BMI - Brain Substantia nigra
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] 10051#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
966 710 602 390 478 579 472 391 399 396 602 539 215 330 338 461 604 154 780 312
21 22
112 221 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 8301#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.8259Check convergence of parameters
| Version | Author | Date |
|---|---|---|
| e6bc169 | sq-96 | 2022-02-13 |
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0049107 0.0002978 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
24.57 17.49 #report sample size
print(sample_size)[1] 336107#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 10051 7535010#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.003607 0.116746 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.2472 16.0925Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
6994 CCDC127 5_1 1.0000 17290.64 5.144e-02 3.012 1
9381 GSAP 7_49 1.0000 33042.01 9.831e-02 5.260 1
6940 PPM1M 3_36 1.0000 883.39 2.628e-03 5.130 3
3273 FLT3 13_7 0.9340 33.54 9.319e-05 -5.360 1
9286 MAPK11 22_24 0.8447 26.72 6.716e-05 4.904 1
7028 ZNF12 7_9 0.7824 27.51 6.403e-05 5.114 2
8362 PACS1 11_36 0.7676 30.12 6.880e-05 5.121 2
1149 DYNLL1 12_74 0.7403 37.63 8.288e-05 -5.806 1
10169 PRMT6 1_66 0.7190 33.46 7.157e-05 5.528 1
11016 CCDC188 22_4 0.7147 25.33 5.386e-05 4.590 1
3302 ZMIZ2 7_33 0.6997 66.53 1.385e-04 -8.105 1
2386 HPS5 11_13 0.6850 25.31 5.159e-05 -4.584 2
2668 PDCD10 3_103 0.6769 24.04 4.841e-05 -4.059 2
3354 WWP1 8_61 0.6516 1124.54 2.180e-03 5.312 2
1166 KIF16B 20_12 0.5881 25.03 4.379e-05 -4.620 1
1275 CBX5 12_33 0.5878 25.63 4.483e-05 -4.691 1
4143 AGAP3 7_94 0.5479 26.84 4.375e-05 -5.031 2
11192 ATP5J2 7_61 0.5431 53.47 8.640e-05 -7.117 1
12531 RP5-965G21.3 20_19 0.5401 36.60 5.881e-05 -5.901 2
9907 MRPL21 11_38 0.5000 27942.14 4.157e-02 4.379 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
9 SEMA3F 3_35 0.000e+00 73763 0.000e+00 7.681 1
6937 CAMKV 3_35 0.000e+00 53990 0.000e+00 -9.848 1
7091 CCDC171 9_13 0.000e+00 50879 0.000e+00 8.043 2
33 RBM6 3_35 0.000e+00 41693 0.000e+00 12.536 1
6938 MST1R 3_35 0.000e+00 35597 0.000e+00 -12.626 1
9381 GSAP 7_49 1.000e+00 33042 9.831e-02 5.260 1
8677 DHFR2 3_59 0.000e+00 32585 0.000e+00 5.146 1
2783 CHST10 2_58 3.786e-10 31946 3.598e-11 4.807 1
8680 STX19 3_59 0.000e+00 31753 0.000e+00 -5.106 1
10629 SLC35E2 1_1 0.000e+00 30962 0.000e+00 5.161 1
4077 IGHMBP2 11_38 5.000e-01 27942 4.157e-02 -4.379 1
9907 MRPL21 11_38 5.000e-01 27942 4.157e-02 4.379 1
4918 MFAP1 15_16 2.759e-12 24102 1.979e-13 4.303 1
4364 HEY2 6_84 0.000e+00 23771 0.000e+00 3.066 1
6934 RNF123 3_35 0.000e+00 23572 0.000e+00 -10.959 1
11239 NAT6 3_35 0.000e+00 22664 0.000e+00 -7.156 2
4757 TMOD3 15_21 0.000e+00 19109 0.000e+00 5.412 1
2819 PLCL1 2_117 0.000e+00 19108 0.000e+00 -5.642 1
6966 RNF180 5_39 0.000e+00 17815 0.000e+00 -3.717 2
6994 CCDC127 5_1 1.000e+00 17291 5.144e-02 3.012 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
9381 GSAP 7_49 1.0000 33042.01 9.831e-02 5.260 1
6994 CCDC127 5_1 1.0000 17290.64 5.144e-02 3.012 1
9907 MRPL21 11_38 0.5000 27942.14 4.157e-02 4.379 1
4077 IGHMBP2 11_38 0.5000 27942.14 4.157e-02 -4.379 1
6940 PPM1M 3_36 1.0000 883.39 2.628e-03 5.130 3
3354 WWP1 8_61 0.6516 1124.54 2.180e-03 5.312 2
8340 ASPHD1 16_24 0.4769 118.33 1.679e-04 -11.849 1
3302 ZMIZ2 7_33 0.6997 66.53 1.385e-04 -8.105 1
6156 GPR61 1_67 0.4900 80.05 1.167e-04 8.755 1
3273 FLT3 13_7 0.9340 33.54 9.319e-05 -5.360 1
11192 ATP5J2 7_61 0.5431 53.47 8.640e-05 -7.117 1
1149 DYNLL1 12_74 0.7403 37.63 8.288e-05 -5.806 1
10169 PRMT6 1_66 0.7190 33.46 7.157e-05 5.528 1
8362 PACS1 11_36 0.7676 30.12 6.880e-05 5.121 2
10689 VPS52 6_28 0.1824 125.80 6.827e-05 1.654 2
