bioRxivEvolutionary Biology
Local fitness and epistatic effects lead to distinct patterns of linkagedisequilibrium in protein-coding genes
Abstract
Selected mutations interfere and interact with evolutionary processes at nearby loci, distorting allele frequency trajectories and creating correlations between pairs of mutations. A number of recent studies have used patterns of linkage disequilibrium (LD) between selected variants to test for selective interference and epistatic interactions, with some disagreement over interpreting observations from data. Interpretation is hindered by a lack of analytic or even numerical expectations for patterns of variation between pairs of loci under the combined effects of selection, dominance, epistasis, and demography. Here, I develop a numerical approach to compute the expected two-locus sampling distribution under diploid selection with arbitrary epistasis and dominance, recombination, and variable population size. I use this to explore how epistasis and dominance affect expected signed LD, including for non-steadystate demography relevant to human populations. Using whole-genome sequencing data from humans, I explore genome-wide patterns of LD within protein-coding genes. I show that positive LD between missense mutations within genes is driven by strong positive allele-frequency correlations between pairs of mutations that fall within the same annotated conserved domain, pointing to compensatory mutations or antagonistic epistasis as the prevailing mode of interaction within conserved genic elements. LD between missense mutations is reduced outside of conserved domains, as would expected under Hill-Robertson interference. This variation in both mutational fitness effects and selective interactions within proteincoding genes calls for more refined inferences of the joint distribution of fitness and interactive effects, and the methods presented here should prove useful in that pursuit.
- organism: human
- assay: whole-genome sequencing
- research focus: chromatin & epigenetic regulation
- approach: statistical model
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