Autism - Brain Putamen basal ganglia
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] 11274#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1123 795 659 440 534 593 558 410 421 464 669 651 228 384 383 516
17 18 19 20 21 22
678 179 848 340 123 278 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 8904#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.7898Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0123138 0.0002506 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
10.662 8.503 #report sample size
print(sample_size)[1] 82315#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 11274 7573890#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01798 0.19610 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.1204 1.4388Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
3448 CRHR1 17_27 0.9977 3537.44 0.0428738 3.362 1
10867 ZNF823 19_10 0.9813 29.49 0.0003516 5.455 1
4092 FEZF1 7_74 0.9787 28.51 0.0003390 -5.314 1
11990 AC012074.2 2_15 0.9012 21.92 0.0002399 4.623 1
8791 GNG12 1_42 0.8876 22.45 0.0002421 4.526 2
3043 SF3B1 2_117 0.8595 43.81 0.0004574 6.725 1
11945 HIST1H2BN 6_21 0.7887 91.05 0.0008723 10.773 1
6321 PLBD2 12_68 0.7749 20.26 0.0001907 3.986 1
8798 FUT9 6_65 0.7434 29.72 0.0002684 5.427 1
7435 SERPINI1 3_103 0.6967 20.40 0.0001726 -4.038 1
433 ARID1B 6_102 0.6827 22.81 0.0001892 -3.907 1
13621 LINC02033 3_27 0.6750 42.14 0.0003455 -6.688 1
11497 AS3MT 10_66 0.6629 47.24 0.0003805 8.510 2
4444 REEP2 5_82 0.6544 27.96 0.0002223 5.204 1
10737 PCBP2 12_33 0.6333 22.13 0.0001703 4.202 1
11110 LIN28B-AS1 6_70 0.6276 23.11 0.0001762 -4.630 2
3935 KLC1 14_54 0.6130 41.31 0.0003076 7.069 1
11329 ITSN1 21_14 0.6071 24.37 0.0001798 3.885 1
5721 CEP170 1_128 0.5858 24.35 0.0001733 -4.678 1
905 NT5C2 10_66 0.5494 40.33 0.0002691 -8.066 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
3448 CRHR1 17_27 9.977e-01 3537.44 4.287e-02 3.36232 1
10167 ARL17A 17_27 0.000e+00 3039.45 0.000e+00 -2.78513 2
6964 ARHGAP27 17_27 0.000e+00 338.09 0.000e+00 0.34014 1
12064 HLA-DQA2 6_26 1.865e-14 251.01 5.688e-17 0.21639 1
10691 HLA-DQA1 6_26 2.209e-14 201.77 5.415e-17 3.44601 1
12119 LY6G5B 6_26 7.245e-09 185.79 1.635e-11 -7.00014 1
10035 FMNL1 17_27 0.000e+00 140.29 0.000e+00 -0.66376 1
11190 MSH5 6_26 1.045e-04 127.66 1.621e-07 7.59028 2
4897 NMT1 17_27 0.000e+00 119.81 0.000e+00 2.85333 1
8857 DCAKD 17_27 0.000e+00 115.79 0.000e+00 -2.99967 1
9689 ACBD4 17_27 0.000e+00 105.48 0.000e+00 0.12846 2
11639 C4B 6_26 5.842e-13 102.32 7.262e-16 -4.92818 1
11945 HIST1H2BN 6_21 7.887e-01 91.05 8.723e-04 10.77288 1
9507 HLA-DQB1 6_26 6.661e-15 85.64 6.930e-18 4.33946 1
10958 HLA-DRB5 6_26 4.996e-15 75.21 4.565e-18 2.07566 1
10276 HEXIM1 17_27 0.000e+00 73.25 0.000e+00 -2.84515 1
2412 GOSR2 17_27 0.000e+00 72.14 0.000e+00 -2.50963 1
