BMI - Brain Nucleus accumbens basal ganglia
sheng Qian
2021-2-6
Last updated: 2022-02-21
Checks: 6 1
Knit directory: cTWAS_analysis/
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Weight QC
#number of imputed weights
nrow(qclist_all)[1] 11487#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1134 801 689 451 561 646 551 431 414 460 695 611 220 384 375 529
17 18 19 20 21 22
672 184 903 357 131 288 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 9066#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.7892Check convergence of parameters
| Version | Author | Date |
|---|---|---|
| e6bc169 | sq-96 | 2022-02-13 |
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0080450 0.0002899 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
20.23 17.57 #report sample size
print(sample_size)[1] 336107#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 11487 7535010#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.005563 0.114195 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.05016 17.88795Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
8160 SMAD3 15_31 1.0000 8015.87 2.385e-02 -2.853 2
9866 TMIE 3_33 0.9990 35.00 1.040e-04 -7.092 2
3378 CCND2 12_4 0.9649 28.86 8.286e-05 -5.120 1
9479 NIM1K 5_28 0.9385 3262.53 9.110e-03 5.551 2
7777 ZNF12 7_9 0.9364 27.18 7.571e-05 5.127 2
13756 NOL12 22_15 0.8990 61.45 1.644e-04 -4.511 2
7320 TAL1 1_29 0.7874 49.34 1.156e-04 -6.866 1
1678 NINL 20_19 0.7831 32.88 7.659e-05 -5.600 2
12851 CYP2A6 19_28 0.7800 21.54 4.999e-05 -3.990 1
13574 RP11-108M9.6 1_12 0.7772 28.35 6.556e-05 -4.915 1
7010 NBL1 1_13 0.7556 34.04 7.653e-05 -5.638 1
4404 TUBG1 17_25 0.7230 30.27 6.512e-05 -5.660 1
13759 RP11-823E8.3 12_54 0.7000 103.66 2.159e-04 -6.438 1
4137 PODXL 7_80 0.6950 26.19 5.416e-05 4.018 1
9422 SOX11 2_4 0.6665 26.53 5.261e-05 4.517 1
7829 YWHAZ 8_69 0.6476 22.78 4.389e-05 4.235 1
5519 CDH13 16_46 0.6397 23.81 4.533e-05 -4.826 1
1132 RRN3 16_15 0.6239 22.14 4.110e-05 4.374 1
12949 RP11-566J3.2 14_52 0.6215 34.21 6.326e-05 -5.489 1
2821 SENP6 6_52 0.6091 25.38 4.600e-05 -4.618 2Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
10604 SLC38A3 3_35 0.000e+00 68426 0.00e+00 6.726 1
7846 CCDC171 9_13 0.000e+00 52441 0.00e+00 7.973 2
7671 CAMKV 3_35 0.000e+00 52028 0.00e+00 -9.574 2
2153 PIK3R2 19_14 0.000e+00 47755 0.00e+00 -7.140 1
36 RBM6 3_35 0.000e+00 41376 0.00e+00 12.536 1
7673 MST1R 3_35 0.000e+00 35329 0.00e+00 -12.623 2
9620 STX19 3_59 0.000e+00 31329 0.00e+00 -5.060 1
5542 NOB1 16_37 0.000e+00 25285 0.00e+00 2.763 1
10375 GSAP 7_49 0.000e+00 24183 0.00e+00 5.120 3
5442 MFAP1 15_16 4.269e-12 23938 3.04e-13 4.303 1
7668 RNF123 3_35 0.000e+00 23403 0.00e+00 -10.957 1
5264 TMOD3 15_21 0.000e+00 22653 0.00e+00 -4.222 1
5446 LYSMD2 15_21 0.000e+00 22305 0.00e+00 -4.403 1
1353 WDR76 15_16 0.000e+00 21498 0.00e+00 4.809 2
901 MCM6 2_80 0.000e+00 18038 0.00e+00 -3.886 1
