Sources of gene expression variation in a globally diverse human cohort

Dylan J. Taylor, Surya B. Chhetri, Michael G. Tassia, Arjun Biddanda, Stephanie M. Yan, Genevieve L. Wojcik, Alexis Battle and Rajiv C. McCoy ()
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Dylan J. Taylor: Johns Hopkins University
Surya B. Chhetri: Johns Hopkins University
Michael G. Tassia: Johns Hopkins University
Arjun Biddanda: Johns Hopkins University
Stephanie M. Yan: Johns Hopkins University
Genevieve L. Wojcik: Johns Hopkins University
Alexis Battle: Johns Hopkins University
Rajiv C. McCoy: Johns Hopkins University

Nature, 2024, vol. 632, issue 8023, 122-130

Abstract: Abstract Genetic variation that influences gene expression and splicing is a key source of phenotypic diversity1–5. Although invaluable, studies investigating these links in humans have been strongly biased towards participants of European ancestries, which constrains generalizability and hinders evolutionary research. Here to address these limitations, we developed MAGE, an open-access RNA sequencing dataset of lymphoblastoid cell lines from 731 individuals from the 1000 Genomes Project6, spread across 5 continental groups and 26 populations. Most variation in gene expression (92%) and splicing (95%) was distributed within versus between populations, which mirrored the variation in DNA sequence. We mapped associations between genetic variants and expression and splicing of nearby genes (cis-expression quantitative trait loci (eQTLs) and cis-splicing QTLs (sQTLs), respectively). We identified more than 15,000 putatively causal eQTLs and more than 16,000 putatively causal sQTLs that are enriched for relevant epigenomic signatures. These include 1,310 eQTLs and 1,657 sQTLs that are largely private to underrepresented populations. Our data further indicate that the magnitude and direction of causal eQTL effects are highly consistent across populations. Moreover, the apparent ‘population-specific’ effects observed in previous studies were largely driven by low resolution or additional independent eQTLs of the same genes that were not detected. Together, our study expands our understanding of human gene expression diversity and provides an inclusive resource for studying the evolution and function of human genomes.

Date: 2024
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DOI: 10.1038/s41586-024-07708-2

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