13230 RP1-86C11.7 6_21 1.175e-01 71.63 1.022e-04 9.03322 1
11877 CYP21A2 6_26 1.862e-13 63.59 1.438e-16 -0.01504 1
10244 BTN3A2 6_20 1.530e-02 56.53 1.051e-05 8.19734 3Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
3448 CRHR1 17_27 0.9977 3537.44 0.0428738 3.362 1
11945 HIST1H2BN 6_21 0.7887 91.05 0.0008723 10.773 1
3043 SF3B1 2_117 0.8595 43.81 0.0004574 6.725 1
11497 AS3MT 10_66 0.6629 47.24 0.0003805 8.510 2
10867 ZNF823 19_10 0.9813 29.49 0.0003516 5.455 1
13621 LINC02033 3_27 0.6750 42.14 0.0003455 -6.688 1
4092 FEZF1 7_74 0.9787 28.51 0.0003390 -5.314 1
3935 KLC1 14_54 0.6130 41.31 0.0003076 7.069 1
905 NT5C2 10_66 0.5494 40.33 0.0002691 -8.066 1
8798 FUT9 6_65 0.7434 29.72 0.0002684 5.427 1
2590 MDK 11_28 0.5312 38.43 0.0002480 -6.357 1
8791 GNG12 1_42 0.8876 22.45 0.0002421 4.526 2
11990 AC012074.2 2_15 0.9012 21.92 0.0002399 4.623 1
4444 REEP2 5_82 0.6544 27.96 0.0002223 5.204 1
6321 PLBD2 12_68 0.7749 20.26 0.0001907 3.986 1
433 ARID1B 6_102 0.6827 22.81 0.0001892 -3.907 1
11329 ITSN1 21_14 0.6071 24.37 0.0001798 3.885 1
11110 LIN28B-AS1 6_70 0.6276 23.11 0.0001762 -4.630 2
5721 CEP170 1_128 0.5858 24.35 0.0001733 -4.678 1
7435 SERPINI1 3_103 0.6967 20.40 0.0001726 -4.038 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
11945 HIST1H2BN 6_21 7.887e-01 91.05 8.723e-04 10.773 1
13230 RP1-86C11.7 6_21 1.175e-01 71.63 1.022e-04 9.033 1
11497 AS3MT 10_66 6.629e-01 47.24 3.805e-04 8.510 2
10244 BTN3A2 6_20 1.530e-02 56.53 1.051e-05 8.197 3
905 NT5C2 10_66 5.494e-01 40.33 2.691e-04 -8.066 1
6164 CNNM2 10_66 7.538e-02 34.34 3.145e-05 -7.691 1
11190 MSH5 6_26 1.045e-04 127.66 1.621e-07 7.590 2
3935 KLC1 14_54 6.130e-01 41.31 3.076e-04 7.069 1
12119 LY6G5B 6_26 7.245e-09 185.79 1.635e-11 -7.000 1
10392 ZSCAN23 6_22 1.162e-01 47.98 6.775e-05 -6.789 2
3043 SF3B1 2_117 8.595e-01 43.81 4.574e-04 6.725 1
10545 ZKSCAN3 6_22 2.269e-02 33.66 9.278e-06 6.709 1
13621 LINC02033 3_27 6.750e-01 42.14 3.455e-04 -6.688 1
10732 ZSCAN26 6_22 1.608e-02 37.56 7.336e-06 6.658 3
6302 ABCB9 12_75 7.651e-03 38.72 3.599e-06 6.404 1
2590 MDK 11_28 5.312e-01 38.43 2.480e-04 -6.357 1
5872 CCDC39 3_111 2.934e-01 38.48 1.372e-04 -6.338 1
2929 FXR1 3_111 1.977e-01 37.68 9.050e-05 6.308 1
1212 PPP1R13B 14_54 9.198e-02 42.90 4.793e-05 6.297 1
13228 U91328.19 6_20 5.982e-02 41.81 3.038e-05 -6.254 1Comparing z scores and PIPs
[1] 0.006741 genename region_tag susie_pip mu2 PVE z num_eqtl
11945 HIST1H2BN 6_21 7.887e-01 91.05 8.723e-04 10.773 1
13230 RP1-86C11.7 6_21 1.175e-01 71.63 1.022e-04 9.033 1
11497 AS3MT 10_66 6.629e-01 47.24 3.805e-04 8.510 2
10244 BTN3A2 6_20 1.530e-02 56.53 1.051e-05 8.197 3
905 NT5C2 10_66 5.494e-01 40.33 2.691e-04 -8.066 1
6164 CNNM2 10_66 7.538e-02 34.34 3.145e-05 -7.691 1
11190 MSH5 6_26 1.045e-04 127.66 1.621e-07 7.590 2
