BMI - Brain Frontal Cortex BA9
sheng Qian
2021-2-6
Last updated: 2022-02-20
Checks: 6 1
Knit directory: cTWAS_analysis/
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Weight QC
#number of imputed weights
nrow(qclist_all)[1] 11472#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1144 832 640 455 556 662 528 439 419 459 689 634 234 371 365 512
17 18 19 20 21 22
717 176 886 353 119 282 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 8962#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.7812Check convergence of parameters
| Version | Author | Date |
|---|---|---|
| e6bc169 | sq-96 | 2022-02-13 |
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0067252 0.0002911 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
25.11 17.26 #report sample size
print(sample_size)[1] 336107#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 11472 7535010#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.005764 0.112682 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.3711 15.5333Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
8994 NEGR1 1_46 1.0000 17779.39 5.290e-02 -10.657 2
13051 RP11-490G2.2 1_60 1.0000 33092.91 9.846e-02 5.044 1
10446 GSAP 7_49 1.0000 32937.39 9.800e-02 5.260 1
751 MAPK6 15_21 1.0000 35089.11 1.044e-01 -4.600 1
7741 PPM1M 3_36 1.0000 247.30 7.358e-04 4.675 3
3673 FLT3 13_7 0.9523 33.57 9.512e-05 -5.360 1
979 PIK3C3 18_23 0.9326 52.82 1.466e-04 6.864 2
8027 CASP7 10_71 0.8054 25.18 6.035e-05 4.610 2
9305 ASPHD1 16_24 0.7995 119.96 2.854e-04 -11.938 1
3823 PREX1 20_30 0.7871 34.78 8.145e-05 5.634 1
1486 RASSF7 11_1 0.7687 22.53 5.153e-05 3.477 1
5622 C18orf8 18_12 0.7675 60.95 1.392e-04 7.723 3
5021 DCAF7 17_37 0.7484 28.87 6.429e-05 5.437 1
10039 KCNB2 8_53 0.7452 66.52 1.475e-04 -8.226 1
12703 CTC-467M3.3 5_52 0.7200 90.78 1.945e-04 9.482 1
2989 IFT57 3_67 0.7135 44.79 9.508e-05 -5.822 2
13296 AARSD1 17_25 0.7088 33.54 7.073e-05 5.542 1
4728 YWHAQ 2_6 0.6955 26.36 5.455e-05 4.911 1
6022 ECE2 3_113 0.6950 29.99 6.201e-05 -5.305 1
10781 SF3B3 16_37 0.6811 35.86 7.266e-05 -6.852 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
9 SEMA3F 3_35 0.000e+00 72070 0.000e+00 7.681 1
10678 SLC38A3 3_35 0.000e+00 67358 0.000e+00 6.726 1
7734 CAMKV 3_35 0.000e+00 52493 0.000e+00 -10.226 2
7917 CCDC171 9_13 0.000e+00 50279 0.000e+00 8.023 2
1462 MAST3 19_14 0.000e+00 42245 0.000e+00 6.953 2
31 RBM5 3_35 0.000e+00 42133 0.000e+00 12.473 1
41 RBM6 3_35 0.000e+00 40749 0.000e+00 12.536 1
751 MAPK6 15_21 1.000e+00 35089 1.044e-01 -4.600 1
8150 LEO1 15_21 2.603e-03 34888 2.702e-04 4.603 1
13051 RP11-490G2.2 1_60 1.000e+00 33093 9.846e-02 5.044 1
10446 GSAP 7_49 1.000e+00 32937 9.800e-02 5.260 1
6317 CNNM2 10_66 0.000e+00 31589 0.000e+00 -5.981 2
5488 MFAP1 15_16 7.668e-04 23547 5.372e-05 4.303 1
12490 HYPK 15_16 6.638e-06 23445 4.631e-07 4.322 1
7732 RNF123 3_35 0.000e+00 23053 0.000e+00 -10.957 1
11029 MRPL21 11_38 0.000e+00 22517 0.000e+00 4.215 2
1379 WDR76 15_16 0.000e+00 21367 0.000e+00 4.687 2
11910 CKMT1A 15_16 0.000e+00 21087 0.000e+00 4.130 1
9680 DHFR2 3_59 0.000e+00 17796 0.000e+00 3.266 3
8994 NEGR1 1_46 1.000e+00 17779 5.290e-02 -10.657 2Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
751 MAPK6 15_21 1.000000 35089.11 1.044e-01 -4.600 1
13051 RP11-490G2.2 1_60 1.000000 33092.91 9.846e-02 5.044 1
10446 GSAP 7_49 1.000000 32937.39 9.800e-02 5.260 1
8994 NEGR1 1_46 1.000000 17779.39 5.290e-02 -10.657 2
10925 TTC30B 2_107 0.349013 762.39 7.917e-04 -3.137 1
7741 PPM1M 3_36 1.000000 247.30 7.358e-04 4.675 3
3029 SPCS1 3_36 0.516681 361.49 5.557e-04 -5.067 1
9305 ASPHD1 16_24 0.799534 119.96 2.854e-04 -11.938 1
8150 LEO1 15_21 0.002603 34887.56 2.702e-04 4.603 1
12703 CTC-467M3.3 5_52 0.720017 90.78 1.945e-04 9.482 1
10039 KCNB2 8_53 0.745172 66.52 1.475e-04 -8.226 1
979 PIK3C3 18_23 0.932638 52.82 1.466e-04 6.864 2
5622 C18orf8 18_12 0.767469 60.95 1.392e-04 7.723 3
6868 GPR61 1_67 0.582832 80.07 1.389e-04 8.755 1
