cTWAS_analysis_for_white_blood_counts
Jing Gu
2023-08-10
Last updated: 2023-08-10
Checks: 5 2
Knit directory: m6A_in_disease_genetics/
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Load ctwas results
# top 1 method
res <- impute_expr_z(z_snp, weight = weight, ld_R_dir = ld_R_dir,
method = NULL, outputdir = outputdir, outname = outname.e,
harmonize_z = T, harmonize_wgt = T, scale_by_ld_variance=F,
strand_ambig_action_z = "recover",
recover_strand_ambig_wgt = T
# lasso/elastic-net method
res <- impute_expr_z(z_snp, weight = weight, ld_R_dir = ld_R_dir,
method = NULL, outputdir = outputdir, outname = outname.e,
harmonize_z = F, harmonize_wgt = F, scale_by_ld_variance=T,
strand_ambig_action_z = "recover",
recover_strand_ambig_wgt = TGWAS: UK Biobank GWAS summary statistics - European individuals
Weights: FUSION weights using top1, lasso, or elastic-net models converted into PredictDB format
Imputation: For the lasso and elastic-net models, I made mistakes in not harmonizing QTL SNPs and GWAS/LD SNPs, and in performing scaling for FUSION weights.
Check convergence of parameters
cTWAS analysis on m6A alone
[1] "Check convergence for the top1 model:"
[1] "Table of group size:"
SNP gene
8713250 888
SNP gene
estimated_group_prior 2.481e-04 1.227e-02
estimated_group_prior_var 1.920e+01 2.631e+01
estimated_group_pve 1.184e-01 8.178e-04
attributable_group_pve 9.931e-01 6.858e-03
[1] "Check convergence for the enet model:"
[1] "Table of group size:"
SNP gene
8713250 908
SNP gene
estimated_group_prior 2.375e-04 0.01375
estimated_group_prior_var 1.861e+01 31.17344
estimated_group_pve 1.099e-01 0.00111
attributable_group_pve 9.900e-01 0.01001
[1] "Check convergence for the lasso model:"
[1] "Table of group size:"
SNP gene
8713250 912
SNP gene
estimated_group_prior 0.000241 1.180e-02
estimated_group_prior_var 19.018872 3.113e+01
estimated_group_pve 0.113956 9.563e-04
attributable_group_pve 0.991678 8.322e-03$top1
$enet
$lasso
Joint analysis of expression, splicing and m6A
[1] "Check convergence for the top1 model when jointly analyzing expression, splicing and m6A:"
[1] "Table of group size before/after matching with UKBB SNPs:"
SNP eQTL sQTL m6AQTL
prior_group_size 9.324e+06 2005.0000 2191.000 918.0000
group_size 8.713e+06 1928.0000 2123.000 888.0000
percent_of_overlaps 9.345e-01 0.9616 0.969 0.9673
SNP eQTL sQTL m6AQTL
estimated_group_prior 2.406e-04 8.895e-03 0.012934 1.236e-02
estimated_group_prior_var 1.858e+01 1.683e+01 36.589120 2.554e+01
estimated_group_pve 1.112e-01 8.236e-04 0.002867 7.999e-04
attributable_group_pve 9.612e-01 7.120e-03 0.024783 6.916e-03$top1
Joining with `by = join_by(ensembl_gene_id)`Top expression/splicing/m6A units
For m6A or splicing QTLs, they are assigned to the nearest genes (m6A needs to be confirmed with Kevin).
