BMI - Brain Amygdala
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] 10285#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1009 735 594 399 502 598 475 370 404 396 626 561 223 326 336 453
17 18 19 20 21 22
604 162 819 311 117 265 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 8365#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.8133Check convergence of parameters
| Version | Author | Date |
|---|---|---|
| e6bc169 | sq-96 | 2022-02-13 |
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0078519 0.0002945 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
22.61 17.40 #report sample size
print(sample_size)[1] 336107#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 10285 7535010#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.005432 0.114855 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.1859 16.1230Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
7067 PPM1M 3_36 1.0000 203.85 6.065e-04 4.323 2
9534 GSAP 7_49 1.0000 31328.56 9.321e-02 5.260 1
9062 TMIE 3_33 0.9989 35.23 1.047e-04 -6.902 2
3144 CCND2 12_4 0.9639 29.10 8.345e-05 -5.120 1
2362 B3GAT1 11_84 0.9273 25.66 7.080e-05 -4.502 2
7314 CASP7 10_71 0.8414 24.92 6.240e-05 4.584 1
1743 TSC2 16_2 0.8369 30.80 7.669e-05 5.278 1
10858 SLC12A8 3_77 0.8108 22.51 5.431e-05 -4.338 1
129 CELSR3 3_34 0.8058 57.17 1.370e-04 -7.731 1
4947 SUOX 12_35 0.7929 57.59 1.359e-04 -5.807 1
6745 TAL1 1_29 0.7916 49.63 1.169e-04 -6.866 1
7155 ZNF12 7_9 0.7764 25.85 5.972e-05 4.972 2
4469 HEY2 6_84 0.7704 33571.89 7.696e-02 4.930 1
12540 RP11-823E8.3 12_54 0.7460 31.13 6.909e-05 -6.438 1
8259 EFEMP2 11_36 0.7404 97.56 2.149e-04 -7.542 2
3049 PRRC2C 1_84 0.7402 28.99 6.384e-05 -5.173 1
2893 SLC1A4 2_42 0.7264 23.46 5.069e-05 -4.047 1
7589 NCKAP5L 12_31 0.7218 48.20 1.035e-04 -8.217 1
10874 VPS52 6_28 0.7097 125.93 2.659e-04 1.606 1
12020 LINC01977 17_45 0.7064 28.57 6.004e-05 5.230 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
9746 SLC38A3 3_35 0.000e+00 67724 0.000e+00 6.726 1
7061 CAMKV 3_35 0.000e+00 53039 0.000e+00 9.848 1
7212 CCDC171 9_13 0.000e+00 50688 0.000e+00 7.997 1
7063 MST1R 3_35 0.000e+00 34978 0.000e+00 -12.602 2
4469 HEY2 6_84 7.704e-01 33572 7.696e-02 4.930 1
8838 DHFR2 3_59 0.000e+00 32025 0.000e+00 5.146 1
9534 GSAP 7_49 1.000e+00 31329 9.321e-02 5.260 1
8841 STX19 3_59 0.000e+00 31018 0.000e+00 -5.060 1
7432 LEO1 15_21 2.077e-07 27409 1.694e-08 4.647 1
5003 LYSMD2 15_21 0.000e+00 26190 0.000e+00 -4.403 1
4997 MFAP1 15_16 1.608e-07 23703 1.134e-08 4.303 1
7058 RNF123 3_35 0.000e+00 23172 0.000e+00 -10.957 1
1259 WDR76 15_16 0.000e+00 21159 0.000e+00 4.859 2
9777 DPYD 1_60 0.000e+00 19618 0.000e+00 -3.213 1
849 MCM6 2_80 0.000e+00 17859 0.000e+00 -3.886 1
