BMI - Brain Hippocampus
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] 10973#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1076 769 643 427 529 614 510 402 415 437 663 585 220 377 370 516
17 18 19 20 21 22
664 166 865 330 117 278 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 8798#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.8018Check convergence of parameters
| Version | Author | Date |
|---|---|---|
| e6bc169 | sq-96 | 2022-02-13 |
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0074990 0.0002925 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
23.53 17.53 #report sample size
print(sample_size)[1] 336107#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 10973 7535010#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.005761 0.114953 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.2223 18.6719Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
717 MAPK6 15_21 1.0000 34111.66 1.015e-01 -4.600 1
10029 GSAP 7_49 1.0000 32344.92 9.623e-02 5.260 1
7429 PPM1M 3_36 1.0000 241.22 7.177e-04 4.468 2
9989 ARL17A 17_27 0.9437 32.41 9.100e-05 5.325 1
1199 DYNLL1 12_74 0.9380 59.08 1.649e-04 -5.806 1
12053 ETV5 3_114 0.9122 96.75 2.626e-04 9.862 1
8671 EFEMP2 11_36 0.7914 56.03 1.319e-04 -8.201 1
3564 ZMIZ2 7_33 0.7783 66.49 1.540e-04 -8.105 1
9621 KCNB2 8_53 0.7522 66.19 1.481e-04 -8.226 1
13243 HIST1H2BE 6_20 0.7441 31.10 6.885e-05 -6.515 1
5796 ECE2 3_113 0.7149 29.87 6.354e-05 -5.305 1
1445 DERL3 22_6 0.6864 22.98 4.694e-05 4.037 1
7736 R3HCC1L 10_62 0.6830 40.52 8.234e-05 7.439 1
13421 PRICKLE4 6_32 0.6622 24.65 4.858e-05 -4.797 1
8923 ASPHD1 16_24 0.6556 578.64 1.129e-03 -11.938 1
11969 ATP5J2 7_61 0.6482 53.45 1.031e-04 -7.117 1
10750 UCKL1 20_38 0.6400 25.31 4.819e-05 3.573 1
151 CSDE1 1_71 0.6372 22.68 4.300e-05 -4.745 1
6243 DPYSL4 10_83 0.6345 43.79 8.266e-05 -6.801 1
9938 GPRIN3 4_60 0.6310 25.08 4.709e-05 -3.769 2Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
10 SEMA3F 3_35 0.000e+00 73861 0.000e+00 7.681 1
10261 SLC38A3 3_35 0.000e+00 69034 0.000e+00 6.726 1
7591 CCDC171 9_13 0.000e+00 44932 0.000e+00 7.405 2
8624 NEGR1 1_46 0.000e+00 43597 0.000e+00 -10.375 2
40 RBM6 3_35 0.000e+00 41746 0.000e+00 12.536 1
6640 ZNF689 16_24 0.000e+00 39994 0.000e+00 -6.014 1
7425 MST1R 3_35 0.000e+00 35624 0.000e+00 -12.635 2
717 MAPK6 15_21 1.000e+00 34112 1.015e-01 -4.600 1
10029 GSAP 7_49 1.000e+00 32345 9.623e-02 5.260 1
9293 STX19 3_59 0.000e+00 31600 0.000e+00 -5.060 1
9289 DHFR2 3_59 0.000e+00 25976 0.000e+00 4.031 2
5274 MFAP1 15_16 1.202e-06 24147 8.636e-08 4.303 1
7420 RNF123 3_35 0.000e+00 23601 0.000e+00 -10.959 1
12024 NAT6 3_35 0.000e+00 23005 0.000e+00 -6.362 1
10512 C6orf106 6_29 0.000e+00 22878 0.000e+00 2.962 1
11433 CKMT1A 15_16 0.000e+00 21625 0.000e+00 4.130 1
1326 WDR76 15_16 0.000e+00 21190 0.000e+00 4.963 2
1785 ZNF629 16_24 0.000e+00 20375 0.000e+00 4.335 1
10290 DPYD 1_60 0.000e+00 19961 0.000e+00 -3.213 1
10101 HYAL3 3_35 0.000e+00 18111 0.000e+00 6.243 2Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
717 MAPK6 15_21 1.00000 34111.66 1.015e-01 -4.600 1
10029 GSAP 7_49 1.00000 32344.92 9.623e-02 5.260 1
821 SDHA 5_1 0.22996 12142.57 8.308e-03 2.907 1
8923 ASPHD1 16_24 0.65557 578.64 1.129e-03 -11.938 1
7429 PPM1M 3_36 1.00000 241.22 7.177e-04 4.468 2
12053 ETV5 3_114 0.91224 96.75 2.626e-04 9.862 1
6834 ADPGK 15_34 0.05907 1201.55 2.112e-04 5.872 3
1199 DYNLL1 12_74 0.93799 59.08 1.649e-04 -5.806 1
3564 ZMIZ2 7_33 0.77828 66.49 1.540e-04 -8.105 1
9621 KCNB2 8_53 0.75221 66.19 1.481e-04 -8.226 1
6587 GPR61 1_67 0.58712 79.87 1.395e-04 8.755 1
5143 USO1 4_51 0.37687 123.87 1.389e-04 -2.134 1
8671 EFEMP2 11_36 0.79142 56.03 1.319e-04 -8.201 1
9035 NUPR1 16_23 0.55509 69.67 1.151e-04 -10.643 2
11969 ATP5J2 7_61 0.64819 53.45 1.031e-04 -7.117 1
