T2D - Pancreas
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] 7605#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
748 581 480 310 401 436 387 270 287 327 513 475 161 273 282 292 407 119 367 228
21 22
92 169 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 4452#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.5854Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0123097 0.0003667 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
8.181 8.890 #report sample size
print(sample_size)[1] 62892#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 7605 5017190#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01218 0.26008 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.06992 1.44417Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
5708 SCRN2 17_28 0.8107 23.10 0.0002977 -4.970 2
3452 ARG1 6_87 0.7825 28.71 0.0003572 -5.418 2
8741 ONECUT1 15_22 0.7596 27.19 0.0003284 5.079 1
10062 ARL6IP4 12_75 0.7433 36.01 0.0004257 5.620 1
11158 PARVA 11_9 0.7138 21.89 0.0002484 -3.862 2
2227 DNASE2 19_10 0.7083 19.09 0.0002150 -3.744 1
4461 ZNF236 18_45 0.7059 20.70 0.0002323 -4.378 1
7604 CFAP221 2_69 0.6764 20.29 0.0002183 -4.050 2
10527 GSAP 7_49 0.6505 24.56 0.0002541 -4.185 1
183 GIPR 19_32 0.6326 36.32 0.0003653 -6.632 1
1451 CWF19L1 10_64 0.6280 33.14 0.0003309 -5.803 2
12493 LINC01184 5_78 0.5774 20.30 0.0001864 3.793 1
4519 TUBG1 17_25 0.5616 23.49 0.0002098 5.268 2
6236 CRIP3 6_33 0.5552 21.66 0.0001912 4.545 2
2221 MIER2 19_1 0.5414 23.96 0.0002063 3.684 1
6019 MRPS5 2_57 0.5399 21.72 0.0001865 -3.737 1
11243 ZNF251 8_94 0.5343 24.42 0.0002075 -4.886 1
1411 TYRO3 15_15 0.5096 23.09 0.0001871 4.866 1
3830 KBTBD4 11_29 0.4950 25.44 0.0002003 -5.098 1
12664 LINC01933 5_89 0.4651 20.74 0.0001534 3.780 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
6795 JAZF1 7_23 0.0253350 147.34 5.935e-05 -13.082 2
474 BCAR1 16_40 0.0891404 58.62 8.308e-05 -7.720 1
10584 ARAP1 11_41 0.0192136 57.43 1.754e-05 7.886 1
6345 CDKN2B 9_16 0.0874433 55.91 7.773e-05 -8.192 1
13639 LINC01126 2_27 0.0310930 53.94 2.667e-05 -8.377 1
10742 NCR3LG1 11_12 0.0210812 51.25 1.718e-05 -7.370 1
7991 NKX6-3 8_36 0.4571379 42.55 3.093e-04 -6.781 2
9232 PEAK1 15_36 0.4152426 40.39 2.667e-04 6.885 1
7192 ATP5G1 17_28 0.0676819 39.63 4.264e-05 6.400 1
11440 RBM20 10_70 0.0002241 38.43 1.369e-07 -3.000 1
7194 SNF8 17_28 0.0573206 37.71 3.437e-05 6.300 1
1291 P2RX7 12_74 0.2975092 37.49 1.773e-04 5.484 2
10224 BMP8A 1_24 0.0347387 37.42 2.067e-05 6.296 1
7087 AP3S2 15_41 0.3727948 37.36 2.215e-04 6.356 1
183 GIPR 19_32 0.6326493 36.32 3.653e-04 -6.632 1
10062 ARL6IP4 12_75 0.7433361 36.01 4.257e-04 5.620 1
10674 HAPLN4 19_15 0.0946101 35.81 5.387e-05 5.347 1
11533 MICB 6_25 0.0933549 33.28 4.940e-05 5.546 2
1451 CWF19L1 10_64 0.6279579 33.14 3.309e-04 -5.803 2
8540 TAP1 6_27 0.0647764 32.27 3.323e-05 -5.575 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
10062 ARL6IP4 12_75 0.7433 36.01 0.0004257 5.620 1
183 GIPR 19_32 0.6326 36.32 0.0003653 -6.632 1
3452 ARG1 6_87 0.7825 28.71 0.0003572 -5.418 2
1451 CWF19L1 10_64 0.6280 33.14 0.0003309 -5.803 2
8741 ONECUT1 15_22 0.7596 27.19 0.0003284 5.079 1
7991 NKX6-3 8_36 0.4571 42.55 0.0003093 -6.781 2
5708 SCRN2 17_28 0.8107 23.10 0.0002977 -4.970 2
9232 PEAK1 15_36 0.4152 40.39 0.0002667 6.885 1
10527 GSAP 7_49 0.6505 24.56 0.0002541 -4.185 1
11158 PARVA 11_9 0.7138 21.89 0.0002484 -3.862 2
4461 ZNF236 18_45 0.7059 20.70 0.0002323 -4.378 1
7087 AP3S2 15_41 0.3728 37.36 0.0002215 6.356 1
7604 CFAP221 2_69 0.6764 20.29 0.0002183 -4.050 2
2227 DNASE2 19_10 0.7083 19.09 0.0002150 -3.744 1
4519 TUBG1 17_25 0.5616 23.49 0.0002098 5.268 2
11243 ZNF251 8_94 0.5343 24.42 0.0002075 -4.886 1
2221 MIER2 19_1 0.5414 23.96 0.0002063 3.684 1
3830 KBTBD4 11_29 0.4950 25.44 0.0002003 -5.098 1
