Genetic analysis of blood molecular phenotypes reveals common properties in the regulatory networks affecting complex traits

Abstract

We evaluate the shared genetic regulation of mRNA molecules, proteins and metabolites derived from whole blood from 3029 human donors. We find abundant allelic heterogeneity, where multiple variants regulate a particular molecular phenotype, and pleiotropy, where a single variant associates with multiple molecular phenotypes over multiple genomic regions. The highest proportion of share genetic regulation is detected between gene expression and proteins (66.6%), with a further median shared genetic associations across 49 different tissues of 78.3% and 62.4% between plasma proteins and gene expression. We represent the genetic and molecular associations in networks including 2828 known GWAS variants, showing that GWAS variants are more often connected to gene expression in trans than other molecular phenotypes in the network. Our work provides a roadmap to understanding molecular networks and deriving the underlying mechanism of action of GWAS variants using different molecular phenotypes in an accessible tissue.

The functional impact of rare variation across the regulatory cascade

“…Our work complements the growing literature in connecting common genetic variation to traits through molecular QTLs. 43 , 44 , 45 , 46 , 47 , 48 , 49 It supports a unified framework where multi-omics functional signals can inform prioritization of genetic variation across the entire frequency spectrum, such that the incorporation of rare variants into common variant frameworks such as polygenic risk scoring could improve stratification of patient risks. 3 An important consideration when predicting disease risk using Watershed posteriors on rare variants, however, is the relevant genetic regulatory context.…”

Instrumental variable and colocalization analyses identify endotrophin and HTRA1 as potential therapeutic targets for coronary artery disease

“… 11 Additionally, the surprisingly low shared genetic regulation of plasma proteins and RNA levels raises the question of whether modeling these different molecular traits as exposures will result in different effect estimates. 17 , 18 , 19 To assess whether transcriptome- and proteome-wide causal inference methods differed in their predictions, we next explored the concordance in direction and size of effect between the transcriptome- and proteome-based methods. The estimates of disease association were generally similar between xWAS and their corresponding MR methods (e.g., Pearson correlation r = 0.80 for effect sizes from coronary artery TWAS and TWMR, data not shown), indicating that the sparser instrument selection framework utilized in two-sample MR can be used to obtain a similar disease association estimate as the xWAS methods.…”

Integrative genomics sheds light on the immunobiology of tuberculosis in cattle

“…However, understanding the biology of these QTLs will be important in bridging the genome to phenome gap for bTB disease resilience because regulatory QTLs, especially cis - and trans -eQTLs, contribute a large proportion of the variance in complex trait heritability 44,45 . Additionally, it has been estimated that up to 50% of GWAS signals are shared with at least one molecular phenotype in humans 79 , with a particular enrichment observed for regulatory QTLs associated with proximal and distal gene expression regulation in PB 80 .…”