Large scale variation in the rate ofde novomutation, base composition, divergence and diversity in humans

Abstract

The rate of divergence between species, and the level diversity within a species, are expected to depend on the rate of mutation. Here we investigate the relationship between divergence, diversity and the rate of mutation across the human genome at scales of 1MB and 100KB using >40,000 de novo mutations (DNM). We show that there is significant variation in the rate of DNM across the human genome, but that the variation is modest. Different types of mutation show similar levels of variation and appear to vary in concert. Regressing the rate of DNM against a range of genomic features suggests that nucleosome occupancy is the most important correlate, but that GC content, recombination rate, replication time and various histone methylation signals also correlate significantly. In total the model explains 75% of the explainable variance. As expected the rate of divergence between species and the level of diversity within humans is correlated to the rate of DNM. However, the correlations are weaker than if all the variation in divergence was due to variation in the mutation rate. We provide evidence that this is due the effect of biased gene conversion on the probability that a mutation will become fixed. Finally, we show that the correlation between divergence and DNM density declines as increasingly divergent species are considered. Our results have important implications for the use of divergence and diversity data to study variation in the mutation rate.

Genomewide patterns of variation in genetic diversity are shared among populations, species and higher‐order taxa

“…However, support for a role of mutation rate in modulating the level of genetic variation and differentiation across the genome is limited (Cutter & Payseur, ). While some studies found a contribution (Dutoit et al., ; Smith & Eyre‐Walker, ), genetic diversity is generally only weakly associated with proxies for mutation rate (Cutter & Payseur, ; Vijay et al., ). Another parameter that can affect genetic diversity is recombination rate which is reportedly conserved at broadscale between clades (Auton et al., ; Burri et al., ; Kawakami et al., ; Roesti, Hendry, Salzburger, & Berner, ; Singhal et al., ; Tine et al., ).…”

Comparison of the full distribution of fitness effects of new amino acid mutations across great apes

“…Great apes share most of their genes and genomic configuration. Gene density, mutation rate and recombination rate (at least at broad scale) are very similar in these species (Stevison et al 2016;Smith et al 2018;Kronenberg et al 2018;Besenbacher et al 2019). Thus, our inference of the DFE is affected only marginally by variation in these factors.…”

Recommendations for improving statistical inference in population genomics

“…For a handful of commonly studied species, both the mean of, and genomic heterogeneity in, mutation rates have been quantified via mutationaccumulation lines and/or pedigree studies (Pfeifer 2020a). However, even for these species, ascertainment issues remain complicating (Smith et al 2018), variation amongst individuals may be substantial (Ness et al 2015), and estimates only represent a temporal snapshot of rates and patterns that are probably changing over evolutionary time-scales and may be affected by the environment (Lynch et al 2016;Maddamsetti & Grant 2020). In organisms lacking experimental information, often the best available estimates come either from a distantly related species or from molecular clock-based approaches.…”

“…Nevertheless, all models should not be viewed equally. Decades of work supporting the central tenets of the Neutral Theory (Jensen et al 2019), high-quality experimental and computational work quantifying mutation rate and recombination rate (e.g., Lynch et al 2008;Comeron et al 2012;Ness et al 2015;Smith et al 2018;Pfeifer 2020a), constantly improving experimental and theoretical approaches to quantify the neutral and deleterious DFE from natural population, mutation-accumulation, or directed mutagenesis data (e.g., Bank et al 2014b;Foll et al 2014;Böndel et al 2019;Johri et al 2020), and often historical knowledge (e.g., anthropological, ecological, clinical) of population size change or structure -combined with the fact that all of these processes may strongly shape observed levels and patterns of variation and divergence -justify their status in comprising the appropriate baseline model for genomic analysis. Given this, and particularly once accounting for the inflation of variance contributed by uncertainty in these parameters, potential model violations, as well as the quantity and quality of data available in any given analysis, it will often be the case that many hypotheses of interest may not be addressable with the dataset and knowledge at hand.…”