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Population bottlenecks followed by re-expansions have been common throughout history of many populations. The response of alleles under selection to such demographic perturbations has been a subject of great interest in population genetics. On the basis of theoretical analysis and computer simulations, we suggest that this response qualitatively depends on dominance. The number of dominant or additive deleterious alleles per haploid genome is expected to be slightly increased following the bottleneck and re-expansion. In contrast, the number of completely or partially recessive alleles should be sharply reduced. Changes of population size expose differences between recessive and additive selection, potentially providing insight into the prevalence of dominance in natural populations. Specifically, we use a simple statistic, [Formula: see text], where xi represents the derived allele frequency, to compare the number of mutations in different populations, and detail its functional dependence on the strength of selection and the intensity of the population bottleneck. We also provide empirical evidence showing that gene sets associated with autosomal recessive disease in humans may have a BR indicative of recessive selection. Together, these theoretical predictions and empirical observations show that complex demographic history may facilitate rather than impede inference of parameters of natural selection.

Understanding the role of rare variants is important in elucidating the genetic basis of human disease. Negative selection can cause rare variants to have larger per-allele effect sizes than common variants. Here, we develop a method to estimate the minor allele frequency (MAF) dependence of SNP effect sizes. We use a model in which per-allele effect sizes have variance proportional to [ p (1 −  p )] α , where p is the MAF and negative values of α imply larger effect sizes for rare variants. We estimate α for 25 UK Biobank diseases and complex traits. All traits produce negative α estimates, with best-fit mean of –0.38 (s.e. 0.02) across traits. Despite larger rare variant effect sizes, rare variants (MAF < 1%) explain less than 10% of total SNP-heritability for most traits analyzed. Using evolutionary modeling and forward simulations, we validate the α model of MAF-dependent trait effects and assess plausible values of relevant evolutionary parameters. Negative selection removes deleterious genetic variation, and can influence genetic architectures and evolution of complex traits. Here, the authors analyze data from 25 UK Biobank diseases and complex traits, and quantify frequency-dependent genetic architectures which suggests actions of negative selection.

Shamil Sunyaev, David Beier and colleagues report an analysis of the fitness effects of heterozygous protein-truncating variants from the Exome Aggregation Consortium. They find that high heterozygous selection coefficients are enriched in Mendelian disease-associated genes and essential mouse genes, suggesting that this coefficient can be used to prioritize candidate disease-associated genes from clinical exome-sequencing data. The evolutionary cost of gene loss is a central question in genetics and has been investigated in model organisms and human cell lines 1 , 2 , 3 . In humans, tolerance of the loss of one or both functional copies of a gene is related to the gene's causal role in disease. However, estimates of the selection and dominance coefficients in humans have been elusive. Here we analyze exome sequence data from 60,706 individuals 4 to make genome-wide estimates of selection against heterozygous loss of gene function. Using this distribution of selection coefficients for heterozygous protein-truncating variants (PTVs), we provide corresponding Bayesian estimates for individual genes. We find that genes under the strongest selection are enriched in embryonic lethal mouse knockouts, Mendelian disease-associated genes, and regulators of transcription. Screening by essentiality, we find a large set of genes under strong selection that are likely to have crucial functions but have not yet been thoroughly characterized.

Using the Telomere-to-Telomere reference, we assemble the distribution of simple tandem repeat lengths present in the human genome. Analyzing over three hundred mammalian genomes, we find remarkable consistency in the shape of the distribution across evolutionary epochs. All observed genomes harbor an excess of long repeats, which are potentially prone to developing into repeat expansion disorders. We measure mutation rates for repeat length instability, quantitatively model the per-generation action of mutations, and observe the corresponding long-term behavior shaping the repeat tract length distribution. We find that short repetitive sequences appear to be a straightforward consequence of random substitution. Evolving largely independently, longer repeats (above roughly 10 nt) emerge and persist in a rapidly mutating dynamic balance between expansion, contraction, and interruption. These mutational processes, collectively, are sufficient to explain the abundance of long repeats, without invoking natural selection. Our analysis constrains properties of molecular mechanisms responsible for maintaining genome fidelity that underlie repeat instability. Repetitive DNA sequences shape genome evolution and instability. Here, the authors analyze repeat length distributions across over 300 mammals and show that long repeats arise and persist through a dynamic balance of mutation processes, without requiring natural selection.

