Inferring Genome-Wide Correlations of Mutation Fitness Effects between Populations

Xin Huang, Alyssa Lyn Fortier, Alec J. Coffman, Travis J. Struck, Megan N. Irby, Jennifer E. James, José E. León-Burguete, Aaron P. Ragsdale, Ryan N. Gutenkunst

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

The effect of a mutation on fitness may differ between populations depending on environmental and genetic context, but little is known about the factors that underlie such differences. To quantify genome-wide correlations in mutation fitness effects, we developed a novel concept called a joint distribution of fitness effects (DFE) between populations. We then proposed a new statistic w to measure the DFE correlation between populations. Using simulation, we showed that inferring the DFE correlation from the joint allele frequency spectrum is statistically precise and robust. Using population genomic data, we inferred DFE correlations of populations in humans, Drosophila melanogaster, and wild tomatoes. In these species, we found that the overall correlation of the joint DFE was inversely related to genetic differentiation. In humans and D. melanogaster, deleterious mutations had a lower DFE correlation than tolerated mutations, indicating a complex joint DFE. Altogether, the DFE correlation can be reliably inferred, and it offers extensive insight into the genetics of population divergence.

Original languageEnglish (US)
Pages (from-to)4588-4602
Number of pages15
JournalMolecular biology and evolution
Volume38
Issue number10
DOIs
StatePublished - Oct 1 2021

Keywords

  • distribution of fitness effects
  • population divergence
  • population genetics

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Molecular Biology
  • Genetics

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