NEW YORK – An international team led by investigators at the Wellcome Sanger Institute, the University of Cambridge, and AstraZeneca has systematically spelled out the functional effects of genetic variants implicated in conditions such as dermatitis, hypertension, and COVID-19 susceptibility — particularly risk variants affecting regulatory activity in non-protein coding parts of the genome.
The team's findings appeared in Nature Genetics on Tuesday.
"Our study significantly advances the understanding of the genetic factors that independently affect gene expression in whole blood," co-senior and corresponding author Dirk Paul, with the University of Cambridge and AstraZeneca's Centre for Genomics Research, said in an email.
As part of the INTERVAL study, the researchers used RNA sequencing to profile transcriptomic features found in blood samples from 4,732 participating blood donors. They also assessed samples from the same individuals with SomaScan or Olink protein profiling, mass spectrometry- and nuclear magnetic resonance spectroscopy-based metabolite profiling, and metabolite and lipid analyses done using Metabolon and Nightingale Health technology.
"By combining gene regulatory data with protein, metabolite, and lipid levels from the same individuals, we created an intricate map of how genetic variants can affect how a cell functions," co-first author Elodie Persyn, with the University of Cambridge, said in a statement.
"This approach starts to fill in the gaps in our knowledge and helps to explain how genetic variants might impact disease mechanisms," she explained. "Understanding which pathways are involved in health conditions could help future researchers find new ways to treat those that have a genetic component."
Along with summary statistics from prior genome-wide association studies on traits or conditions such as dermatitis, eczema, plasma protein quantity, hypertension, or COVID-19 susceptibility, the new data helped the team track down 17,233 expression quantitative trait loci (eQTL) and more than 29,500 splicing QTLs (sQTLs) affecting nearly 6,9000 genes.
"Our results confirm previous research that splicing QTLs play a major role in influencing complex traits," Paul noted. "By analyzing splicing QTLs alongside expression QTLs, we discovered additional independent pathways through which genetic variations can impact mRNA and protein levels."
In particular, the researchers highlighted mechanisms involved in hypertension-associated increases in WARS1 gene expression and representation in blood plasma samples. Similarly, they found regulatory features linked to IL7R expression in dermatitis and COVID-19 susceptibility-related expression of IFNAR2, prompting speculation around potential therapeutic targets for such conditions.
The analyses also highlighted overlap between expression or splicing-associated signals and some 3,430 proteomic or metabolomic traits, the researchers reported, while pointing to 222 molecular phenotypes related to gene expression or splicing features flagged in the study.
"We used multi-omics data from the same individuals in the INTERVAL study to systematically conduct mediation analyses and assess causality, offering deeper insights into functional mechanisms of disease loci," Paul explained.
The study's authors have shared their findings through a portal known as INTERVAL RNA-seq, providing what they called "an open-access resource on the shared genetic etiology across transcriptional phenotypes, molecular traits, and health outcomes in humans."
More broadly, the current findings underline the value of integrating multiple types of genomic and molecular data from the same individuals, Paul suggested, and "provide a scientific rationale for the generation of increasingly large-scale QTL data in easily accessible tissues, such as peripheral blood" in large cohorts, including population datasets such as the UK Biobank.
"As molecular data becomes available at the single-cell level across diverse tissues, mapping QTLs on a population scale will be possible," he added. "This data will help us examine gene-regulatory networks with greater detail across specific cell types and dynamic processes."