731 1
14285 RP11-395N3.2 2_133 0.035389 109.24 6.147e-05 -11.370 1
2723 WFS1 4_7 0.029567 53.23 2.502e-05 10.642 1
356 ANK1 8_36 0.040526 59.39 3.827e-05 8.667 1
6954 ZFP36L2 2_27 0.039417 55.29 3.465e-05 8.377 1
6449 CDKAL1 6_15 0.003752 40.70 2.428e-06 -8.192 1
14659 LINC01126 2_27 0.049134 47.85 3.738e-05 -7.520 2
3368 THADA 2_27 0.037436 45.75 2.723e-05 7.450 1
7490 UBE2Z 17_28 0.702743 47.08 5.260e-04 -7.392 1
11174 KCNJ11 11_12 0.022353 49.77 1.769e-05 7.231 1
11270 NCR3LG1 11_12 0.022725 49.31 1.782e-05 -7.169 2
8537 C2 6_26 0.219641 52.12 1.820e-04 7.149 1
1417 PABPC4 1_24 0.208389 47.85 1.585e-04 -7.054 1
943 ZZEF1 17_4 0.090687 43.96 6.339e-05 6.917 1
10726 BMP8A 1_24 0.090841 42.59 6.152e-05 6.868 1
10282 ZNF664 12_75 0.315661 43.32 2.174e-04 -6.452 1
12119 MICB 6_25 0.483138 29.84 2.292e-04 6.443 3
13467 RP11-419C23.1 8_33 0.506567 37.39 3.012e-04 -6.317 1
7491 SNF8 17_28 0.064662 43.17 4.438e-05 6.300 1
7368 AP3S2 15_41 0.392664 36.08 2.252e-04 6.273 2Comparing z scores and PIPs
[1] 0.009079 genename region_tag susie_pip mu2 PVE z num_eqtl
7048 JAZF1 7_23 0.010490 138.57 2.311e-05 -12.731 1
14285 RP11-395N3.2 2_133 0.035389 109.24 6.147e-05 -11.370 1
2723 WFS1 4_7 0.029567 53.23 2.502e-05 10.642 1
356 ANK1 8_36 0.040526 59.39 3.827e-05 8.667 1
6954 ZFP36L2 2_27 0.039417 55.29 3.465e-05 8.377 1
6449 CDKAL1 6_15 0.003752 40.70 2.428e-06 -8.192 1
14659 LINC01126 2_27 0.049134 47.85 3.738e-05 -7.520 2
3368 THADA 2_27 0.037436 45.75 2.723e-05 7.450 1
7490 UBE2Z 17_28 0.702743 47.08 5.260e-04 -7.392 1
11174 KCNJ11 11_12 0.022353 49.77 1.769e-05 7.231 1
11270 NCR3LG1 11_12 0.022725 49.31 1.782e-05 -7.169 2
8537 C2 6_26 0.219641 52.12 1.820e-04 7.149 1
1417 PABPC4 1_24 0.208389 47.85 1.585e-04 -7.054 1
943 ZZEF1 17_4 0.090687 43.96 6.339e-05 6.917 1
10726 BMP8A 1_24 0.090841 42.59 6.152e-05 6.868 1
10282 ZNF664 12_75 0.315661 43.32 2.174e-04 -6.452 1
12119 MICB 6_25 0.483138 29.84 2.292e-04 6.443 3
13467 RP11-419C23.1 8_33 0.506567 37.39 3.012e-04 -6.317 1
7491 SNF8 17_28 0.064662 43.17 4.438e-05 6.300 1
7368 AP3S2 15_41 0.392664 36.08 2.252e-04 6.273 2GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 34Uploading 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
47 Jansen type metaphyseal chondrodysplasia 0.02097 1/16 1/9703
69 Eiken Skeletal Dysplasia 0.02097 1/16 1/9703
71 Failure of Tooth Eruption, Primary 0.02097 1/16 1/9703
72 Chondrodysplasia, blomstrand type 0.02097 1/16 1/9703
76 MICROPHTHALMIA, ISOLATED 4 (disorder) 0.02097 1/16 1/9703
83 LEBER CONGENITAL AMAUROSIS 17 0.02097 1/16 1/9703
89 MULTIPLE SYNOSTOSES SYNDROME 4 0.02097 1/16 1/9703
48 Hyperargininemia 0.02933 1/16 2/9703
73 KLIPPEL-FEIL SYNDROME, AUTOSOMAL DOMINANT 0.02933 1/16 2/9703
79 MICROPHTHALMIA, ISOLATED, WITH COLOBOMA 6 0.02933 1/16 2/9703WebGestalt 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] 25#significance threshold for TWAS
print(sig_thresh)[1] 4.533#number of ctwas genes
length(ctwas_genes)[1] 34#number of TWAS genes
length(twas_genes)[1] 78#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
6245 MRPS5 2_57 0.6981 19.67 0.0002184 -3.810 2
11514 PRPF40A 2_91 0.5173 20.30 0.0001670 -3.928 1
11477 CPNE4 3_81 0.5763 21.37 0.0001958 4.312 1
10066 ZBTB38 3_86 0.5397 17.85 0.0001532 -3.085 1
7947 DCAF16 4_17 0.6680 21.59 0.0002293 -4.110 2
13258 LINC01184 5_78 0.6725 19.24 0.0002058 3.793 1
3924 SEPT7 7_26 0.7751 20.71 0.0002553 -4.167 1
11034 ZBTB6 9_63 0.5209 21.63 0.0001792 4.264 1
11712 PARVA 11_9 0.7846 20.35 0.0002539 -3.862 1
6672 EIF3M 11_22 0.5005 18.64 0.0001483 -3.890 1
3988 ZC3H13 13_19 0.7732 20.56 0.0002527 4.310 1
10469 TMEM132E 17_20 0.6954 21.33 0.0002358 -3.693 2
4665 ZNF236 18_45 0.6700 20.05 0.0002136 -4.227 2
2323 DNASE2 19_10 0.7718 18.79 0.0002306 -3.744 1
1860 CTSZ 20_34 0.6903 19.10 0.0002097 -3.496 2
7267 GDF6 8_67 0.7821 21.36 0.0002656 -4.372 1
4388 RNF6 13_6 0.6475 23.06 0.0002374 -4.341 1
13239 N4BP2L2 13_11 0.8441 24.34 0.0003267 -4.430 1
250 ANGEL1 14_36 0.9577 22.68 0.0003453 4.529 2#sensitivity / recall
print(sensitivity) ctwas TWAS
0.00000 0.02778 #specificity
print(specificity) ctwas TWAS
0.9960 0.9911 #precision / PPV
print(precision) ctwas TWAS
0.00000 0.02564
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