9286 MAPK11 22_24 0.8447 26.72 6.716e-05 4.904 1
8106 EFEMP2 11_36 0.4098 53.04 6.467e-05 -7.485 2
7028 ZNF12 7_9 0.7824 27.51 6.403e-05 5.114 2
12531 RP5-965G21.3 20_19 0.5401 36.60 5.881e-05 -5.901 2
11016 CCDC188 22_4 0.7147 25.33 5.386e-05 4.590 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
6938 MST1R 3_35 0.000e+00 35597.35 0.000e+00 -12.626 1
33 RBM6 3_35 0.000e+00 41693.00 0.000e+00 12.536 1
8340 ASPHD1 16_24 4.769e-01 118.33 1.679e-04 -11.849 1
8341 KCTD13 16_24 5.853e-02 113.47 1.976e-05 -11.491 1
8339 SEZ6L2 16_24 3.177e-02 111.72 1.056e-05 -11.407 1
6934 RNF123 3_35 0.000e+00 23571.65 0.000e+00 -10.959 1
5905 POC5 5_44 1.482e-02 92.05 4.059e-06 -10.428 1
9879 SULT1A2 16_23 6.661e-02 96.31 1.909e-05 -10.415 1
9829 C6orf106 6_28 3.808e-05 122.40 1.387e-08 -10.264 1
7444 ZNF668 16_24 7.817e-02 79.05 1.838e-05 10.000 1
7445 ZNF646 16_24 7.817e-02 79.05 1.838e-05 -10.000 1
1759 KAT8 16_24 1.420e-02 75.60 3.193e-06 -9.874 2
1758 BCKDK 16_24 1.464e-02 75.72 3.299e-06 9.873 1
5100 SAE1 19_33 3.006e-03 100.74 9.010e-07 9.849 1
6937 CAMKV 3_35 0.000e+00 53990.32 0.000e+00 -9.848 1
8065 C1QTNF4 11_29 6.441e-03 88.85 1.703e-06 9.564 1
10961 RP11-196G11.6 16_24 7.677e-03 69.97 1.598e-06 9.354 2
7210 PSMC3 11_29 6.912e-03 77.52 1.594e-06 -8.866 1
7209 SLC39A13 11_29 6.350e-03 76.24 1.440e-06 -8.831 1
8448 NUPR1 16_23 8.723e-03 68.70 1.783e-06 -8.775 1Comparing z scores and PIPs
[1] 0.0201 genename region_tag susie_pip mu2 PVE z num_eqtl
6938 MST1R 3_35 0.000e+00 35597.35 0.000e+00 -12.626 1
33 RBM6 3_35 0.000e+00 41693.00 0.000e+00 12.536 1
8340 ASPHD1 16_24 4.769e-01 118.33 1.679e-04 -11.849 1
8341 KCTD13 16_24 5.853e-02 113.47 1.976e-05 -11.491 1
8339 SEZ6L2 16_24 3.177e-02 111.72 1.056e-05 -11.407 1
6934 RNF123 3_35 0.000e+00 23571.65 0.000e+00 -10.959 1
5905 POC5 5_44 1.482e-02 92.05 4.059e-06 -10.428 1
9879 SULT1A2 16_23 6.661e-02 96.31 1.909e-05 -10.415 1
9829 C6orf106 6_28 3.808e-05 122.40 1.387e-08 -10.264 1
7444 ZNF668 16_24 7.817e-02 79.05 1.838e-05 10.000 1
7445 ZNF646 16_24 7.817e-02 79.05 1.838e-05 -10.000 1
1759 KAT8 16_24 1.420e-02 75.60 3.193e-06 -9.874 2
1758 BCKDK 16_24 1.464e-02 75.72 3.299e-06 9.873 1
5100 SAE1 19_33 3.006e-03 100.74 9.010e-07 9.849 1
6937 CAMKV 3_35 0.000e+00 53990.32 0.000e+00 -9.848 1
8065 C1QTNF4 11_29 6.441e-03 88.85 1.703e-06 9.564 1
10961 RP11-196G11.6 16_24 7.677e-03 69.97 1.598e-06 9.354 2
7210 PSMC3 11_29 6.912e-03 77.52 1.594e-06 -8.866 1
7209 SLC39A13 11_29 6.350e-03 76.24 1.440e-06 -8.831 1
8448 NUPR1 16_23 8.723e-03 68.70 1.783e-06 -8.775 1GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 20Uploading 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
34 Acute myeloid leukemia, minimal differentiation 0.01020 1/9 1/9703
41 Cerebral Cavernous Malformations 3 0.01020 1/9 1/9703
51 Familial cerebral cavernous malformation 0.01020 1/9 1/9703
52 MENTAL RETARDATION, AUTOSOMAL DOMINANT 17 0.01020 1/9 1/9703
54 HERMANSKY-PUDLAK SYNDROME 5 0.01020 1/9 1/9703
45 Mixed phenotype acute leukemia 0.01700 1/9 2/9703
46 Cavernous Hemangioma of Brain 0.02185 1/9 3/9703
15 Acute myelomonocytic leukemia 0.02907 1/9 5/9703
17 Leukocytosis 0.02907 1/9 6/9703
30 Pleocytosis 0.02907 1/9 6/9703WebGestalt 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] 19#significance threshold for TWAS
print(sig_thresh)[1] 4.566#number of ctwas genes
length(ctwas_genes)[1] 5#number of TWAS genes
length(twas_genes)[1] 202#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
6994 CCDC127 5_1 1 17291 0.05144 3.012 1#sensitivity / recall
print(sensitivity) ctwas TWAS
0.00000 0.04878 #specificity
print(specificity) ctwas TWAS
0.9995 0.9801 #precision / PPV
print(precision) ctwas TWAS
0.000000 0.009901 
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