4751 TUBGCP4 15_16 0.000e+00 16922 0.000e+00 3.371 1
10116 ENTPD6 20_18 0.000e+00 16405 0.000e+00 -5.561 1
8836 NSUN3 3_59 0.000e+00 15636 0.000e+00 4.755 1
7782 ADAL 15_16 0.000e+00 14788 0.000e+00 -2.861 1
7783 LCMT2 15_16 0.000e+00 14376 0.000e+00 -3.087 2Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
9534 GSAP 7_49 1.0000 31328.56 9.321e-02 5.260 1
4469 HEY2 6_84 0.7704 33571.89 7.696e-02 4.930 1
9966 TTC30B 2_107 0.3199 757.36 7.208e-04 -3.137 1
7067 PPM1M 3_36 1.0000 203.85 6.065e-04 4.323 2
10874 VPS52 6_28 0.7097 125.93 2.659e-04 1.606 1
8259 EFEMP2 11_36 0.7404 97.56 2.149e-04 -7.542 2
7134 SFXN1 5_105 0.0708 1012.70 2.133e-04 -3.398 1
129 CELSR3 3_34 0.8058 57.17 1.370e-04 -7.731 1
4947 SUOX 12_35 0.7929 57.59 1.359e-04 -5.807 1
6745 TAL1 1_29 0.7916 49.63 1.169e-04 -6.866 1
9062 TMIE 3_33 0.9989 35.23 1.047e-04 -6.902 2
7589 NCKAP5L 12_31 0.7218 48.20 1.035e-04 -8.217 1
9164 KCNB2 8_53 0.4664 63.18 8.767e-05 -8.057 2
3144 CCND2 12_4 0.9639 29.10 8.345e-05 -5.120 1
1743 TSC2 16_2 0.8369 30.80 7.669e-05 5.278 1
2362 B3GAT1 11_84 0.9273 25.66 7.080e-05 -4.502 2
12540 RP11-823E8.3 12_54 0.7460 31.13 6.909e-05 -6.438 1
8212 NEGR1 1_46 0.5012 45.67 6.810e-05 -8.928 1
3049 PRRC2C 1_84 0.7402 28.99 6.384e-05 -5.173 1
7314 CASP7 10_71 0.8414 24.92 6.240e-05 4.584 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
7063 MST1R 3_35 0.000e+00 34978.09 0.000e+00 -12.602 2
7058 RNF123 3_35 0.000e+00 23171.66 0.000e+00 -10.957 1
5866 TAOK2 16_24 2.301e-02 95.97 6.571e-06 10.738 1
11609 RP11-1348G14.4 16_23 1.472e-01 90.57 3.967e-05 10.603 1
9939 SULT1A1 16_23 9.114e-02 89.15 2.417e-05 10.415 1
10040 SULT1A2 16_23 9.114e-02 89.15 2.417e-05 -10.415 1
7566 ZNF668 16_24 1.131e-01 77.72 2.615e-05 10.000 1
7567 ZNF646 16_24 1.131e-01 77.72 2.615e-05 -10.000 1
5192 SAE1 19_33 4.601e-03 101.25 1.386e-06 9.849 1
7061 CAMKV 3_35 0.000e+00 53039.36 0.000e+00 9.848 1
8211 C1QTNF4 11_29 3.023e-02 96.82 8.707e-06 9.834 2
439 PRSS8 16_24 1.755e-02 72.64 3.792e-06 -9.765 1
7337 RAPSN 11_29 1.110e-02 87.11 2.877e-06 9.614 1
10701 LAT 16_23 1.333e-01 85.22 3.380e-05 -9.553 1
2358 MTCH2 11_29 9.832e-03 84.51 2.472e-06 -9.514 1
11572 CTC-467M3.3 5_52 1.492e-10 355.30 1.577e-13 9.482 1
8212 NEGR1 1_46 5.012e-01 45.67 6.810e-05 -8.928 1
7336 PSMC3 11_29 1.089e-02 74.52 2.413e-06 -8.866 1
1699 MAPK3 16_24 1.422e-02 68.99 2.918e-06 8.826 1
12567 RCC1L 7_48 1.287e-01 83.58 3.199e-05 -8.667 1Comparing z scores and PIPs
[1] 0.02129 genename region_tag susie_pip mu2 PVE z num_eqtl
7063 MST1R 3_35 0.000e+00 34978.09 0.000e+00 -12.602 2
7058 RNF123 3_35 0.000e+00 23171.66 0.000e+00 -10.957 1
5866 TAOK2 16_24 2.301e-02 95.97 6.571e-06 10.738 1
11609 RP11-1348G14.4 16_23 1.472e-01 90.57 3.967e-05 10.603 1