2155 PDE4C 19_14 0.000e+00 17891 0.00e+00 6.668 1
5158 TUBGCP4 15_16 0.000e+00 16376 0.00e+00 3.555 2
1434 MAST3 19_14 0.000e+00 15939 0.00e+00 -2.208 1
9613 NSUN3 3_59 0.000e+00 15792 0.00e+00 4.755 1
8457 LCMT2 15_16 0.000e+00 14935 0.00e+00 -2.861 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
8160 SMAD3 15_31 1.00000 8015.87 2.385e-02 -2.853 2
9479 NIM1K 5_28 0.93854 3262.53 9.110e-03 5.551 2
13498 CTC-498M16.4 5_52 0.05807 5398.40 9.328e-04 7.883 1
10849 TTC30B 2_107 0.26101 752.00 5.840e-04 -3.137 1
7062 ADPGK 15_34 0.08467 1200.49 3.024e-04 5.925 3
13759 RP11-823E8.3 12_54 0.70005 103.66 2.159e-04 -6.438 1
10660 SKOR1 15_31 0.60658 97.96 1.768e-04 -9.880 2
13756 NOL12 22_15 0.89905 61.45 1.644e-04 -4.511 2
7320 TAL1 1_29 0.78744 49.34 1.156e-04 -6.866 1
9866 TMIE 3_33 0.99898 35.00 1.040e-04 -7.092 2
13962 DHRS11 17_22 0.53374 61.96 9.840e-05 -8.128 1
8419 ATXN2L 16_23 0.36167 77.23 8.310e-05 -10.702 1
3378 CCND2 12_4 0.96490 28.86 8.286e-05 -5.120 1
5575 C18orf8 18_12 0.48219 57.71 8.279e-05 7.477 2
1678 NINL 20_19 0.78308 32.88 7.659e-05 -5.600 2
7010 NBL1 1_13 0.75559 34.04 7.653e-05 -5.638 1
7777 ZNF12 7_9 0.93640 27.18 7.571e-05 5.127 2
5380 SUOX 12_35 0.53799 47.10 7.539e-05 -5.191 1
385 PHLPP2 16_38 0.57431 41.84 7.149e-05 4.619 1
13574 RP11-108M9.6 1_12 0.77719 28.35 6.556e-05 -4.915 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
7673 MST1R 3_35 0.000e+00 35329.04 0.000e+00 -12.623 2
36 RBM6 3_35 0.000e+00 41375.93 0.000e+00 12.536 1
7668 RNF123 3_35 0.000e+00 23402.76 0.000e+00 -10.957 1
8554 INO80E 16_24 3.166e-02 99.46 9.370e-06 10.886 2
8419 ATXN2L 16_23 3.617e-01 77.23 8.310e-05 -10.702 1
10629 CLN3 16_23 4.170e-02 75.23 9.334e-06 10.363 2
10875 C6orf106 6_28 2.561e-05 120.41 9.173e-09 -10.264 1
12178 NPIPB7 16_23 3.663e-02 74.39 8.107e-06 10.038 1
8218 ZNF668 16_24 1.121e-01 77.19 2.575e-05 10.000 1
10660 SKOR1 15_31 6.066e-01 97.96 1.768e-04 -9.880 2
9435 NFATC2IP 16_23 4.577e-02 75.98 1.035e-05 -9.863 1
4330 ZC3H4 19_33 5.248e-03 98.99 1.546e-06 9.849 1
1906 KAT8 16_24 1.702e-02 72.07 3.650e-06 -9.785 1
1905 BCKDK 16_24 1.243e-02 68.65 2.539e-06 9.638 1
11636 NDUFS3 11_29 1.310e-02 84.89 3.309e-06 -9.629 2
7671 CAMKV 3_35 0.000e+00 52028.02 0.000e+00 -9.574 2
8924 C1QTNF4 11_29 1.137e-02 83.82 2.836e-06 9.564 1
11640 LAT 16_23 7.245e-02 77.05 1.661e-05 -9.553 1
10852 FAM180B 11_29 1.083e-02 82.28 2.652e-06 -9.490 1
2550 MTCH2 11_29 1.053e-02 81.11 2.540e-06 -9.432 1Comparing z scores and PIPs
[1] 0.02002 genename region_tag susie_pip mu2 PVE z num_eqtl
7673 MST1R 3_35 0.000e+00 35329.04 0.000e+00 -12.623 2
36 RBM6 3_35 0.000e+00 41375.93 0.000e+00 12.536 1
7668 RNF123 3_35 0.000e+00 23402.76 0.000e+00 -10.957 1
8554 INO80E 16_24 3.166e-02 99.46 9.370e-06 10.886 2
8419 ATXN2L 16_23 3.617e-01 77.23 8.310e-05 -10.702 1
10629 CLN3 16_23 4.170e-02 75.23 9.334e-06 10.363 2