3935 KLC1 14_54 6.130e-01 41.31 3.076e-04 7.069 1
12119 LY6G5B 6_26 7.245e-09 185.79 1.635e-11 -7.000 1
10392 ZSCAN23 6_22 1.162e-01 47.98 6.775e-05 -6.789 2
3043 SF3B1 2_117 8.595e-01 43.81 4.574e-04 6.725 1
10545 ZKSCAN3 6_22 2.269e-02 33.66 9.278e-06 6.709 1
13621 LINC02033 3_27 6.750e-01 42.14 3.455e-04 -6.688 1
10732 ZSCAN26 6_22 1.608e-02 37.56 7.336e-06 6.658 3
6302 ABCB9 12_75 7.651e-03 38.72 3.599e-06 6.404 1
2590 MDK 11_28 5.312e-01 38.43 2.480e-04 -6.357 1
5872 CCDC39 3_111 2.934e-01 38.48 1.372e-04 -6.338 1
2929 FXR1 3_111 1.977e-01 37.68 9.050e-05 6.308 1
1212 PPP1R13B 14_54 9.198e-02 42.90 4.793e-05 6.297 1
13228 U91328.19 6_20 5.982e-02 41.81 3.038e-05 -6.254 1GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 27Uploading 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 leukocyte cell-cell adhesion (GO:1903037)
2 regulation of leukocyte adhesion to vascular endothelial cell (GO:1904994)
3 positive regulation of neuron projection development (GO:0010976)
4 positive regulation of cell projection organization (GO:0031346)
Overlap Adjusted.P.value Genes
1 2/12 0.01811 FUT9;MDK
2 2/13 0.01811 FUT9;MDK
3 3/88 0.01985 FUT9;MDK;SERPINI1
4 3/117 0.03438 FUT9;MDK;SERPINI1
[1] "GO_Cellular_Component_2021"
Term Overlap Adjusted.P.value
1 protein phosphatase type 2A complex (GO:0000159) 2/17 0.008722
Genes
1 PTPA;PPP2R5B
[1] "GO_Molecular_Function_2021"
Term Overlap Adjusted.P.value
1 protein phosphatase activator activity (GO:0072542) 2/13 0.007189
Genes
1 PTPA;PPP2R5BDisGeNET enrichment analysis for genes with PIP>0.5
Description FDR Ratio
3 Anxiety Disorders 0.02009 2/13
60 Anxiety States, Neurotic 0.02009 2/13
94 Anxiety neurosis (finding) 0.02009 2/13
103 Familial encephalopathy with neuroserpin inclusion bodies 0.02009 1/13
113 SPASTIC PARAPLEGIA 72, AUTOSOMAL RECESSIVE 0.02009 1/13
114 SPASTIC PARAPLEGIA 72, AUTOSOMAL DOMINANT 0.02009 1/13
116 SPASTIC PARAPLEGIA 45, AUTOSOMAL RECESSIVE 0.02009 1/13
117 CONE-ROD DYSTROPHY 20 0.02009 1/13
118 HYPOGONADOTROPIC HYPOGONADISM 22 WITH OR WITHOUT ANOSMIA 0.02009 1/13
28 Neoplasms, Glandular and Epithelial 0.02125 1/13
BgRatio
3 44/9703
60 44/9703
94 44/9703
103 1/9703
113 1/9703
114 1/9703
116 1/9703
117 1/9703
118 1/9703
28 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: 6 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] 27#significance threshold for TWAS
print(sig_thresh)[1] 4.59#number of ctwas genes
length(ctwas_genes)[1] 6#number of TWAS genes
length(twas_genes)[1] 76#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
8791 GNG12 1_42 0.8876 22.45 0.0002421 4.526 2
3448 CRHR1 17_27 0.9977 3537.44 0.0428738 3.362 1#sensitivity / recall
print(sensitivity)ctwas TWAS
0 0 #specificity
print(specificity) ctwas TWAS
0.9995 0.9932 #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