9939 SULT1A1 16_23 9.114e-02 89.15 2.417e-05 10.415 1
10040 SULT1A2 16_23 9.114e-02 89.15 2.417e-05 -10.415 1
7566 ZNF668 16_24 1.131e-01 77.72 2.615e-05 10.000 1
7567 ZNF646 16_24 1.131e-01 77.72 2.615e-05 -10.000 1
5192 SAE1 19_33 4.601e-03 101.25 1.386e-06 9.849 1
7061 CAMKV 3_35 0.000e+00 53039.36 0.000e+00 9.848 1
8211 C1QTNF4 11_29 3.023e-02 96.82 8.707e-06 9.834 2
439 PRSS8 16_24 1.755e-02 72.64 3.792e-06 -9.765 1
7337 RAPSN 11_29 1.110e-02 87.11 2.877e-06 9.614 1
10701 LAT 16_23 1.333e-01 85.22 3.380e-05 -9.553 1
2358 MTCH2 11_29 9.832e-03 84.51 2.472e-06 -9.514 1
11572 CTC-467M3.3 5_52 1.492e-10 355.30 1.577e-13 9.482 1
8212 NEGR1 1_46 5.012e-01 45.67 6.810e-05 -8.928 1
7336 PSMC3 11_29 1.089e-02 74.52 2.413e-06 -8.866 1
1699 MAPK3 16_24 1.422e-02 68.99 2.918e-06 8.826 1
12567 RCC1L 7_48 1.287e-01 83.58 3.199e-05 -8.667 1GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 38Uploading 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"
Term Overlap
1 vascular associated smooth muscle cell development (GO:0097084) 2/7
2 vascular associated smooth muscle cell differentiation (GO:0035886) 2/8
3 aorta morphogenesis (GO:0035909) 2/17
4 muscle tissue morphogenesis (GO:0060415) 2/17
Adjusted.P.value Genes
1 0.01559 EFEMP2;HEY2
2 0.01559 EFEMP2;HEY2
3 0.03744 EFEMP2;HEY2
4 0.03744 EFEMP2;HEY2
[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
43 Acute Myeloid Leukemia, M1 0.008685
160 Acute Myeloid Leukemia (AML-M2) 0.008685
37 Leukemia, Myelocytic, Acute 0.020284
75 Sulfite oxidase deficiency 0.029827
148 DEAFNESS, AUTOSOMAL RECESSIVE 6 0.029827
165 RETINITIS PIGMENTOSA 42 0.029827
167 Sulfocysteinuria 0.029827
171 CUTIS LAXA, AUTOSOMAL RECESSIVE, TYPE IB 0.029827
176 NEMALINE MYOPATHY 8 0.029827
180 MEGALENCEPHALY-POLYMICROGYRIA-POLYDACTYLY-HYDROCEPHALUS SYNDROME 3 0.029827
Ratio BgRatio
43 4/19 125/9703
160 4/19 125/9703
37 4/19 173/9703
75 1/19 1/9703
148 1/19 1/9703
165 1/19 1/9703
167 1/19 1/9703
171 1/19 1/9703
176 1/19 1/9703
180 1/19 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: 1 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] 22#significance threshold for TWAS
print(sig_thresh)[1] 4.571#number of ctwas genes
length(ctwas_genes)[1] 9#number of TWAS genes
length(twas_genes)[1] 219#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
7067 PPM1M 3_36 1.0000 203.85 6.065e-04 4.323 2
10858 SLC12A8 3_77 0.8108 22.51 5.431e-05 -4.338 1
2362 B3GAT1 11_84 0.9273 25.66 7.080e-05 -4.502 2#sensitivity / recall
print(sensitivity) ctwas TWAS
0.00000 0.09756 #specificity
print(specificity) ctwas TWAS
0.9991 0.9791 #precision / PPV
print(precision) ctwas TWAS
0.00000 0.01826 
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