Last updated: 2022-01-04
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Observations from the paper:
Meta-analysis using up to 31,684 blood samples from 37 eQTLGen Consortium cohorts.
For trans, they focused on 10,317 trait-associated SNPs.
The paper linked trans-eQTLs with traits in two ways to find the potential driver genes for traits.
One-third of trait-associated variants have distal effects.
Identified 59,786 trans-eQTL, representing 3,853 SNPs (37% of tested GWAS SNPs) and 6,298 genes (32% of tested genes).
The largest previous trans-eQTL meta-analysis in blood (N = 5,311) identified trans-eQTL for 8% of tested SNPs.
Identify genes that are coordinately affected by multiple independent trait-associated SNPs.
Identified 47 GWAS traits for which at least four independent variants affected the same gene in trans (Supplementary Tables 10). Examples genes affected by at least three SLE-associated genetic variants.
But, Individual trans-eQTL effects too weak to detect. Another way to look for the potential driver genes for traits and the “core” genes:
Individual trait-associated SNPs are combined into a PGS that is associated with gene expression.
when the PGS for a trait correlates with the expression of a gene, trans-eQTL effects of individual risk variants converge on that gene, and it can be prioritized as a putative driver of the disease.
1,263 traits in total. 18,210 eQTSs representing 689 unique traits (55% of tested traits) and 2,568 genes (13% of tested genes).
Of these genes, 719 (28%) were not identified in the trans-eQTL analysis.
Therefore, all 3,853 trans-eQTLs eQTLGen identified are GWAS hits.
Among 10,317 trait-associated SNPs, 9,056 (~89%) are included in DGN SNPs. Among 3,853 eQTLGen trans- eQTLs, 27 (~0.7%) are replicated in DGN signals (1,863, \(p<1e-8\)).
In the paper, another trans- study was mentioned,
The largest previous trans-eQTL meta-analysis in blood (N = 5,311) identified trans-eQTL for 8% of tested SNPs.
As noted in earlier section, we observed,
Therefore, we wanted to know,
I looked at the expression matrix of 13634 genes. ZNF90P1 doesn’t have expression data in this matrix.
See files for the cross mappability for module 25, module 51, module 153, module 156.
The columns include: “score” for cross mappability score, “score_map1” for mappability score of gene1, “score_map2” for mappability score of gene2.
See a previous file here.
https://www-nature-com.proxy.uchicago.edu/articles/s41588-021-00969-x Another paper highlighting SENP7 as a trans-meQTL.
R version 4.1.0 (2021-05-18)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.7 whisker_0.4 knitr_1.33 magrittr_2.0.1
[5] R6_2.5.1 rlang_0.4.12 fastmap_1.1.0 fansi_0.5.0
[9] stringr_1.4.0 tools_4.1.0 xfun_0.23 utf8_1.2.2
[13] git2r_0.28.0 jquerylib_0.1.4 htmltools_0.5.2 ellipsis_0.3.2
[17] rprojroot_2.0.2 yaml_2.2.1 digest_0.6.29 tibble_3.1.6
[21] lifecycle_1.0.1 crayon_1.4.2 later_1.3.0 sass_0.4.0
[25] vctrs_0.3.8 promises_1.2.0.1 fs_1.5.2 glue_1.6.0
[29] evaluate_0.14 rmarkdown_2.10 stringi_1.7.6 bslib_0.3.1
[33] compiler_4.1.0 pillar_1.6.4 jsonlite_1.7.2 httpuv_1.6.4
[37] pkgconfig_2.0.3