Autism - Brain Amygdala
sheng Qian
2021-2-6
Last updated: 2022-02-27
Checks: 6 1
Knit directory: cTWAS_analysis/
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|---|---|---|---|---|
| Rmd | 3dd5b4c | sq-96 | 2022-02-27 | update |
Weight QC
#number of imputed weights
nrow(qclist_all)[1] 10292#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1005 740 588 392 518 542 481 374 407 397 632 597 222 335 340 460
17 18 19 20 21 22
598 163 797 309 120 275 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 8408#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.8169Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0072071 0.0002653 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
14.431 8.448 #report sample size
print(sample_size)[1] 82315#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 10292 7573890#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.0130 0.2062 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.04663 1.55649Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
10169 ZNF823 19_10 0.9717 30.47 0.0003597 5.455 1
11179 HIST1H2BN 6_21 0.8621 100.93 0.0010571 10.773 1
11222 AC012074.2 2_15 0.8529 23.33 0.0002418 4.648 2
2362 B3GAT1 11_84 0.7950 23.70 0.0002289 -4.459 2
242 VSIG2 11_77 0.7863 26.81 0.0002560 -3.818 1
5997 TMEM56 1_58 0.7396 21.89 0.0001967 -3.918 1
3251 CYSTM1 5_83 0.7299 23.06 0.0002045 -4.025 1
10971 LINC00390 13_17 0.6968 22.04 0.0001866 -4.220 1
5798 ARFGAP2 11_29 0.6909 25.35 0.0002128 4.740 1
5920 PLBD2 12_68 0.6614 22.50 0.0001808 3.986 1
3172 HSDL2 9_57 0.6477 25.20 0.0001983 -4.322 1
10503 ZFP57 6_23 0.6316 73.92 0.0005672 7.267 1
10520 TCTN1 12_67 0.6306 24.15 0.0001850 4.840 1
5344 RIT1 1_76 0.5471 23.38 0.0001554 -3.496 1
377 CTNNA1 5_82 0.5417 25.60 0.0001685 5.064 1
9014 FAM83H 8_94 0.5412 24.57 0.0001616 4.317 2
677 PPP2R5B 11_36 0.5310 25.56 0.0001649 -4.623 1
11888 RP11-220I1.5 9_28 0.5132 23.80 0.0001484 -4.450 1
9316 ACOT1 14_34 0.4857 28.81 0.0001700 3.967 2
2406 MDK 11_28 0.4793 39.90 0.0002323 -6.357 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
8899 HLA-DQB1 6_26 5.651e-14 882.98 6.062e-16 4.2352 1
9980 HLA-DQA1 6_26 7.838e-14 755.62 7.195e-16 4.0876 1
9870 HLA-DRB1 6_26 8.471e-14 504.68 5.194e-16 4.3158 1
10465 MSH5 6_26 8.843e-13 381.27 4.096e-15 8.8122 1
9057 ACBD4 17_27 0.000e+00 196.81 0.000e+00 1.1059 1
10458 SLC44A4 6_26 5.934e-12 170.69 1.230e-14 6.2502 1
9377 FMNL1 17_27 0.000e+00 123.32 0.000e+00 -0.6638 1
11179 HIST1H2BN 6_21 8.621e-01 100.93 1.057e-03 10.7729 1
10706 CLIC1 6_26 2.934e-13 86.02 3.066e-16 0.4634 1
1218 PUS7 7_65 0.000e+00 77.22 0.000e+00 -3.2022 1
10503 ZFP57 6_23 6.316e-01 73.92 5.672e-04 7.2673 1
9573 BTN3A2 6_20 1.147e-02 68.67 9.569e-06 9.0494 3
2240 GOSR2 17_27 0.000e+00 68.45 0.000e+00 -2.5096 1
4578 NMT1 17_27 0.000e+00 66.09 0.000e+00 2.2782 1
8918 RPRML 17_27 0.000e+00 63.52 0.000e+00 1.5727 1
8984 HIST1H2BC 6_20 1.263e-02 55.02 8.443e-06 -8.0277 1
437 MPHOSPH9 12_75 1.291e-01 48.60 7.624e-05 7.1580 1
4868 PRDM5 4_78 0.000e+00 45.06 0.000e+00 0.3272 2
6704 LEMD2 6_28 1.496e-01 42.65 7.752e-05 4.2792 2
12183 RP11-247A12.7 9_66 3.131e-01 41.87 1.593e-04 4.3022 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
11179 HIST1H2BN 6_21 0.8621 100.93 0.0010571 10.773 1
10503 ZFP57 6_23 0.6316 73.92 0.0005672 7.267 1
10169 ZNF823 19_10 0.9717 30.47 0.0003597 5.455 1
242 VSIG2 11_77 0.7863 26.81 0.0002560 -3.818 1
11222 AC012074.2 2_15 0.8529 23.33 0.0002418 4.648 2
2406 MDK 11_28 0.4793 39.90 0.0002323 -6.357 1
2362 B3GAT1 11_84 0.7950 23.70 0.0002289 -4.459 2
5798 ARFGAP2 11_29 0.6909 25.35 0.0002128 4.740 1
3251 CYSTM1 5_83 0.7299 23.06 0.0002045 -4.025 1
3172 HSDL2 9_57 0.6477 25.20 0.0001983 -4.322 1
5997 TMEM56 1_58 0.7396 21.89 0.0001967 -3.918 1
10971 LINC00390 13_17 0.6968 22.04 0.0001866 -4.220 1
