The landscape of nucleotide diversity inDrosophila melanogasteris shaped by mutation rate variation

Abstract

What shapes the distribution of nucleotide diversity along the genome? Attempts to answer this question have sparked debate about the roles of neutral stochastic processes and natural selection in molecular evolution. However, the mechanisms of evolution do not act in isolation, and integrative models that simultaneously consider the influence of multiple factors on diversity are lacking; without them, confounding factors lurk in the estimates. Here we present a new statistical method that jointly infers the genomic landscapes of genealogies, recombination rates and mutation rates. In doing so, our model captures the effects of genetic drift, linked selection and local mutation rates on patterns of genomic variation. Guided by our causal model, we use linear regression to estimate the individual contributions of these micro-evolutionary forces to levels of nucleotide diversity. Our analyses reveal the signature of selection in Drosophila melanogaster, but we estimate that the mutation landscape is the major driver of the distribution of diversity in this species. Furthermore, our simulation study suggests that in many evolutionary scenarios the mutation landscape will be a crucial force shaping diversity, depending notably on the genomic window size used in the analysis. We argue that incorporating mutation rate variation into the null model of molecular evolution will lead to more realistic inference in population genomics.

Improved inference of population histories by integrating genomic and epigenomic data

“…Adding other genomic markers besides SNPs will improve full genome approaches, which are currently limited by the observed nucleotide diversity [34,58,54]. We predict that our results pave the way to improve the inference of 1) biological traits or recombination rate through time [14,60], 2) multiple merger events [36], and 3) recombination and mutation rate maps [5,4]. Our method also should help to dissect the effect of evolutionary forces on genomic diversity [32,31], and to improve the simultaneous detection, quantification and dating of selection events [1,8,30].…”

“…Despite the power of the SMC, well-known model violations such as variation of recombination and mutation rates along the genome [5,4] or pervasive selection [53,31,30] can compromise the accuracy of demographic and selective inference [24,56]. There are two other important issues that have received less attention in the literature.…”

Genomic architecture drives population structuring in Amazonian birds

“…Identifying the driving forces shaping patterns of diversity along the genome of non-model organisms and understanding how theoretical models extend to natural systems is a major endeavor in speciation genomics (Comeron 2014; Elyashiv et al 2016; Stankowski et al 2019; Barroso and Dutheil 2021). In this study, we demonstrated that the interplay between recombination and selection had a strong impact on phylogenetic inference and demographic parameters, which are key for distinguishing alternative spatial models of divergence.…”

“…However, other genomic processes might be shaping phylogenetic signals across the genome. For instance, levels of polymorphism across the genome could be derived from variation in mutation rate (Besenbacher et al 2019; Jónsson et al 2018; Smith et al 2018; Barroso and Dutheil 2021). Non-crossover gene conversion (Korunes and Noor 2017), where DNA strands break during meiosis and are repaired based on homologous sequences without crossing-over, and crossover events could be mutagenic, leading to higher mutation rates in areas of higher recombination (Arbeithuber et al 2015; Korunes and Noor 2017).…”

“…On the other hand, demographic parameters may be heavily biased when recombination and selection are neglected (Pouyet et al 2018;Ewing and Jensen 2016). This conundrum indicates that methods designed to simultaneously account for multiple genomic processes, such as recombination, selection, drift, and mutation (Barroso and Dutheil 2021;Johri et al 2020Johri et al , 2021, associated with simulation studies (Tigano et al 2021) might be necessary to unbiasedly obtain evolutionary parameters from genome-wide variation.…”