Predictdb sQTL from Liver
XSun
2024-09-25
Last updated: 2024-09-26
Checks: 6 1
Knit directory: multigroup_ctwas_analysis/
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Introduction
We run susie_rss from susieR to compare
with the ctwas finemapping function here.
We run with uniform prior.
Results
Run with ctwas processed zscores
Using SNP only
| standard susie (PIP_CS) | ctwas finemapping (PIP_CS) | |||||||
|---|---|---|---|---|---|---|---|---|
| PIP_rs6062496(sQTL) | PIP_rs6089961 | PIP_rs202143810 | othersnps | PIP_rs6062496(sQTL) | PIP_rs6089961 | PIP_rs202143810 | othersnps | |
| L | ||||||||
| 1 | 1_1 | 1_1 | ||||||
| 2 | 1_1 | 1_2 | 1_1 | 1_2 | ||||
| 3 | 1_3 | 1_2 | rs112662625 0.89_1;rs113365193 0.06_1 | 1_1 | 1_3 | 1_2 | ||
| 4 | 1_1 | 1_2 | 1_1 | 1_2 | ||||
| 5 | 1_3 | 1_2 | rs112662625 0.90_1;rs113365193 0.06_1 | 1_1 | 1_3 | 1_2 | ||
| 6 | 1_1 | 1_2 | 1_1 | 1_2 | rs35201382 0.14_1 | |||
| 7 | 1_3 | 1_2 | rs112662625 0.90_1;rs113365193 0.06_1 | 1_1 | 1_3 | 1_2 | rs35201382 0.13_3 | |
| 8 | 1_1 | 1_2 | rs35201382 0.22_1 | 1_1 | 1_2 | rs35201382 0.30_1 | ||
| 9 | 1_5 | 1_2 | rs35201382 0.21_5, rs112662625 0.90_1;rs113365193 0.07_1 | 1_1 | 1_3 | 1_2 | rs35201382 0.29_3 | |
| 10 | 1_5 | 1_2 | rs35201382 0.2_5, rs367993153 0.91_1, rs1295810 0.08_1 | 1_1 | 1_2 | rs35201382 0.27_1, rs1295810 0.94_9, rs367993153 0.03_9 |
load("/project/xinhe/xsun/multi_group_ctwas/8.deciding_weights/data/R2_topsnp.rdata")
DT::datatable(R2,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','R2 for the SNPs above'),options = list(pageLength = 10) )Using SNP + intron of TNFRSF6B
| standard susie (PIP_CS) | ctwas finemapping (PIP_CS) | |||||||
|---|---|---|---|---|---|---|---|---|
| L | PIP_rs6062496(sQTL) | PIP_rs6089961 | PIP_rs202143810 | othersnps | PIP_rs6062496(sQTL) | PIP_rs6089961 | PIP_rs202143810 | othersnps |
| 1 | rs6062496_0.5_1; TNFRSF6B_0.5_1 | rs6062496_0.5_1; TNFRSF6B_0.5_1 | ||||||
| 2 | 1_1 | 1_2 | 1_1 | 1_2 | ||||
| 3 | 1_3 | 1_2 | rs112662625 0.89_1;rs113365193 0.06_1 | rs6062496_0.5_1; TNFRSF6B_0.5_1 | 1_3 | 1_2 | ||
| 4 | 1_1 | 1_2 | 1_1 | 1_2 | ||||
| 5 | 1_3 | 1_2 | rs112662625 0.90_1;rs113365193 0.06_1 | rs6062496_0.5_1; TNFRSF6B_0.5_1 | 1_3 | 1_2 | ||
| 6 | 1_1 | 1_2 | 1_1 | 1_2 | rs35201382 0.14_1 | |||
| 7 | 1_3 | 1_2 | rs112662625 0.90_1;rs113365193 0.06_1 | rs6062496_0.5_1; TNFRSF6B_0.5_1 | 1_3 | 1_2 | rs35201382 0.13_3 | |
| 8 | 1_1 | 1_2 | rs35201382 0.22_1 | 1_1 | 1_2 | rs35201382 0.30_1 | ||
| 9 | 1_5 | 1_2 | rs35201382 0.21_5, rs112662625 0.90_1;rs113365193 0.07_1 | rs6062496_0.5_1; TNFRSF6B_0.5_1 | 1_3 | 1_2 | rs35201382 0.29_3 | |
| 10 | 1_5 | 1_2 | rs35201382 0.2_5, rs367993153 0.91_1, rs1295810 0.08_1 | 1_1 | 1_2 | rs35201382 0.27_1, rs1295810 0.94_9, rs367993153 0.03_9 |
Run with raw zscores
For standard susie, if we run with raw zscores computed from the origin vcf file:
L=1~10, rs6062496 is the only SNP with PIP > 0.8 & in CS. The PIP for this SNP is 1. For the other two SNPs, rs6089961 and rs202143810, the highest PIP is 0.002.
Compare the z_scores
load("/project/xinhe/xsun/multi_group_ctwas/8.deciding_weights/data/z_snp_merge.rdata")
z_snp_merge$z_computefromvcf <- z_snp_merge$ES_raw/z_snp_merge$SE_raw
DT::datatable(z_snp_merge,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','z_snp'),options = list(pageLength = 10) )plot(z_snp_merge$z_ctwasprocess,z_snp_merge$z_computefromvcf)
| Version | Author | Date |
|---|---|---|
| e6f2526 | XSun | 2024-09-25 |
index <- which(z_snp_merge$z_computefromvcf != -z_snp_merge$z_ctwasprocess)
DT::datatable(z_snp_merge[index,],caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','The outlier SNPs'),options = list(pageLength = 10) )
sessionInfo()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] C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
loaded via a namespace (and not attached):
[1] Rcpp_1.0.12 highr_0.9 pillar_1.9.0 compiler_4.2.0
[5] bslib_0.3.1 later_1.3.0 jquerylib_0.1.4 git2r_0.30.1
[9] workflowr_1.7.0 tools_4.2.0 digest_0.6.29 jsonlite_1.8.0
[13] evaluate_0.15 lifecycle_1.0.4 tibble_3.2.1 pkgconfig_2.0.3
[17] rlang_1.1.2 cli_3.6.1 rstudioapi_0.13 crosstalk_1.2.0
[21] yaml_2.3.5 xfun_0.41 fastmap_1.1.0 stringr_1.5.1
[25] knitr_1.39 fs_1.5.2 vctrs_0.6.5 sass_0.4.1
[29] htmlwidgets_1.5.4 rprojroot_2.0.3 DT_0.22 glue_1.6.2
[33] R6_2.5.1 fansi_1.0.3 rmarkdown_2.25 magrittr_2.0.3
[37] whisker_0.4 promises_1.2.0.1 htmltools_0.5.2 httpuv_1.6.5
[41] utf8_1.2.2 stringi_1.7.6