Top 20 rows of SNPs or genes with PIP > 0.6
$eQTL
hgnc_symbol susie_pip type region_tag
1913 CSNK1G1 0.9587 eQTL 15_29
1916 RAPGEFL1 0.7362 eQTL 17_23
132 NDUFS2 0.6213 eQTL 1_81
$m6AQTL
hgnc_symbol susie_pip type region_tag
4938 SLC9A3R1 0.9539 m6AQTL 17_42
4922 ZKSCAN5 0.7863 m6AQTL 7_61
4067 THEMIS2 0.7816 m6AQTL 1_19
4312 TRAM2 0.7091 m6AQTL 6_39
$sQTL
hgnc_symbol susie_pip type region_tag
4000 RN7SKP83 1.0000 sQTL 2_54
4011 CCM2 0.9932 sQTL 7_33
2385 ADPRH 0.7962 sQTL 3_74
4033 SLIT1 0.7387 sQTL 10_61
3788 ZNF225-AS1 0.6957 sQTL 19_30Top m6A modification pip
hgnc_symbol region_tag susie_pip z
1 SLC9A3R1 17_42 0.9539 -7.630
2 ZKSCAN5 7_61 0.7863 7.158
3 THEMIS2 1_19 0.7816 6.277
4 TRAM2 6_39 0.7091 5.233
5 BANF1 11_36 0.5786 6.174
6 TRIT1 1_25 0.5278 5.298
7 S1PR2 19_9 0.5220 9.939
8 WAC-AS1 10_20 0.4945 11.169
9 SQSTM1 5_108 0.4934 -4.857
10 CD320 19_8 0.3627 -4.062
11 SUPT5H 19_26 0.3551 -5.837
12 HMGN4 6_20 0.2707 4.297
13 MDM2 12_42 0.2706 4.606
14 SMG9 19_30 0.2544 3.901
15 CREB3L2 7_84 0.2449 -3.563
16 CNDP2 18_44 0.2407 3.360
17 CD79B 17_37 0.2406 5.091
18 CAND1 12_41 0.2377 -3.502
19 CHURC1 14_30 0.2315 -3.465
20 ADCY7 16_27 0.2181 3.717Summing up PIPs for m6A peaks located in the same gene
Top 10 m6A PIPs by genes
# A tibble: 800 × 2
hgnc_symbol total_susie_pip
<chr> <dbl>
1 SLC9A3R1 0.954
2 ZKSCAN5 0.786
3 THEMIS2 0.782
4 TRAM2 0.709
5 BANF1 0.579
6 TRIT1 0.528
7 S1PR2 0.522
8 WAC-AS1 0.513
9 SQSTM1 0.493
10 CD320 0.379
# ℹ 790 more rowsTop splicing PIPs
peak_id hgnc_symbol region_tag susie_pip z
1 chr2:85823772-85824227 RN7SKP83 2_54 1.0000 5.009
2 chr7:45009474-45009639 CCM2 7_33 0.9932 -11.719
3 chr3:119582452-119624602 ADPRH 3_74 0.7962 5.622
4 chr10:97007123-97023621 SLIT1 10_61 0.7387 -7.331
5 chr19:44112259-44118381 ZNF225-AS1 19_30 0.6957 -4.929
6 chr5:122111457-122130961 ZNF474 5_74 0.5628 -6.687
7 chr11:67120548-67124214 KDM2A 11_37 0.5416 -4.432
8 chr6:29693820-29694660 ZFP57 6_23 0.5000 -16.046
9 chr6:29694781-29695734 ZDHHC20P1 6_23 0.5000 -16.046
10 chr11:47761655-47765505 FNBP4 11_29 0.4878 10.101
11 chr19:13885521-13886291 BRME1 19_11 0.4797 6.500
12 chr19:13886427-13888866 BRME1 19_11 0.4797 6.500
13 chr7:72986365-72987174 STAG3L3 7_47 0.4741 6.870
14 chr1:224544695-224548197 CNIH3 1_116 0.4698 8.830
15 chr19:1036561-1037624 CNN2 19_2 0.4643 6.170
16 chr19:1036999-1037624 CNN2 19_2 0.4643 -6.170
17 chr17:47288203-47295101 ITGB3 17_28 0.4626 -4.041
18 chr19:49458856-49459455 ALDH16A1 19_34 0.4605 -4.118
19 chr7:5569315-5570155 ACTB 7_7 0.4288 -4.696
20 chr16:67690548-67690704 GFOD2 16_36 0.3912 -3.955Summing up PIPs for spliced introns located in the same gene
Top 10 splicing PIPs by genes
# A tibble: 10 × 2
hgnc_symbol total_susie_pip
<chr> <dbl>
1 RN7SKP83 1.00
2 CCM2 0.993
