Autism - Brain Cortex
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] 11805#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1182 833 689 456 564 597 567 438 449 491 708 673 233 392 390 558
17 18 19 20 21 22
697 184 896 372 130 306 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 9268#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.7851Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0117819 0.0002498 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
10.115 8.745 #report sample size
print(sample_size)[1] 82315#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 11805 7573890#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01709 0.20104 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.07764 1.58587Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
11314 ZNF823 19_10 0.9894 30.41 0.0003655 5.576 2
13679 RP11-230C9.4 6_102 0.9655 24.38 0.0002859 -4.864 2
3165 SF3B1 2_117 0.8406 43.74 0.0004467 6.725 1
3085 SPCS1 3_36 0.8248 35.15 0.0003522 -6.504 1
11176 PCBP2 12_33 0.7873 20.60 0.0001970 4.202 1
5055 RCBTB1 13_21 0.7808 21.04 0.0001996 -4.143 2
421 TRIT1 1_25 0.7795 21.04 0.0001992 -4.073 3
11969 AS3MT 10_66 0.7577 38.26 0.0003522 6.688 3
6435 ARFGAP2 11_29 0.7533 24.88 0.0002277 4.740 1
6035 METTL21A 2_122 0.7032 22.51 0.0001923 -4.406 1
13958 CWC25 17_23 0.6607 22.97 0.0001843 -3.926 2
376 CUL3 2_132 0.6584 29.41 0.0002352 -5.491 1
3183 CNPPD1 2_129 0.6333 23.74 0.0001827 -4.678 2
4002 ARMC7 17_42 0.5918 23.73 0.0001706 4.133 2
9752 ZNF354C 5_108 0.5861 21.75 0.0001549 -3.965 1
12379 LINC01305 2_105 0.5824 22.86 0.0001617 4.523 1
4909 CCDC146 7_49 0.5702 20.79 0.0001441 3.799 3
5958 CEP170 1_128 0.5671 24.30 0.0001674 4.678 1
752 PPP2R5B 11_36 0.5634 24.18 0.0001655 -4.577 1
10297 PCBP3 21_23 0.5553 21.27 0.0001435 4.308 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
7218 ARHGAP27 17_27 0.00000 2129.27 0.000e+00 -1.847 2
3591 CRHR1 17_27 0.00000 2068.72 0.000e+00 -3.270 1
11645 LY6G6C 6_26 0.00000 960.79 0.000e+00 8.872 1
11907 CLIC1 6_26 0.00000 949.93 0.000e+00 9.312 2
10504 SPPL2C 17_27 0.00000 752.27 0.000e+00 -1.978 1
10996 HLA-DRB1 6_26 0.00000 525.39 0.000e+00 4.535 1
11640 HSPA1L 6_26 0.00000 401.95 0.000e+00 -7.126 1
11118 HLA-DQA1 6_26 0.00000 166.39 0.000e+00 1.889 1
4915 SRPK2 7_65 0.00000 128.12 0.000e+00 -1.338 1
10699 HEXIM1 17_27 0.00000 122.54 0.000e+00 -3.372 1
10447 FMNL1 17_27 0.00000 109.85 0.000e+00 1.802 2
12300 SAPCD1 6_26 0.00000 107.97 0.000e+00 -2.781 1
9224 DCAKD 17_27 0.00000 91.74 0.000e+00 -2.216 3
12783 C4A 6_26 0.00000 77.97 0.000e+00 3.137 2
9902 HLA-DQB1 6_26 0.00000 76.18 0.000e+00 1.677 2
10083 ACBD4 17_27 0.00000 68.16 0.000e+00 1.719 2
2927 PRSS16 6_21 0.11206 62.33 8.485e-05 -8.564 2
2503 GOSR2 17_27 0.00000 57.01 0.000e+00 -3.444 2
958 NT5C2 10_66 0.50836 51.68 3.192e-04 -8.066 1
6404 INA 10_66 0.02367 48.25 1.387e-05 -7.264 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
3165 SF3B1 2_117 0.8406 43.74 0.0004467 6.725 1
11314 ZNF823 19_10 0.9894 30.41 0.0003655 5.576 2
3085 SPCS1 3_36 0.8248 35.15 0.0003522 -6.504 1
11969 AS3MT 10_66 0.7577 38.26 0.0003522 6.688 3
958 NT5C2 10_66 0.5084 51.68 0.0003192 -8.066 1
13679 RP11-230C9.4 6_102 0.9655 24.38 0.0002859 -4.864 2
376 CUL3 2_132 0.6584 29.41 0.0002352 -5.491 1
6435 ARFGAP2 11_29 0.7533 24.88 0.0002277 4.740 1
2682 MDK 11_29 0.4345 39.31 0.0002075 -6.357 1
5055 RCBTB1 13_21 0.7808 21.04 0.0001996 -4.143 2
421 TRIT1 1_25 0.7795 21.04 0.0001992 -4.073 3
11176 PCBP2 12_33 0.7873 20.60 0.0001970 4.202 1
6035 METTL21A 2_122 0.7032 22.51 0.0001923 -4.406 1
13958 CWC25 17_23 0.6607 22.97 0.0001843 -3.926 2
13401 CORO7 16_4 0.5211 28.97 0.0001834 -5.016 2
3183 CNPPD1 2_129 0.6333 23.74 0.0001827 -4.678 2
6507 TMEM219 16_24 0.4011 37.11 0.0001808 6.243 1
