T2D - Liver
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] 6749#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
693 529 449 280 351 381 339 268 267 299 405 423 131 247 240 250 343 112 336 180
21 22
67 159 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 4358#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.6457Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0121777 0.0003704 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
8.728 8.899 #report sample size
print(sample_size)[1] 62892#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 6749 5017190#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01141 0.26295 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.06078 1.45686Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
5632 CAND2 3_9 0.8555 22.92 0.0003117 -4.854 1
7788 NCKAP5L 12_31 0.8257 27.05 0.0003551 5.000 1
3444 GTF3A 13_7 0.7913 23.01 0.0002896 -4.478 2
11216 CYP21A2 6_26 0.7664 38.72 0.0004718 7.835 1
11883 RP11-209K10.2 15_22 0.7522 27.46 0.0003284 -5.056 1
7394 TP53INP1 8_66 0.7463 25.33 0.0003006 -5.474 1
6171 ARL14EP 11_21 0.7361 22.13 0.0002590 -4.512 3
10272 PARVA 11_9 0.7148 21.99 0.0002500 3.862 1
3551 KBTBD4 11_29 0.7095 26.51 0.0002990 -5.098 1
2050 DNASE2 19_10 0.7071 19.19 0.0002157 -3.744 1
4127 ZNF236 18_45 0.6921 20.89 0.0002298 -4.378 1
8335 CLSTN1 1_7 0.6180 20.20 0.0001985 3.978 1
6831 RPL8 8_94 0.6080 26.54 0.0002566 -5.063 1
1320 CWF19L1 10_64 0.5714 32.84 0.0002984 -5.742 2
9797 SLIT1 10_62 0.5606 23.74 0.0002116 4.762 1
6558 AP3S2 15_41 0.5570 39.32 0.0003483 6.483 1
8968 ALS2CL 3_33 0.5364 22.70 0.0001936 3.405 1
5574 MRPS5 2_57 0.5288 22.15 0.0001862 -3.737 1
11765 RP11-110I1.12 11_71 0.5256 18.71 0.0001563 3.747 1
5773 CRIP3 6_33 0.5254 21.27 0.0001777 4.511 2Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
9887 NCR3LG1 11_12 0.02703 67.63 2.907e-05 -8.447 2
12661 LINC01126 2_27 0.03031 54.49 2.626e-05 -8.377 1
6291 JAZF1 7_23 0.01529 42.70 1.038e-05 -6.628 1
9311 UBE2E2 3_17 0.45738 39.65 2.883e-04 6.058 2
6558 AP3S2 15_41 0.55702 39.32 3.483e-04 6.483 1
6667 UBE2Z 17_28 0.05352 39.28 3.343e-05 -6.797 1
10351 TMEM229B 14_32 0.27272 38.81 1.683e-04 -3.685 2
11216 CYP21A2 6_26 0.76639 38.72 4.718e-04 7.835 1
4550 P2RX4 12_74 0.19404 38.50 1.188e-04 4.087 1
2084 RASA4 7_63 0.14390 33.96 7.771e-05 -4.470 1
10830 SYNJ2BP 14_32 0.15324 33.12 8.070e-05 -3.228 1
1320 CWF19L1 10_64 0.57140 32.84 2.984e-04 -5.742 2
2887 NRBP1 2_16 0.02462 32.74 1.281e-05 -5.595 1
6867 FMNL3 12_31 0.17840 32.21 9.137e-05 3.719 1
6223 GPR180 13_47 0.20330 31.88 1.030e-04 -3.353 1
