Inferring Genome-Wide Correlations of Mutation Fitness Effects between Populations |
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| Authors: | Xin Huang Alyssa Lyn Fortier Alec J Coffman Travis J Struck Megan N Irby Jennifer E James Jos E Len-Burguete Aaron P Ragsdale Ryan N Gutenkunst |
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| Institution: | 1.Department of Molecular and Cellular Biology, University of Arizona, Tucson, AZ, USA;2.Department of Biology, Stanford University, Stanford, CA, USA;3.Department of Chemistry, University of Pennsylvania, Philadelphia, PA, USA;4.Center for Genomic Sciences, National Autonomous University of Mexico, MR, Mexico;5.Department of Human Genetics, McGill University, Montreal, QC, Canada |
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| 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. |
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| Keywords: | population genetics distribution of fitness effects population divergence |
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