NEW YORK – Members of a large, international team have mapped out nearby and long-range expression quantitative trait loci (eQTL) influencing gene expression profiles found in the blood — a collection of regulatory clues that is expected to inform future analyses of variants influencing human traits and conditions, including genome-wide association studies.
"[T]he interpretation of the results of GWAS is not straightforward and it is often unclear via which mechanisms the identified variants exert their effect on the phenotype," co-first author and corresponding author Urmo Võsa, a researcher at the University of Tartu's Estonian Genome Centre, explained in an email, adding that "we aimed to expand the current knowledge and identify which trait-related variants of the genome also affect the activity of the genes in the blood."
For a study published in Nature Genetics on Thursday, members of the eQTLGen Consortium brought together genotypes and array- or RNA sequencing-based expression profiles for blood or peripheral blood mononuclear cell (PBMC) samples for nearly 31,700 participants from dozens of eQTLGen Consortium cohorts, focusing in on so-called cis-eQTLs influencing the expression of 16,987 nearby genes — representing just over 88 percent of genes analyzed — along with trans-eQTLs with effects on almost 6,300 distant genes (accounting for around 32 percent of the genes).
"Such a large sample size gave us the unprecedented statistical power to investigate aspects for which smaller datasets are un-optimal," Võsa said, noting that "the catalog of genetic associations on blood gene expression can be a useful resource for interpreting the findings of GWASs. It can also be treated as a prioritized list of variants and genes for which to focus on more fine-grained follow-up studies."
When the team looked at more than 10,300 variants previously linked to human traits, it found that some 37 percent appeared to have trans-eQTL roles. Meanwhile, the investigators' search for potential ties between polygenic scores (PGS) spanning 1,263 phenotypes and blood-based gene expression patterns pointed to a set of expression quantitative trait scores (eQTSs) affecting an estimated 2,568 genes.
"[I]t remains challenging to systematically evaluate which fraction of the detected eQTS genes is causal," the authors explained, though they added that "our results can serve as a starting point to follow up on eQTS genes and to ascertain their role in complex traits. Our eQTS analysis provides a comprehensive resource for blood that can be used to interpret the effects of PGS on a molecular level."
While the trans-eQTLs were less likely than cis-eQTLs to be validated in their follow-up analyses on single-cell RNA sequencing data on PBMCs from 1,139 individuals, the researchers were able to define transcription factor activity and other proposed biological mechanisms for roughly half of the trans-eQTLs considered.
"Trans-eQTL exerted their effects via several mechanisms, primarily through regulation by transcription factors," they reported.
Using published GWAS data, the team tracked down 59,786 trans-eQTL associations involving trait-associated SNPs from past GWAS, while defining centrally acting SNPs and flagging traits with associated variants that affected the same distant gene. The analyses also highlighted the possibility of narrowing in on expression-related variants and eQTS for traits and conditions evaluated by GWAS based on summary statistic data.
"Full summary statistics for our cis-eQTL, trans-eQTL, and eQTS analyses can be used to interpret GWASs, to prioritize putative trait-related genes for in-depth functional studies, and to develop methods to perform these tasks," the authors wrote. "We envision that upcoming statistical tools and frameworks that enable federated analyses in consortia will facilitate conducting highlight powered global trans-eQTL studies."