BMI - Brain Putamen 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] 11258#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1123 787 660 445 518 661 554 400 415 452 668 611 225 384 383 517
17 18 19 20 21 22
686 178 855 348 121 267 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 8841#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.7853Check convergence of parameters
| Version | Author | Date |
|---|---|---|
| e6bc169 | sq-96 | 2022-02-13 |
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0070376 0.0002893 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
19.50 17.87 #report sample size
print(sample_size)[1] 336107#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 11258 7535010#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.004596 0.115884 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.02674 16.29002Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
10175 ATP6V0C 16_2 0.9492 26.58 7.507e-05 -4.711 1
13394 NOL12 22_15 0.8865 62.62 1.652e-04 -4.504 2
241 ISL1 5_30 0.7868 24.88 5.824e-05 5.010 1
5250 FGD4 12_22 0.7584 23.51 5.305e-05 4.449 2
8817 EFEMP2 11_36 0.7570 52.91 1.192e-04 -8.201 1
5487 C18orf8 18_12 0.7461 53.49 1.187e-04 7.458 2
9562 ZADH2 18_44 0.7295 22.99 4.991e-05 4.278 1
13411 HIST1H2BE 6_20 0.7016 28.99 6.052e-05 -6.515 1
11599 FADS3 11_34 0.6986 25.36 5.272e-05 4.311 1
8733 RNASEH1 2_2 0.6955 26.12 5.405e-05 4.231 2
10490 SKOR1 15_31 0.6954 54.86 1.135e-04 -9.754 1
9657 TRAPPC5 19_7 0.6882 25.48 5.218e-05 4.065 2
12847 LINC01977 17_45 0.6839 28.29 5.756e-05 5.230 1
5878 ECE2 3_113 0.6831 30.11 6.120e-05 -5.287 1
666 CACNB1 17_23 0.6825 24.80 5.035e-05 3.883 1
9431 ERBB4 2_125 0.6817 6016.69 1.220e-02 -7.023 1
368 PHLPP2 16_38 0.6728 49.57 9.923e-05 4.619 1
12529 AP006621.5 11_1 0.6619 25.40 5.002e-05 -4.506 1
309 VRK2 2_38 0.6542 22.96 4.468e-05 3.879 2
6637 FBXL18 7_7 0.6341 24.60 4.642e-05 -4.562 2Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
10436 SLC38A3 3_35 0 70545 0 6.726 1
7563 CAMKV 3_35 0 55235 0 -9.848 1
7741 PSIP1 9_13 0 54061 0 7.951 1
7742 CCDC171 9_13 0 54049 0 7.979 1
2148 PIK3R2 19_14 0 49133 0 -7.140 1
36 RBM6 3_35 0 42639 0 12.536 1
7565 MST1R 3_35 0 36400 0 -12.626 2
9443 STX19 3_59 0 32288 0 -5.060 1
5360 MFAP1 15_16 0 24650 0 4.303 1
12170 HYPK 15_16 0 24544 0 4.322 1
7560 RNF123 3_35 0 24100 0 -10.959 1
5186 TMOD3 15_21 0 19482 0 -5.412 1
3086 PLCL1 2_117 0 19300 0 -5.642 1
5884 CENPC 4_47 0 19277 0 5.863 2
12210 NAT6 3_35 0 18820 0 -6.264 2
7603 RNF180 5_39 0 18492 0 -3.745 2
7962 LEO1 15_21 0 18380 0 2.536 2
5088 TUBGCP4 15_16 0 17595 0 3.371 1
1042 CCNT2 2_80 0 17196 0 4.382 2
1422 MAST3 19_14 0 16401 0 2.208 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
9431 ERBB4 2_125 0.681733 6016.69 1.220e-02 -7.023 1
13394 NOL12 22_15 0.886527 62.62 1.652e-04 -4.504 2
8817 EFEMP2 11_36 0.757011 52.91 1.192e-04 -8.201 1
6710 GPR61 1_67 0.508658 78.53 1.188e-04 8.755 1
5487 C18orf8 18_12 0.746061 53.49 1.187e-04 7.458 2
10490 SKOR1 15_31 0.695443 54.86 1.135e-04 -9.754 1
5219 G3BP2 4_51 0.304950 123.45 1.120e-04 -2.134 1
368 PHLPP2 16_38 0.672814 49.57 9.923e-05 4.619 1
12412 RP11-1348G14.4 16_23 0.312488 102.15 9.497e-05 10.740 1
13154 CTC-498M16.4 5_52 0.005306 5461.35 8.622e-05 7.706 1
12235 GS1-259H13.2 7_62 0.526231 50.82 7.957e-05 -7.078 1
7903 TRMT61A 14_54 0.615046 41.71 7.633e-05 6.576 2
10175 ATP6V0C 16_2 0.949227 26.58 7.507e-05 -4.711 1
9806 KCNB2 8_53 0.374727 62.19 6.933e-05 -8.041 2
4200 NECTIN2 19_31 0.614744 33.79 6.180e-05 5.114 1
5878 ECE2 3_113 0.683096 30.11 6.120e-05 -5.287 1
13411 HIST1H2BE 6_20 0.701582 28.99 6.052e-05 -6.515 1
241 ISL1 5_30 0.786770 24.88 5.824e-05 5.010 1
12847 LINC01977 17_45 0.683924 28.29 5.756e-05 5.230 1