8172 MC4R 18_34 0.344096 130.50 1.336e-04 13.312 1
5343 G3BP2 4_51 0.360464 121.38 1.302e-04 -2.134 1
7387 TAL1 1_29 0.666872 48.92 9.705e-05 -6.745 2
3673 FLT3 13_7 0.952277 33.57 9.512e-05 -5.360 1
2989 IFT57 3_67 0.713549 44.79 9.508e-05 -5.822 2
5424 SUOX 12_35 0.519005 58.30 9.003e-05 -5.807 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
8172 MC4R 18_34 0.344096 130.50 1.336e-04 13.312 1
41 RBM6 3_35 0.000000 40748.94 0.000e+00 12.536 1
31 RBM5 3_35 0.000000 42132.93 0.000e+00 12.473 1
9305 ASPHD1 16_24 0.799534 119.96 2.854e-04 -11.938 1
6407 TAOK2 16_24 0.051558 125.44 1.924e-05 11.849 1
7736 MST1R 3_35 0.000000 6793.57 0.000e+00 -11.803 2
9306 KCTD13 16_24 0.033411 112.45 1.118e-05 11.491 1
9304 SEZ6L2 16_24 0.020287 110.70 6.682e-06 -11.407 1
7732 RNF123 3_35 0.000000 23052.76 0.000e+00 -10.957 1
8634 INO80E 16_24 0.023304 98.36 6.820e-06 10.794 2
8994 NEGR1 1_46 1.000000 17779.39 5.290e-02 -10.657 2
10707 CLN3 16_23 0.089642 68.19 1.819e-05 10.453 1
7734 CAMKV 3_35 0.000000 52493.18 0.000e+00 -10.226 2
12221 RP11-196G11.6 16_24 0.336710 81.09 8.123e-05 10.128 1
11002 SULT1A2 16_23 0.040750 64.26 7.791e-06 -10.075 2
12241 NPIPB7 16_23 0.029322 65.41 5.706e-06 10.038 1
8290 ZNF646 16_24 0.081105 77.81 1.878e-05 -10.000 1
2891 COL4A3BP 5_44 0.022154 72.01 4.747e-06 -9.828 1
10737 SKOR1 15_31 0.542610 52.50 8.476e-05 -9.635 1
8993 C1QTNF4 11_29 0.008762 87.67 2.286e-06 9.564 1Comparing z scores and PIPs
[1] 0.02223 genename region_tag susie_pip mu2 PVE z num_eqtl
8172 MC4R 18_34 0.344096 130.50 1.336e-04 13.312 1
41 RBM6 3_35 0.000000 40748.94 0.000e+00 12.536 1
31 RBM5 3_35 0.000000 42132.93 0.000e+00 12.473 1
9305 ASPHD1 16_24 0.799534 119.96 2.854e-04 -11.938 1
6407 TAOK2 16_24 0.051558 125.44 1.924e-05 11.849 1
7736 MST1R 3_35 0.000000 6793.57 0.000e+00 -11.803 2
9306 KCTD13 16_24 0.033411 112.45 1.118e-05 11.491 1
9304 SEZ6L2 16_24 0.020287 110.70 6.682e-06 -11.407 1
7732 RNF123 3_35 0.000000 23052.76 0.000e+00 -10.957 1
8634 INO80E 16_24 0.023304 98.36 6.820e-06 10.794 2
8994 NEGR1 1_46 1.000000 17779.39 5.290e-02 -10.657 2
10707 CLN3 16_23 0.089642 68.19 1.819e-05 10.453 1
7734 CAMKV 3_35 0.000000 52493.18 0.000e+00 -10.226 2
12221 RP11-196G11.6 16_24 0.336710 81.09 8.123e-05 10.128 1
11002 SULT1A2 16_23 0.040750 64.26 7.791e-06 -10.075 2
12241 NPIPB7 16_23 0.029322 65.41 5.706e-06 10.038 1
8290 ZNF646 16_24 0.081105 77.81 1.878e-05 -10.000 1
2891 COL4A3BP 5_44 0.022154 72.01 4.747e-06 -9.828 1
10737 SKOR1 15_31 0.542610 52.50 8.476e-05 -9.635 1
8993 C1QTNF4 11_29 0.008762 87.67 2.286e-06 9.564 1GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 40Uploading 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
39 Sulfite oxidase deficiency 0.02288 1/18 1/9703
42 Acute myeloid leukemia, minimal differentiation 0.02288 1/18 1/9703
63 Sulfocysteinuria 0.02288 1/18 1/9703
65 FANCONI ANEMIA, COMPLEMENTATION GROUP P 0.02288 1/18 1/9703
72 OROFACIODIGITAL SYNDROME XVIII 0.02288 1/18 1/9703
74 PROTEASOME-ASSOCIATED AUTOINFLAMMATORY SYNDROME 1 0.02288 1/18 1/9703
15 Childhood Acute Lymphoblastic Leukemia 0.02613 2/18 52/9703
16 L2 Acute Lymphoblastic Leukemia 0.02613 2/18 50/9703
18 Leukemia, Myelocytic, Acute 0.02613 3/18 173/9703
50 Nakajo syndrome 0.02613 1/18 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: 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] 26#significance threshold for TWAS
print(sig_thresh)[1] 4.594#number of ctwas genes
length(ctwas_genes)[1] 8#number of TWAS genes
length(twas_genes)[1] 255#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
0.02439 0.12195 #specificity
print(specificity) ctwas TWAS
0.9994 0.9782 #precision / PPV
print(precision) ctwas TWAS
0.12500 0.01961 
Locus Plots - 1_46
Locus Plots - 1_60
Locus Plots - 7_49
Locus Plots - 15_21
Locus Plots - 3_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_1.0.16
[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