8847 CCDC121 2_16 0.13639 31.87 6.912e-05 3.505 1
6456 ART3 4_51 0.22417 31.82 1.134e-04 -3.686 2
191 CEP68 2_42 0.50066 31.63 2.518e-04 6.229 2
7489 SDCCAG3 9_73 0.31043 31.39 1.549e-04 -3.739 2
9802 RP11-195F19.5 9_27 0.36489 31.33 1.818e-04 -3.408 2Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
11216 CYP21A2 6_26 0.7664 38.72 0.0004718 7.835 1
7788 NCKAP5L 12_31 0.8257 27.05 0.0003551 5.000 1
6558 AP3S2 15_41 0.5570 39.32 0.0003483 6.483 1
11883 RP11-209K10.2 15_22 0.7522 27.46 0.0003284 -5.056 1
5632 CAND2 3_9 0.8555 22.92 0.0003117 -4.854 1
7394 TP53INP1 8_66 0.7463 25.33 0.0003006 -5.474 1
3551 KBTBD4 11_29 0.7095 26.51 0.0002990 -5.098 1
1320 CWF19L1 10_64 0.5714 32.84 0.0002984 -5.742 2
3444 GTF3A 13_7 0.7913 23.01 0.0002896 -4.478 2
9311 UBE2E2 3_17 0.4574 39.65 0.0002883 6.058 2
6171 ARL14EP 11_21 0.7361 22.13 0.0002590 -4.512 3
6831 RPL8 8_94 0.6080 26.54 0.0002566 -5.063 1
191 CEP68 2_42 0.5007 31.63 0.0002518 6.229 2
10272 PARVA 11_9 0.7148 21.99 0.0002500 3.862 1
3522 BHLHE41 12_18 0.4800 30.18 0.0002304 5.640 1
4127 ZNF236 18_45 0.6921 20.89 0.0002298 -4.378 1
2050 DNASE2 19_10 0.7071 19.19 0.0002157 -3.744 1
9797 SLIT1 10_62 0.5606 23.74 0.0002116 4.762 1
10501 MAP3K3 17_37 0.4887 26.31 0.0002045 -5.170 1
8335 CLSTN1 1_7 0.6180 20.20 0.0001985 3.978 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
9887 NCR3LG1 11_12 0.02703 67.63 2.907e-05 -8.447 2
12661 LINC01126 2_27 0.03031 54.49 2.626e-05 -8.377 1
11216 CYP21A2 6_26 0.76639 38.72 4.718e-04 7.835 1
6667 UBE2Z 17_28 0.05352 39.28 3.343e-05 -6.797 1
6291 JAZF1 7_23 0.01529 42.70 1.038e-05 -6.628 1
6558 AP3S2 15_41 0.55702 39.32 3.483e-04 6.483 1
191 CEP68 2_42 0.50066 31.63 2.518e-04 6.229 2
9311 UBE2E2 3_17 0.45738 39.65 2.883e-04 6.058 2
10639 MICB 6_25 0.38273 28.87 1.757e-04 5.917 1
1320 CWF19L1 10_64 0.57140 32.84 2.984e-04 -5.742 2
3522 BHLHE41 12_18 0.48002 30.18 2.304e-04 5.640 1
2887 NRBP1 2_16 0.02462 32.74 1.281e-05 -5.595 1
11110 LTA 6_25 0.04071 27.69 1.793e-05 5.500 1
7394 TP53INP1 8_66 0.74634 25.33 3.006e-04 -5.474 1
326 ATP6V0A1 17_25 0.11561 26.56 4.882e-05 5.188 2
10501 MAP3K3 17_37 0.48875 26.31 2.045e-04 -5.170 1
3848 TSPAN8 12_44 0.24678 27.07 1.062e-04 5.137 1
3551 KBTBD4 11_29 0.70951 26.51 2.990e-04 -5.098 1
10594 PSMB8 6_27 0.20581 27.99 9.160e-05 5.081 1
6831 RPL8 8_94 0.60805 26.54 2.566e-04 -5.063 1Comparing z scores and PIPs
[1] 0.006075 genename region_tag susie_pip mu2 PVE z num_eqtl
9887 NCR3LG1 11_12 0.02703 67.63 2.907e-05 -8.447 2
12661 LINC01126 2_27 0.03031 54.49 2.626e-05 -8.377 1
