Last updated: 2025-04-24

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Data

To analyze the correlation between polygenetic risk score (PRS) and gene expression, we first compute PRS of each GTEx samples for a trait:

SNP-based PRS:

Immune traits: trained by our collaborator, Salem Werdyani. Study information including Open GWAS ID, sample sizes, number of cases & controls are listed below:

Trait	pmid	Open GWAS ID	Sample size	Cases	Control
Celiac	34278373	GCST90014442	326,438	2,364	324,074
Celiac	38789286 20190752	GCST90468120	15,283	4,533	10,750
IBD	34017140	GCST90013901	407,746	4,161	403,585
IBD	34017140	GCST90013951	404,781	4,130	400,650
SLE	26502338	GCST003156	14,267	5,201	9,066
SLE	33536424	GCST011096	12,615	4,576	8,039
T1D	33830302	GCST90000529	17,685	7,467	10,218
T1D	34012112	GCST90014023	520,580	18,942	501,638

blood cell counts: sourced from Vuckovic D et al’s study. The study leverages UK Biobank cohort to perform a genome-wide discovery analysis in 408,112 European participants, investigating 29 blood cell phenotypes.

Both data sources provide effect weights for SNPs associated with specific traits, facilitating the construction of PRS.

GTEx genotype data (version 8), consisting of 15,201 RNA-sequencing samples from 49 tissues of 838 postmortem donors. Here, we focus on whole blood samples (n = 670).

sessionInfo()

R version 4.2.2 (2022-10-31)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur ... 10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/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_1.7.1

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.14       compiler_4.2.2    pillar_1.10.1     bslib_0.9.0      
 [5] later_1.4.1       git2r_0.33.0      jquerylib_0.1.4   tools_4.2.2      
 [9] getPass_0.2-4     digest_0.6.37     jsonlite_1.8.9    evaluate_1.0.3   
[13] lifecycle_1.0.4   tibble_3.2.1      pkgconfig_2.0.3   rlang_1.1.5      
[17] cli_3.6.3         rstudioapi_0.17.1 yaml_2.3.10       xfun_0.50        
[21] fastmap_1.2.0     httr_1.4.7        stringr_1.5.1     knitr_1.49       
[25] fs_1.6.5          vctrs_0.6.5       sass_0.4.9        rprojroot_2.0.4  
[29] glue_1.8.0        R6_2.5.1          processx_3.8.5    rmarkdown_2.29   
[33] callr_3.7.6       magrittr_2.0.3    whisker_0.4.1     ps_1.8.1         
[37] promises_1.3.2    htmltools_0.5.8.1 httpuv_1.6.15     stringi_1.8.4    
[41] cachem_1.1.0

PRS_Data

Data