6236 CRIP3 6_33 0.5552 21.66 0.0001912 4.545 2
1411 TYRO3 15_15 0.5096 23.09 0.0001871 4.866 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
6795 JAZF1 7_23 0.02534 147.34 5.935e-05 -13.082 2
13639 LINC01126 2_27 0.03109 53.94 2.667e-05 -8.377 1
6345 CDKN2B 9_16 0.08744 55.91 7.773e-05 -8.192 1
10584 ARAP1 11_41 0.01921 57.43 1.754e-05 7.886 1
474 BCAR1 16_40 0.08914 58.62 8.308e-05 -7.720 1
10742 NCR3LG1 11_12 0.02108 51.25 1.718e-05 -7.370 1
9232 PEAK1 15_36 0.41524 40.39 2.667e-04 6.885 1
7991 NKX6-3 8_36 0.45714 42.55 3.093e-04 -6.781 2
183 GIPR 19_32 0.63265 36.32 3.653e-04 -6.632 1
7192 ATP5G1 17_28 0.06768 39.63 4.264e-05 6.400 1
7087 AP3S2 15_41 0.37279 37.36 2.215e-04 6.356 1
7194 SNF8 17_28 0.05732 37.71 3.437e-05 6.300 1
10224 BMP8A 1_24 0.03474 37.42 2.067e-05 6.296 1
1451 CWF19L1 10_64 0.62796 33.14 3.309e-04 -5.803 2
10062 ARL6IP4 12_75 0.74334 36.01 4.257e-04 5.620 1
3128 NRBP1 2_16 0.02609 31.31 1.298e-05 -5.595 1
8540 TAP1 6_27 0.06478 32.27 3.323e-05 -5.575 1
11533 MICB 6_25 0.09335 33.28 4.940e-05 5.546 2
3134 PPM1G 2_16 0.02637 30.93 1.297e-05 -5.544 1
1291 P2RX7 12_74 0.29751 37.49 1.773e-04 5.484 2Comparing z scores and PIPs
[1] 0.00618 genename region_tag susie_pip mu2 PVE z num_eqtl
6795 JAZF1 7_23 0.02534 147.34 5.935e-05 -13.082 2
13639 LINC01126 2_27 0.03109 53.94 2.667e-05 -8.377 1
6345 CDKN2B 9_16 0.08744 55.91 7.773e-05 -8.192 1
10584 ARAP1 11_41 0.01921 57.43 1.754e-05 7.886 1
474 BCAR1 16_40 0.08914 58.62 8.308e-05 -7.720 1
10742 NCR3LG1 11_12 0.02108 51.25 1.718e-05 -7.370 1
9232 PEAK1 15_36 0.41524 40.39 2.667e-04 6.885 1
7991 NKX6-3 8_36 0.45714 42.55 3.093e-04 -6.781 2
183 GIPR 19_32 0.63265 36.32 3.653e-04 -6.632 1
7192 ATP5G1 17_28 0.06768 39.63 4.264e-05 6.400 1
7087 AP3S2 15_41 0.37279 37.36 2.215e-04 6.356 1
7194 SNF8 17_28 0.05732 37.71 3.437e-05 6.300 1
10224 BMP8A 1_24 0.03474 37.42 2.067e-05 6.296 1
1451 CWF19L1 10_64 0.62796 33.14 3.309e-04 -5.803 2
10062 ARL6IP4 12_75 0.74334 36.01 4.257e-04 5.620 1
3128 NRBP1 2_16 0.02609 31.31 1.298e-05 -5.595 1
8540 TAP1 6_27 0.06478 32.27 3.323e-05 -5.575 1
11533 MICB 6_25 0.09335 33.28 4.940e-05 5.546 2
3134 PPM1G 2_16 0.02637 30.93 1.297e-05 -5.544 1
1291 P2RX7 12_74 0.29751 37.49 1.773e-04 5.484 2GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 18Uploading 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
50 CORTICAL DYSPLASIA, COMPLEX, WITH OTHER BRAIN MALFORMATIONS 4 0.03041 1/10
53 SPINOCEREBELLAR ATAXIA, AUTOSOMAL RECESSIVE 17 0.03041 1/10
32 Hyperargininemia 0.04052 1/10
11 Hair Diseases 0.04557 1/10
1 Amino Acid Metabolism, Inborn Errors 0.06042 1/10
2 Asbestosis 0.06042 1/10
4 Body Weight 0.06042 1/10
10 Glomerulonephritis, Membranoproliferative 0.06042 1/10
13 Immune System Diseases 0.06042 1/10
15 Leishmaniasis 0.06042 1/10
BgRatio
50 1/9703
53 1/9703
32 2/9703
11 3/9703
1 13/9703
2 14/9703
4 15/9703
10 7/9703
13 7/9703
15 9/9703WebGestalt 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] 27#significance threshold for TWAS
print(sig_thresh)[1] 4.507#number of ctwas genes
length(ctwas_genes)[1] 18#number of TWAS genes
length(twas_genes)[1] 47#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
6019 MRPS5 2_57 0.5399 21.72 0.0001865 -3.737 1
12493 LINC01184 5_78 0.5774 20.30 0.0001864 3.793 1
10527 GSAP 7_49 0.6505 24.56 0.0002541 -4.185 1
11158 PARVA 11_9 0.7138 21.89 0.0002484 -3.862 2
4461 ZNF236 18_45 0.7059 20.70 0.0002323 -4.378 1
2221 MIER2 19_1 0.5414 23.96 0.0002063 3.684 1
2227 DNASE2 19_10 0.7083 19.09 0.0002150 -3.744 1
7604 CFAP221 2_69 0.6764 20.29 0.0002183 -4.050 2#sensitivity / recall
print(sensitivity) ctwas TWAS
0.01389 0.01389 #specificity
print(specificity) ctwas TWAS
0.9978 0.9939 #precision / PPV
print(precision) ctwas TWAS
0.05556 0.02128
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