10520 TCTN1 12_67 0.6306 24.15 0.0001850 4.840 1
5920 PLBD2 12_68 0.6614 22.50 0.0001808 3.986 1
413 ARID1B 6_102 0.3600 39.03 0.0001707 3.907 1
9316 ACOT1 14_34 0.4857 28.81 0.0001700 3.967 2
5866 TAOK2 16_24 0.3554 39.04 0.0001685 6.189 1
377 CTNNA1 5_82 0.5417 25.60 0.0001685 5.064 1
677 PPP2R5B 11_36 0.5310 25.56 0.0001649 -4.623 1
9014 FAM83H 8_94 0.5412 24.57 0.0001616 4.317 2Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
11179 HIST1H2BN 6_21 8.621e-01 100.93 1.057e-03 10.773 1
9573 BTN3A2 6_20 1.147e-02 68.67 9.569e-06 9.049 3
10465 MSH5 6_26 8.843e-13 381.27 4.096e-15 8.812 1
8984 HIST1H2BC 6_20 1.263e-02 55.02 8.443e-06 -8.028 1
5778 CNNM2 10_66 1.144e-01 38.59 5.363e-05 -7.691 1
10503 ZFP57 6_23 6.316e-01 73.92 5.672e-04 7.267 1
437 MPHOSPH9 12_75 1.291e-01 48.60 7.624e-05 7.158 1
5903 ABCB9 12_75 4.331e-03 40.53 2.133e-06 6.404 1
2406 MDK 11_28 4.793e-01 39.90 2.323e-04 -6.357 1
11226 ZSCAN31 6_22 9.442e-03 28.74 3.296e-06 -6.270 2
10458 SLC44A4 6_26 5.934e-12 170.69 1.230e-14 6.250 1
9777 DPYD 1_60 6.071e-03 37.20 2.744e-06 -6.222 1
5866 TAOK2 16_24 3.554e-01 39.04 1.685e-04 6.189 1
8969 HARBI1 11_28 1.658e-01 37.21 7.496e-05 6.169 1
10617 DNAJC19 3_111 2.294e-01 39.04 1.088e-04 6.158 1
2590 TRIM38 6_20 9.834e-03 30.60 3.656e-06 5.841 2
10231 ZKSCAN8 6_22 6.487e-03 37.40 2.948e-06 5.829 1
3264 SNX19 11_81 4.623e-02 37.69 2.117e-05 5.761 3
7375 CKB 14_54 9.933e-03 29.53 3.563e-06 -5.704 1
9987 ZSCAN16 6_22 7.220e-03 35.90 3.149e-06 5.677 1Comparing z scores and PIPs
[1] 0.005733 genename region_tag susie_pip mu2 PVE z num_eqtl
11179 HIST1H2BN 6_21 8.621e-01 100.93 1.057e-03 10.773 1
9573 BTN3A2 6_20 1.147e-02 68.67 9.569e-06 9.049 3
10465 MSH5 6_26 8.843e-13 381.27 4.096e-15 8.812 1
8984 HIST1H2BC 6_20 1.263e-02 55.02 8.443e-06 -8.028 1
5778 CNNM2 10_66 1.144e-01 38.59 5.363e-05 -7.691 1
10503 ZFP57 6_23 6.316e-01 73.92 5.672e-04 7.267 1
437 MPHOSPH9 12_75 1.291e-01 48.60 7.624e-05 7.158 1
5903 ABCB9 12_75 4.331e-03 40.53 2.133e-06 6.404 1
2406 MDK 11_28 4.793e-01 39.90 2.323e-04 -6.357 1
11226 ZSCAN31 6_22 9.442e-03 28.74 3.296e-06 -6.270 2
10458 SLC44A4 6_26 5.934e-12 170.69 1.230e-14 6.250 1
9777 DPYD 1_60 6.071e-03 37.20 2.744e-06 -6.222 1
5866 TAOK2 16_24 3.554e-01 39.04 1.685e-04 6.189 1
8969 HARBI1 11_28 1.658e-01 37.21 7.496e-05 6.169 1
10617 DNAJC19 3_111 2.294e-01 39.04 1.088e-04 6.158 1
2590 TRIM38 6_20 9.834e-03 30.60 3.656e-06 5.841 2
10231 ZKSCAN8 6_22 6.487e-03 37.40 2.948e-06 5.829 1
3264 SNX19 11_81 4.623e-02 37.69 2.117e-05 5.761 3
7375 CKB 14_54 9.933e-03 29.53 3.563e-06 -5.704 1
9987 ZSCAN16 6_22 7.220e-03 35.90 3.149e-06 5.677 1GO 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 BgRatio
45 JOUBERT SYNDROME 13 0.02597 1/9 1/9703
50 NOONAN SYNDROME 8 0.02597 1/9 1/9703
36 Macular Dystrophy, Butterfly-Shaped Pigmentary, 2 0.03114 1/9 3/9703
38 Patterned dystrophy of retinal pigment epithelium 0.03114 1/9 3/9703
54 Butterfly-shaped pigmentary macular dystrophy 0.03114 1/9 3/9703
22 Amelogenesis Imperfecta, Type III 0.03459 1/9 4/9703
35 Diabetes Mellitus, Transient Neonatal, 1 0.03704 1/9 5/9703
1 Amelogenesis Imperfecta 0.06312 1/9 12/9703
2 Hereditary Nonpolyposis Colorectal Neoplasms 0.06312 1/9 26/9703
3 Diabetes Mellitus 0.06312 1/9 32/9703WebGestalt 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] 41#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] 4.571#number of ctwas genes
length(ctwas_genes)[1] 3#number of TWAS genes
length(twas_genes)[1] 59#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols][1] genename region_tag susie_pip mu2 PVE z num_eqtl
<0 rows> (or 0-length row.names)#sensitivity / recall
print(sensitivity)ctwas TWAS
0 0 #specificity
print(specificity) ctwas TWAS
0.9997 0.9943 #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