3 BRME1 0.959
4 CNN2 0.929
5 LINC02767 0.904
6 LINC02834 0.811
7 ADPRH 0.796
8 ZNF225-AS1 0.748
9 SLIT1 0.739
10 CNIH3 0.690Top genes by combined PIP
hgnc_symbol combined_pip expression_pip splicing_pip m6A_pip region_tag
2301 RN7SKP83 1.000 0.00000 1.0000000 0.000000 2_54
413 CCM2 0.993 0.00000 0.9932084 0.000000 7_33
610 CSNK1G1 0.969 0.95872 0.0000000 0.010146 15_29
285 BRME1 0.959 0.00000 0.4797074 0.000000 19_11
2601 SLC9A3R1 0.954 0.00000 0.0000000 0.953899 17_42
548 CNN2 0.929 0.00000 0.0001447 0.000000 19_2
1445 LINC02767 0.904 0.00000 0.2984697 0.000000 1_107
1448 LINC02834 0.811 0.00000 0.0247987 0.000000 9_41
65 ADPRH 0.796 0.00000 0.7961515 0.000000 3_74
3195 ZKSCAN5 0.786 0.00000 0.0000000 0.786335 7_61
2832 THEMIS2 0.782 0.00000 0.0000000 0.781582 1_19
2939 TRAM2 0.755 0.04565 0.0000000 0.709110 6_39
3214 ZNF225-AS1 0.748 0.00000 0.6956932 0.000000 19_30
2608 SLIT1 0.739 0.00000 0.7386585 0.000000 10_61
2226 RAPGEFL1 0.736 0.73623 0.0000000 0.000000 17_23
93 ALDH16A1 0.692 0.03207 0.1927717 0.006591 19_34
547 CNIH3 0.690 0.00000 0.4697789 0.000000 1_116
1772 NDUFS2 0.621 0.62125 0.0000000 0.000000 1_81
2869 TMEM156 0.606 0.17597 0.2148226 0.000000 4_32
411 CCDC9 0.597 0.59710 0.0000000 0.000000 19_33
R version 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.3.13-el7-x86_64/lib/libopenblas_haswellp-r0.3.13.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=C
[4] LC_COLLATE=C LC_MONETARY=C LC_MESSAGES=C
[7] LC_PAPER=C LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=C LC_IDENTIFICATION=C
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] cowplot_1.1.1 ggplot2_3.4.2 GenomicRanges_1.48.0
[4] GenomeInfoDb_1.32.2 IRanges_2.30.1 S4Vectors_0.34.0
[7] BiocGenerics_0.42.0 ctwas_0.1.38 dplyr_1.1.2
loaded via a namespace (and not attached):
[1] tidyselect_1.2.0 xfun_0.30 bslib_0.3.1
[4] lattice_0.20-45 pgenlibr_0.3.6 colorspace_2.1-0
[7] vctrs_0.6.3 generics_0.1.3 htmltools_0.5.2
[10] yaml_2.3.5 utf8_1.2.3 rlang_1.1.1
[13] jquerylib_0.1.4 later_1.3.0 pillar_1.9.0
[16] withr_2.5.0 glue_1.6.2 GenomeInfoDbData_1.2.8
[19] foreach_1.5.2 lifecycle_1.0.3 zlibbioc_1.42.0
[22] stringr_1.5.0 munsell_0.5.0 gtable_0.3.3
[25] workflowr_1.7.0 codetools_0.2-18 evaluate_0.15
[28] labeling_0.4.2 knitr_1.39 fastmap_1.1.1
[31] httpuv_1.6.5 fansi_1.0.4 highr_0.9
[34] logging_0.10-108 Rcpp_1.0.11 scales_1.2.1
[37] promises_1.2.0.1 jsonlite_1.8.7 XVector_0.36.0
[40] farver_2.1.1 fs_1.6.3 digest_0.6.33
[43] stringi_1.7.12 rprojroot_2.0.3 grid_4.2.0
[46] cli_3.6.1 tools_4.2.0 bitops_1.0-7
[49] magrittr_2.0.3 sass_0.4.1 RCurl_1.98-1.7
[52] tibble_3.2.1 pkgconfig_2.0.3 Matrix_1.6-0
[55] data.table_1.14.8 rmarkdown_2.14 rstudioapi_0.15.0
[58] iterators_1.0.14 R6_2.5.1 git2r_0.30.1
[61] compiler_4.2.0