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An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
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A gene-level test for directional selection on gene expression.

Laura L Colbran1, Fabian C Ramos-Almodovar, Iain Mathieson

  • 1Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.

Genetics
|April 10, 2023
PubMed
Summary
This summary is machine-generated.

We developed a new method to detect population-specific selection on regulatory variants influencing gene expression. This approach identified 45 genes under selection, offering insights into human adaptation and gene regulation evolution.

Keywords:
evolutiongene regulationhuman evolutionquantitative geneticsselection

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Area of Science:

  • Human genomics
  • Evolutionary biology
  • Population genetics

Background:

  • Most genetic variants identified in human studies are noncoding, limiting understanding of their phenotypic effects.
  • Interpreting noncoding variants' roles in human adaptation is challenging due to difficulties in linking them to specific genes and regulatory functions.

Purpose of the Study:

  • To develop and apply a novel gene-wise test for population-specific selection acting on cis-regulatory variants.
  • To overcome limitations in interpreting noncoding variants by focusing on their combined effects on gene regulation.

Main Methods:

  • Developed a gene-wise test using the QX statistic to assess polygenic selection on cis-regulatory variants.
  • Tested for selection on 17,388 protein-coding genes across 26 populations using data from the Thousand Genomes Project.
  • Evaluated whether variance in predicted gene expression across populations exceeds neutral expectations.

Main Results:

  • Identified 45 genes (0.2%) showing significant evidence of selection (False Discovery Rate < 0.1).
  • Notable genes under selection include FADS1, KHK, SULT1A2, ITGAM, and several in the HLA region.
  • Confirmed that detected selection signals correlate with plausible population-level differences in predicted gene expression.

Conclusions:

  • The developed QX score provides a method independent of traditional genomic tests for selection, specifically identifying selection on regulatory variation.
  • The findings highlight the utility of integrating population genomics with functional data to study the evolution of gene expression.
  • The low number of significant genes suggests that most cis-regulatory variation may evolve under genetic drift or stabilizing selection.