4002 ARMC7 17_42 0.5918 23.73 0.0001706 4.133 2
5958 CEP170 1_128 0.5671 24.30 0.0001674 4.678 1
752 PPP2R5B 11_36 0.5634 24.18 0.0001655 -4.577 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
11907 CLIC1 6_26 0.000000 949.93 0.000e+00 9.312 2
11645 LY6G6C 6_26 0.000000 960.79 0.000e+00 8.872 1
2927 PRSS16 6_21 0.112056 62.33 8.485e-05 -8.564 2
958 NT5C2 10_66 0.508364 51.68 3.192e-04 -8.066 1
6413 CNNM2 10_66 0.047944 45.59 2.655e-05 -7.691 1
10662 BTN3A2 6_20 0.016326 46.66 9.254e-06 7.313 3
6404 INA 10_66 0.023665 48.25 1.387e-05 -7.264 1
13518 LINC01415 18_30 0.027640 32.82 1.102e-05 -7.188 2
11640 HSPA1L 6_26 0.000000 401.95 0.000e+00 -7.126 1
12511 ZSCAN31 6_22 0.029845 37.53 1.361e-05 -6.820 3
3165 SF3B1 2_117 0.840602 43.74 4.467e-04 6.725 1
11969 AS3MT 10_66 0.757723 38.26 3.522e-04 6.688 3
11171 ZSCAN26 6_22 0.016156 37.52 7.365e-06 6.645 3
2756 OGFOD2 12_75 0.010145 39.52 4.870e-06 6.518 1
3085 SPCS1 3_36 0.824822 35.15 3.522e-04 -6.504 1
3616 SNX19 11_81 0.134870 41.83 6.853e-05 6.459 2
10809 ZSCAN23 6_22 0.050592 38.63 2.374e-05 -6.415 1
6550 ABCB9 12_75 0.007503 37.92 3.457e-06 6.404 1
2682 MDK 11_29 0.434514 39.31 2.075e-04 -6.357 1
3159 KCNJ13 2_137 0.209359 34.40 8.748e-05 6.333 1Comparing z scores and PIPs
[1] 0.006607 genename region_tag susie_pip mu2 PVE z num_eqtl
11907 CLIC1 6_26 0.000000 949.93 0.000e+00 9.312 2
11645 LY6G6C 6_26 0.000000 960.79 0.000e+00 8.872 1
2927 PRSS16 6_21 0.112056 62.33 8.485e-05 -8.564 2
958 NT5C2 10_66 0.508364 51.68 3.192e-04 -8.066 1
6413 CNNM2 10_66 0.047944 45.59 2.655e-05 -7.691 1
10662 BTN3A2 6_20 0.016326 46.66 9.254e-06 7.313 3
6404 INA 10_66 0.023665 48.25 1.387e-05 -7.264 1
13518 LINC01415 18_30 0.027640 32.82 1.102e-05 -7.188 2
11640 HSPA1L 6_26 0.000000 401.95 0.000e+00 -7.126 1
12511 ZSCAN31 6_22 0.029845 37.53 1.361e-05 -6.820 3
3165 SF3B1 2_117 0.840602 43.74 4.467e-04 6.725 1
11969 AS3MT 10_66 0.757723 38.26 3.522e-04 6.688 3
11171 ZSCAN26 6_22 0.016156 37.52 7.365e-06 6.645 3
2756 OGFOD2 12_75 0.010145 39.52 4.870e-06 6.518 1
3085 SPCS1 3_36 0.824822 35.15 3.522e-04 -6.504 1
3616 SNX19 11_81 0.134870 41.83 6.853e-05 6.459 2
10809 ZSCAN23 6_22 0.050592 38.63 2.374e-05 -6.415 1
6550 ABCB9 12_75 0.007503 37.92 3.457e-06 6.404 1
2682 MDK 11_29 0.434514 39.31 2.075e-04 -6.357 1
3159 KCNJ13 2_137 0.209359 34.40 8.748e-05 6.333 1GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 26Uploading 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
1 regulation of nucleobase-containing compound metabolic process (GO:0019219)
Overlap Adjusted.P.value Genes
1 2/12 0.02022 PCBP3;PCBP2
[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
55 Disproportionate tall stature 0.009895 1/9
56 Reticular Dystrophy Of Retinal Pigment Epithelium 0.009895 1/9
60 PSEUDOHYPOALDOSTERONISM, TYPE IIE 0.009895 1/9
62 SPASTIC PARAPLEGIA 45, AUTOSOMAL RECESSIVE 0.009895 1/9
63 RETINAL DYSTROPHY WITH OR WITHOUT EXTRAOCULAR ANOMALIES 0.009895 1/9
64 COMBINED OXIDATIVE PHOSPHORYLATION DEFICIENCY 35 0.009895 1/9
17 Neoplasms, Glandular and Epithelial 0.011869 1/9
29 Glandular Neoplasms 0.011869 1/9
48 Refractory anemia with ringed sideroblasts 0.011869 1/9
51 Epithelioma 0.011869 1/9
BgRatio
55 1/9703
56 1/9703
60 1/9703
62 1/9703
63 1/9703
64 1/9703
17 2/9703
29 2/9703
48 2/9703
51 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
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] 25#significance threshold for TWAS
print(sig_thresh)[1] 4.599#number of ctwas genes
length(ctwas_genes)[1] 4#number of TWAS genes
length(twas_genes)[1] 78#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 0.9934 #precision / PPV
print(precision)ctwas TWAS
0 0 
Locus Plots - 3_36
Locus Plots - 15_21
Locus Plots - 12_4
Locus Plots - 16_24
Locus Plots - 11_36
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