bioRxivEvolutionary Biology
Impact of mutation rate and selection at linked sites on DNA variation across the genomes of humans and other homininae
Abstract
DNA diversity varies across the genome of many species. Variation in diversity across a genome might arise for one of three reasons; regional variation in the mutation rate, selection and biased gene conversion. We show that both non-coding and non-synonymous diversity are correlated to a measure of the mutation rate, the recombination rate and the density of conserved sequences in 50KB windows across the genomes of humans and non-human homininae. We show these patterns persist even when we restrict our analysis to GC-conservative mutations, demonstrating that the patterns are not driven by biased gene conversion. The positive correlation between diversity and our measure of the mutation rate seems to be largely a direct consequence of regions with higher mutation rates having more diversity. However, the positive correlation with recombination rate and the negative correlation with the density of conserved sequences suggests that selection at linked sites affect levels of diversity. This is supported by the observation that the ratio of the number of non-synonymous to non-coding polymorphisms is negatively correlated to a measure of the effective population size across the genome. Furthermore, we find evidence that these genomic variables are better predictors of non-coding diversity in large homininae populations than in small populations, after accounting for statistical power. This is consistent with genetic drift decreasing the impact of selection at linked sites in small populations. In conclusion, our comparative analyses describe for the first time how recombination rate, gene density, mutation rate and genetic drift interact to produce the patterns of DNA diversity that we observe along and between homininae genomes.
- organism: human
- organism: non-human primate
- assay: whole-genome sequencing
- research focus: chromatin & epigenetic regulation
- research focus: genome assembly
- approach: statistical model
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