10366 SLC35E2B 1_1 0.50528 63.53 9.550e-05 -7.654 1
12120 CDK11B 1_1 0.50528 63.53 9.550e-05 -7.654 1
9989 ARL17A 17_27 0.94369 32.41 9.100e-05 5.325 1
6243 DPYSL4 10_83 0.63454 43.79 8.266e-05 -6.801 1
7736 R3HCC1L 10_62 0.68300 40.52 8.234e-05 7.439 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
7425 MST1R 3_35 0.000e+00 35623.61 0.000e+00 -12.635 2
40 RBM6 3_35 0.000e+00 41746.10 0.000e+00 12.536 1
8923 ASPHD1 16_24 6.556e-01 578.64 1.129e-03 -11.938 1
8924 KCTD13 16_24 4.364e-03 498.27 6.469e-06 11.491 1
8275 INO80E 16_24 1.638e-04 1631.75 7.953e-07 11.077 1
7420 RNF123 3_35 0.000e+00 23601.01 0.000e+00 -10.959 1
6146 TAOK2 16_24 4.676e-06 1891.49 2.632e-08 10.738 1
9035 NUPR1 16_23 5.551e-01 69.67 1.151e-04 -10.643 2
8624 NEGR1 1_46 0.000e+00 43597.35 0.000e+00 -10.375 2
11727 RP11-196G11.6 16_24 4.163e-08 7371.39 9.131e-10 10.011 1
8623 C1QTNF4 11_29 2.297e-02 95.38 6.518e-06 9.951 2
12053 ETV5 3_114 9.122e-01 96.75 2.626e-04 9.862 1
5469 SAE1 19_33 4.553e-03 100.30 1.359e-06 9.849 1
461 PRSS8 16_24 2.775e-09 6922.87 5.715e-11 -9.765 1
7720 RAPSN 11_29 1.189e-02 88.21 3.121e-06 9.729 1
11241 LAT 16_23 2.439e-01 56.17 4.076e-05 -9.553 1
2491 MTCH2 11_29 9.481e-03 83.10 2.344e-06 -9.514 1
10579 IL27 16_23 6.176e-03 50.94 9.360e-07 9.140 1
3527 POLK 5_44 1.242e-02 53.54 1.978e-06 8.884 1
7718 SLC39A13 11_29 9.708e-03 72.48 2.093e-06 -8.831 1Comparing z scores and PIPs
[1] 0.0216 genename region_tag susie_pip mu2 PVE z num_eqtl
7425 MST1R 3_35 0.000e+00 35623.61 0.000e+00 -12.635 2
40 RBM6 3_35 0.000e+00 41746.10 0.000e+00 12.536 1
8923 ASPHD1 16_24 6.556e-01 578.64 1.129e-03 -11.938 1
8924 KCTD13 16_24 4.364e-03 498.27 6.469e-06 11.491 1
8275 INO80E 16_24 1.638e-04 1631.75 7.953e-07 11.077 1
7420 RNF123 3_35 0.000e+00 23601.01 0.000e+00 -10.959 1
6146 TAOK2 16_24 4.676e-06 1891.49 2.632e-08 10.738 1
9035 NUPR1 16_23 5.551e-01 69.67 1.151e-04 -10.643 2
8624 NEGR1 1_46 0.000e+00 43597.35 0.000e+00 -10.375 2
11727 RP11-196G11.6 16_24 4.163e-08 7371.39 9.131e-10 10.011 1
8623 C1QTNF4 11_29 2.297e-02 95.38 6.518e-06 9.951 2
12053 ETV5 3_114 9.122e-01 96.75 2.626e-04 9.862 1
5469 SAE1 19_33 4.553e-03 100.30 1.359e-06 9.849 1
461 PRSS8 16_24 2.775e-09 6922.87 5.715e-11 -9.765 1
7720 RAPSN 11_29 1.189e-02 88.21 3.121e-06 9.729 1
11241 LAT 16_23 2.439e-01 56.17 4.076e-05 -9.553 1
2491 MTCH2 11_29 9.481e-03 83.10 2.344e-06 -9.514 1
10579 IL27 16_23 6.176e-03 50.94 9.360e-07 9.140 1
3527 POLK 5_44 1.242e-02 53.54 1.978e-06 8.884 1
7718 SLC39A13 11_29 9.708e-03 72.48 2.093e-06 -8.831 1GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 36Uploading 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
44 CUTIS LAXA, AUTOSOMAL RECESSIVE, TYPE IB
47 NEMALINE MYOPATHY 8
48 CONE-ROD DYSTROPHY 20
49 PROGRESSIVE EXTERNAL OPHTHALMOPLEGIA WITH MITOCHONDRIAL DNA DELETIONS, AUTOSOMAL RECESSIVE 2
32 Cutis Laxa, Autosomal Recessive, Type I
36 Cutis laxa, recessive, type I
50 Adult-onset chronic progressive external ophthalmoplegia with mitochondrial myopathy
8 Cutis Laxa
2 Aortic Aneurysm
25 Paranoid Schizophrenia
FDR Ratio BgRatio
44 0.01840 1/14 1/9703
47 0.01840 1/14 1/9703
48 0.01840 1/14 1/9703
49 0.01840 1/14 1/9703
32 0.02101 1/14 2/9703
36 0.02101 1/14 2/9703
50 0.02101 1/14 2/9703
8 0.05501 1/14 6/9703
2 0.05701 1/14 7/9703
25 0.08628 1/14 13/9703WebGestalt 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] 4.584#number of ctwas genes
length(ctwas_genes)[1] 6#number of TWAS genes
length(twas_genes)[1] 237#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
7429 PPM1M 3_36 1 241.2 0.0007177 4.468 2#sensitivity / recall
print(sensitivity) ctwas TWAS
0.00000 0.09756 #specificity
print(specificity) ctwas TWAS
0.9995 0.9787 #precision / PPV
print(precision) ctwas TWAS
0.00000 0.01688 
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