8100 ZNF646 16_24 0.230618 79.70 5.468e-05 -10.092 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
7565 MST1R 3_35 0.000e+00 36399.62 0.000e+00 -12.626 2
36 RBM6 3_35 0.000e+00 42639.01 0.000e+00 12.536 1
9065 KCTD13 16_24 1.062e-01 110.12 3.478e-05 11.491 1
7560 RNF123 3_35 0.000e+00 24100.49 0.000e+00 -10.959 1
8425 INO80E 16_24 3.312e-02 96.97 9.555e-06 10.849 2
12412 RP11-1348G14.4 16_23 3.125e-01 102.15 9.497e-05 10.740 1
10750 SULT1A2 16_23 9.533e-02 104.71 2.970e-05 -10.557 2
10461 CLN3 16_23 4.595e-02 99.79 1.364e-05 10.453 1
9180 NUPR1 16_23 8.732e-02 109.54 2.846e-05 -10.442 2
8100 ZNF646 16_24 2.306e-01 79.70 5.468e-05 -10.092 1
8099 ZNF668 16_24 7.753e-02 77.16 1.780e-05 10.000 1
8773 C1QTNF4 11_29 2.139e-02 94.05 5.987e-06 9.960 2
7563 CAMKV 3_35 0.000e+00 55235.03 0.000e+00 -9.848 1
454 PRSS8 16_24 1.517e-02 71.97 3.248e-06 -9.765 1
10490 SKOR1 15_31 6.954e-01 54.86 1.135e-04 -9.754 1
11425 NDUFS3 11_29 1.196e-02 84.08 2.993e-06 -9.609 2
11430 LAT 16_23 5.639e-02 95.10 1.596e-05 -9.553 1
2537 MTCH2 11_29 1.005e-02 83.11 2.485e-06 -9.551 1
10677 FAM180B 11_29 9.653e-03 82.29 2.363e-06 -9.477 2
12260 LINC00461 5_52 4.937e-11 348.10 5.113e-14 9.418 1Comparing z scores and PIPs
[1] 0.0215 genename region_tag susie_pip mu2 PVE z num_eqtl
7565 MST1R 3_35 0.000e+00 36399.62 0.000e+00 -12.626 2
36 RBM6 3_35 0.000e+00 42639.01 0.000e+00 12.536 1
9065 KCTD13 16_24 1.062e-01 110.12 3.478e-05 11.491 1
7560 RNF123 3_35 0.000e+00 24100.49 0.000e+00 -10.959 1
8425 INO80E 16_24 3.312e-02 96.97 9.555e-06 10.849 2
12412 RP11-1348G14.4 16_23 3.125e-01 102.15 9.497e-05 10.740 1
10750 SULT1A2 16_23 9.533e-02 104.71 2.970e-05 -10.557 2
10461 CLN3 16_23 4.595e-02 99.79 1.364e-05 10.453 1
9180 NUPR1 16_23 8.732e-02 109.54 2.846e-05 -10.442 2
8100 ZNF646 16_24 2.306e-01 79.70 5.468e-05 -10.092 1
8099 ZNF668 16_24 7.753e-02 77.16 1.780e-05 10.000 1
8773 C1QTNF4 11_29 2.139e-02 94.05 5.987e-06 9.960 2
7563 CAMKV 3_35 0.000e+00 55235.03 0.000e+00 -9.848 1
454 PRSS8 16_24 1.517e-02 71.97 3.248e-06 -9.765 1
10490 SKOR1 15_31 6.954e-01 54.86 1.135e-04 -9.754 1
11425 NDUFS3 11_29 1.196e-02 84.08 2.993e-06 -9.609 2
11430 LAT 16_23 5.639e-02 95.10 1.596e-05 -9.553 1
2537 MTCH2 11_29 1.005e-02 83.11 2.485e-06 -9.551 1
10677 FAM180B 11_29 9.653e-03 82.29 2.363e-06 -9.477 2
12260 LINC00461 5_52 4.937e-11 348.10 5.113e-14 9.418 1GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 33Uploading 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
31 Neoplasm Recurrence, Local
85 CHARCOT-MARIE-TOOTH DISEASE, TYPE 4H
86 Familial encephalopathy with neuroserpin inclusion bodies
93 CUTIS LAXA, AUTOSOMAL RECESSIVE, TYPE IB
96 AMYOTROPHIC LATERAL SCLEROSIS 19
97 CONE-ROD DYSTROPHY 20
98 PROGRESSIVE EXTERNAL OPHTHALMOPLEGIA WITH MITOCHONDRIAL DNA DELETIONS, AUTOSOMAL RECESSIVE 2
9 Bladder Exstrophy
20 Herpes Simplex Infections
52 Cutis Laxa, Autosomal Recessive, Type I
FDR Ratio BgRatio
31 0.02082 2/14 39/9703
85 0.02082 1/14 1/9703
86 0.02082 1/14 1/9703
93 0.02082 1/14 1/9703
96 0.02082 1/14 1/9703
97 0.02082 1/14 1/9703
98 0.02082 1/14 1/9703
9 0.02241 1/14 2/9703
20 0.02241 1/14 2/9703
52 0.02241 1/14 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
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] 27#significance threshold for TWAS
print(sig_thresh)[1] 4.59#number of ctwas genes
length(ctwas_genes)[1] 2#number of TWAS genes
length(twas_genes)[1] 242#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
13394 NOL12 22_15 0.8865 62.62 0.0001652 -4.504 2#sensitivity / recall
print(sensitivity) ctwas TWAS
0.00000 0.07317 #specificity
print(specificity) ctwas TWAS
0.9998 0.9787 #precision / PPV
print(precision) ctwas TWAS
0.0000 0.0124 
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