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Gene Expression, Underlying Genetic Variation Profiled in Diverse Human Populations

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NEW YORK – Genetic variants appear to have relatively consistent effects on gene expression and splicing across diverse human populations, according to new research by a Johns Hopkins University team that assembled an atlas of transcriptomic features found in hundreds of individuals from global human populations.

The findings appeared in Nature on Wednesday.

"We identified thousands of variants in DNA that influence patterns of gene expression and splicing and may in turn impact variation in traits," senior and corresponding author Rajiv McCoy, a researcher at Johns Hopkins University, said in an email. "These include a subset of variants that are only found in populations that were underrepresented in previous studies."

For their analyses, McCoy and his colleagues used RNA sequencing to characterize gene expression and transcript splicing in lymphoblastoid cell line (LCL) samples from 731 participants in the 1000 Genomes Project including more than two dozen populations spanning five continents.

"Most research in human genetics has historically focused on people of European ancestries — a longstanding bias that may limit the accuracy of scientific predictions for people from other populations," McCoy said. "To address this bias, we generated a gene expression dataset from participants from diverse populations around the world to increase representation of understudied populations and attain more accurate insights of genetic factors driving human trait diversity."

By pairing the resulting "multi-ancestry analysis of gene expression" (MAGE) dataset with available whole-genome sequencing data for the 1000 Genomes Project participants, they were then able to spell out the expression and splicing variation found within and between the populations profiled.

"Combined with existing whole-genome sequencing data from the same samples, MAGE offers a large open-access dataset for studying the diversity and evolution of human gene expression and splicing," the authors reported, noting that the work "also offers insight into the genetic sources of variation in these key molecular phenotypes, which may in turn mediate variation in organismal traits."

When they analyzed the gene expression data in combination with genetic data and epigenomic clues from the Roadmap Epigenomics effort, for example, the investigators tracked down tens of thousands of expression quantitative trait loci (QTLs) and splicing QTLs with potential ties to specific epigenomic signatures in the genome.

A subset of the QTLs — nearly 1,700 splicing QTLs and more than 1,300 expression QTLs — appeared to be specific to human populations with poor representation in previous studies, they reported. Even so, causal expression QTLs appeared to have shared directional effects across populations, affecting expression to a similar extent.

Likewise, the team estimated that more than 92 percent and 95 percent of the variation in expression and splicing, respectively, turned up within rather than between populations, hinting that genetic effects on transcriptomic traits is relatively consistent across ancestry groups.

Such results argue against the notion that genetic variants — including those with ties to specific traits or conditions — have distinct consequences in different ancestry groups, McCoy explained. Instead, he and his coauthors speculated that the limited portability of genetics-based trait or disease predictions between different human populations may reflect a limited understanding of the variant combinations that exist within these populations or ancestry groups.

"We found that apparent evidence of these [genetic] 'effect differences' was likely instead caused by a failure to account for the combination of multiple trait-associated mutations, some of which might be differentially present or absent in different groups," McCoy said.

Such insights may offer clues for interpreting findings from genome-wide association studies and trait-related variants found at QTL sites, he noted, including GWAS focused on clinically relevant traits or conditions that are overrepresented in some ancestry groups or populations.

"[W]e hope our results may help reduce some of the historical health disparities between ancestry groups, but we also want to emphasize that our study is designed to produce results that are more broadly relevant and applicable," McCoy said.

"We are entering the era of personalized medicine, and we don't want most of the world to get left behind," he added. "We hope that our study helps build a foundation for more equitable medical advances."