11216 CYP21A2 6_26 0.76639 38.72 4.718e-04 7.835 1
6667 UBE2Z 17_28 0.05352 39.28 3.343e-05 -6.797 1
6291 JAZF1 7_23 0.01529 42.70 1.038e-05 -6.628 1
6558 AP3S2 15_41 0.55702 39.32 3.483e-04 6.483 1
191 CEP68 2_42 0.50066 31.63 2.518e-04 6.229 2
9311 UBE2E2 3_17 0.45738 39.65 2.883e-04 6.058 2
10639 MICB 6_25 0.38273 28.87 1.757e-04 5.917 1
1320 CWF19L1 10_64 0.57140 32.84 2.984e-04 -5.742 2
3522 BHLHE41 12_18 0.48002 30.18 2.304e-04 5.640 1
2887 NRBP1 2_16 0.02462 32.74 1.281e-05 -5.595 1
11110 LTA 6_25 0.04071 27.69 1.793e-05 5.500 1
7394 TP53INP1 8_66 0.74634 25.33 3.006e-04 -5.474 1
326 ATP6V0A1 17_25 0.11561 26.56 4.882e-05 5.188 2
10501 MAP3K3 17_37 0.48875 26.31 2.045e-04 -5.170 1
3848 TSPAN8 12_44 0.24678 27.07 1.062e-04 5.137 1
3551 KBTBD4 11_29 0.70951 26.51 2.990e-04 -5.098 1
10594 PSMB8 6_27 0.20581 27.99 9.160e-05 5.081 1
6831 RPL8 8_94 0.60805 26.54 2.566e-04 -5.063 1GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 21Uploading 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
22 Late onset congenital adrenal hyperplasia 0.01134
32 Hyperandrogenism, Nonclassic Type, due to 21-Hydroxylase Deficiency 0.01134
34 Congenital adrenal hyperplasia due to 21 hydroxylase deficiency 0.01134
38 SPINOCEREBELLAR ATAXIA, AUTOSOMAL RECESSIVE 17 0.01134
1 Congenital adrenal hyperplasia 0.05394
2 Atrial Fibrillation 0.05394
8 Glomerulonephritis, Membranoproliferative 0.05394
19 Paroxysmal atrial fibrillation 0.05394
33 Persistent atrial fibrillation 0.05394
35 familial atrial fibrillation 0.05394
Ratio BgRatio
22 1/11 1/9703
32 1/11 1/9703
34 1/11 1/9703
38 1/11 1/9703
1 1/11 9/9703
2 2/11 160/9703
8 1/11 7/9703
19 2/11 156/9703
33 2/11 156/9703
35 2/11 156/9703WebGestalt 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] 20#significance threshold for TWAS
print(sig_thresh)[1] 4.482#number of ctwas genes
length(ctwas_genes)[1] 21#number of TWAS genes
length(twas_genes)[1] 41#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
8335 CLSTN1 1_7 0.6180 20.20 0.0001985 3.978 1
5574 MRPS5 2_57 0.5288 22.15 0.0001862 -3.737 1
8968 ALS2CL 3_33 0.5364 22.70 0.0001936 3.405 1
10272 PARVA 11_9 0.7148 21.99 0.0002500 3.862 1
11765 RP11-110I1.12 11_71 0.5256 18.71 0.0001563 3.747 1
4127 ZNF236 18_45 0.6921 20.89 0.0002298 -4.378 1
2050 DNASE2 19_10 0.7071 19.19 0.0002157 -3.744 1
3444 GTF3A 13_7 0.7913 23.01 0.0002896 -4.478 2#sensitivity / recall
print(sensitivity)ctwas TWAS
0 0 #specificity
print(specificity) ctwas TWAS
0.9969 0.9939 #precision / PPV
print(precision)ctwas TWAS
0 0
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