Shared evolutionary processes shape landscapes of genomic variation in the great apes

For at least the past 5 decades, population genetics, as a field, has worked to describe the precise balance of forces that shape patterns of variation in genomes. The problem is challenging because modeling the interactions between evolutionary processes is difficult, and different processes can impact genetic variation in similar ways. In this paper, we describe how diversity and divergence between closely related species change with time, using correlations between landscapes of genetic variation as a tool to understand the interplay between evolutionary processes. We find strong correlations between landscapes of diversity and divergence in a well-sampled set of great ape genomes, and explore how various processes such as incomplete lineage sorting, mutation rate variation, GC-biased gene conversion and selection contribute to these correlations. Through highly realistic, chromosome-scale, forward-in-time simulations, we show that the landscapes of diversity and divergence in the great apes are too well correlated to be explained via strictly neutral processes alone. Our best fitting simulation includes both deleterious and beneficial mutations in functional portions of the genome, in which 9% of fixations within those regions is driven by positive selection. This study provides a framework for modeling genetic variation in closely related species, an approach which can shed light on the complex balance of forces that have shaped genetic variation.

Intraspecific Variation in Microsatellite Mutation Profiles in Daphnia magna

Microsatellite loci (tandem repeats of short nucleotide motifs) are highly abundant in eukaryotic genomes and often used as genetic markers because they can exhibit variation both within and between populations. Although widely recognized for their mutability and utility, the mutation rates of microsatellites have only been empirically estimated in a few species, and have rarely been compared across genotypes and populations within a species. Here, we investigate the dynamics of microsatellite mutation over long- and short-time periods by quantifying the starting abundance and mutation rates for microsatellites for six different genotypes of Daphnia magna, an aquatic microcrustacean, collected from three populations (Finland, Germany, and Israel). Using whole-genome sequences of these six starting genotypes, descendent mutation accumulation (MA) lines, and large population controls (non-MA lines), we find each genotype exhibits a distinctive initial microsatellite profile which clusters according to the population-of-origin. During the period of MA, we observe motif-specific, highly variable, and rapid microsatellite mutation rates across genotypes of D. magna, the average of which is order of magnitude greater than the recently reported rate observed in a single genotype of the congener, Daphnia pulex. In our experiment, genotypes with more microsatellites starting out exhibit greater losses and those with fewer microsatellites starting out exhibit greater gains—a context-dependent mutation bias that has not been reported previously. We discuss how genotype-specific mutation rates and spectra, in conjunction with evolutionary forces, can shape both the differential accumulation of repeat content in the genome and the evolution of mutation rates.

How much of the variation in the mutation rate along the human genome can be explained?

It has been claimed recently that it may be possible to predict the rate of de novo mutation of each site in the human genome with a high degree of accuracy [Michaelson et al. (2012), Cell 151: 1431−1442]. We show that this claim is unwarranted. By considering the correlation between the rate of de novo mutation and the predictions from the model of Michaelson et al., we show there could be substantial unexplained variance in the mutation rate. We investigate whether the model of Michaelson et al. captures variation at the single nucleotide level that is not due to simple context. We show that the model captures a substantial fraction of this variation at CpG dinucleotides but fails to explain much of the variation at non-CpG sites.