10875 C6orf106 6_28 2.561e-05 120.41 9.173e-09 -10.264 1
12178 NPIPB7 16_23 3.663e-02 74.39 8.107e-06 10.038 1
8218 ZNF668 16_24 1.121e-01 77.19 2.575e-05 10.000 1
10660 SKOR1 15_31 6.066e-01 97.96 1.768e-04 -9.880 2
9435 NFATC2IP 16_23 4.577e-02 75.98 1.035e-05 -9.863 1
4330 ZC3H4 19_33 5.248e-03 98.99 1.546e-06 9.849 1
1906 KAT8 16_24 1.702e-02 72.07 3.650e-06 -9.785 1
1905 BCKDK 16_24 1.243e-02 68.65 2.539e-06 9.638 1
11636 NDUFS3 11_29 1.310e-02 84.89 3.309e-06 -9.629 2
7671 CAMKV 3_35 0.000e+00 52028.02 0.000e+00 -9.574 2
8924 C1QTNF4 11_29 1.137e-02 83.82 2.836e-06 9.564 1
11640 LAT 16_23 7.245e-02 77.05 1.661e-05 -9.553 1
10852 FAM180B 11_29 1.083e-02 82.28 2.652e-06 -9.490 1
2550 MTCH2 11_29 1.053e-02 81.11 2.540e-06 -9.432 1GO 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
49 Lung Neoplasms 0.02168 4/17 265/9703
61 Opisthorchiasis 0.02168 1/17 1/9703
65 Prostatic Neoplasms 0.02168 6/17 616/9703
66 Pulmonary Emphysema 0.02168 2/17 17/9703
110 Malignant neoplasm of lung 0.02168 4/17 266/9703
115 Sulfite oxidase deficiency 0.02168 1/17 1/9703
117 Opisthorchis felineus Infection 0.02168 1/17 1/9703
118 Opisthorchis viverrini Infection 0.02168 1/17 1/9703
122 Malignant neoplasm of prostate 0.02168 6/17 616/9703
156 DEAFNESS, AUTOSOMAL RECESSIVE 6 0.02168 1/17 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
Warning: ggrepel: 7 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
Sensitivity, specificity and precision for silver standard genes
#number of genes in known annotations
print(length(known_annotations))[1] 41#number of genes in known annotations with imputed expression
print(sum(known_annotations %in% ctwas_gene_res$genename))[1] 24#significance threshold for TWAS
print(sig_thresh)[1] 4.594#number of ctwas genes
length(ctwas_genes)[1] 6#number of TWAS genes
length(twas_genes)[1] 230#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
8160 SMAD3 15_31 1.000 8015.87 0.0238492 -2.853 2
13756 NOL12 22_15 0.899 61.45 0.0001644 -4.511 2#sensitivity / recall
print(sensitivity) ctwas TWAS
0.00000 0.04878 #specificity
print(specificity) ctwas TWAS
0.9995 0.9801 #precision / PPV
print(precision) ctwas TWAS
0.000000 0.008696 
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_1.0.16
[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_1.1.1 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 cli_3.1.0 rvest_1.0.2 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_0.4.12 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_2.0.1 Matrix_1.2-18 ggbeeswarm_0.6.0 Rcpp_1.0.7
[49] munsell_0.5.0 fansi_0.5.0 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_1.4.2 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_1.5.1 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_1.5.1 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_1.0.13 beeswarm_0.2.3 memoise_2.0.1 ellipsis_0.3.2