Inferring Genome-Wide Correlations of Mutation Fitness Effects between Populations

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.

Hunting for Beneficial Mutations: Conditioning on SIFT Scores When Estimating the Distribution of Fitness Effect of New Mutations

“…It suggests that the DFE may well be altogether rather stable and somewhat immune to the stochasticities of population demography and environment, but instead constrained by intrinsic properties of a species such as genome characteristics or life-history traits. This is in line with the results recently obtained by Huang et al (2021) showing that DFEs are highly correlated between populations of the same species or of closely related ones, or by Chen et al (2017) showing that is almost constant across populations of the same species or between domesticated species and their wild relatives.…”

The genomic origins of the world’s first farmers

“…The joint Distribution of Fitness Effects (DFE) analysis requires a simpler demographic model than our main inference ( Figure 3 ), and simulations suggest that joint DFE analysis is robust to demographic model details ( Huang et al., 2021 ). Based on prior results ( Gravel et al., 2011 ), we used dadi ( Gutenkunst et al., 2009 ) to fit a demographic model to the synonymous data in which the ancestors of the Neolithic population underwent a bottleneck to relative size n B followed by exponential growth (Figure M1_11A).…”

“…We then fit a model including this demographic history plus a joint DFE to the nonsynonymous data ( Huang et al., 2021 ). We modelled the DFE as a bivariate lognormal distribution.…”

Deleterious Mutations Accumulate Faster in Allopolyploid Than Diploid Cotton (Gossypium) and Unequally between Subgenomes

“…One possible interpretation of our results is that Gossypium polyploids are less fit than their closely related diploid progenitors because they harbor more deleterious mutations in their genomes, especially mutations that have already been driven to fixation. We note, however, that the fitness effects of a mutation may change as a result of the genetic (e.g., epistasis) or environmental (e.g., local adaptation, conditional neutrality) context in which it occurs ( Huang et al 2021 ). Comparative genomics techniques used to infer deleterious mutations at phylogenetically conserved sites, as employed here, cannot identify these shifts in fitness effects ( Huber et al 2018 ), and also frequently incorrectly identify beneficial mutations as deleterious ( Chen et al 2020 ).…”