When large asexual populations adapt competition between simultaneously segregating mutations slows the rate of adaptation and restricts the set of mutations that eventually fix. This phenomenon of interference arises from competition between mutations of different strengths as well as competition between mutations that arise on different fitness backgrounds. Previous work has explored each of these effects in isolation, but the way they combine to influence the dynamics of adaptation remains largely unknown. Here, we describe a theoretical model to treat both aspects of interference in large populations. We calculate the rate of adaptation and the distribution of fixed mutational effects accumulated by the population. We focus particular attention on the case when the effects of beneficial mutations are exponentially distributed, as well as on a moré general class of exponential-like distributions. In both cases, we show that the rate of adaptation and the influence of genetic background on the fixation of new mutants is equivalent to an effective model with a single selection coefficient and rescaled mutation rate, and we explicitly calculate these effective parameters. We find that the effective selection coefficient exactly coincides with the most common fixed mutational effect. This equivalence leads to an intuitive picture of the relative importance of different types of interference effects, which can shift dramatically as a function of the population size, mutation rate, and the underlying distribution of fitness effects.

Understanding the relationship between natural selection and phenotypic variation has been a long-standing challenge in human population genetics. With the emergence of biobank-scale datasets, along with new statistical metrics to approximate strength of purifying selection at the variant level, it is now possible to correlate a proxy of individual relative fitness with a range of medical phenotypes. We calculated a per-individual deleterious load score by summing the total number of derived alleles per individual after incorporating a weight that approximates strength of purifying selection. We assessed four methods for the weight, including GERP, phyloP, CADD, and fitcons. By quantitatively tracking each of these scores with the site frequency spectrum, we identified phyloP as the most appropriate weight. The phyloP-weighted load score was then calculated across 15,129,142 variants in 335,161 individuals from the UK Biobank and tested for association on 1,380 medical phenotypes. After accounting for multiple test correction, we observed a strong association of the load score amongst coding sites only on 27 traits including body mass, adiposity and metabolic rate. We further observed that the association signals were driven by common variants (derived allele frequency > 5%) with high phyloP score (phyloP > 2). Finally, through permutation analyses, we showed that the load score amongst coding sites had an excess of nominally significant associations on many medical phenotypes. These results suggest a broad impact of deleterious load on medical phenotypes and highlight the deleterious load score as a tool to disentangle the complex relationship between natural selection and medical phenotypes.

The fate of alleles in the human population is believed to be highly affected by the stochastic force of genetic drift. Estimation of the strength of natural selection in humans generally necessitates a careful modeling of drift including complex effects of the population history and structure. Protein-truncating variants (PTVs) are expected to evolve under strong purifying selection and to have a relatively high per-gene mutation rate. Thus, it is appealing to model the population genetics of PTVs under a simple deterministic mutation–selection balance, as has been proposed earlier (Cassa et al. 2017). Here, we investigated the limits of this approximation using both computer simulations and data-driven approaches. Our simulations rely on a model of demographic history estimated from 33,370 individual exomes of the Non-Finnish European subset of the ExAC data set (Lek et al. 2016). Additionally, we compared the African and European subset of the ExAC study and analyzed de novo PTVs. We show that the mutation–selection balance model is applicable to the majority of human genes, but not to genes under the weakest selection.

The vast majority of mutations are deleterious and are eliminated by purifying selection. Yet in finite asexual populations, purifying selection cannot completely prevent the accumulation of deleterious mutations due to Muller’s ratchet: once lost by stochastic drift, the most-fit class of genotypes is lost forever. If deleterious mutations are weakly selected, Muller’s ratchet can lead to a rapid degradation of population fitness. Evidently, the long-term stability of an asexual population requires an influx of beneficial mutations that continuously compensate for the accumulation of the weakly deleterious ones. Hence any stable evolutionary state of a population in a static environment must involve a dynamic mutation–selection balance, where accumulation of deleterious mutations is on average offset by the influx of beneficial mutations. We argue that such a state can exist for any population size N and mutation rate U and calculate the fraction of beneficial mutations, ε, that maintains the balanced state. We find that a surprisingly low ε suffices to achieve stability, even in small populations in the face of high mutation rates and weak selection, maintaining a well-adapted population in spite of Muller’s ratchet. This may explain the maintenance of mitochondria and other asexual genomes.

Both the simulations and data suggest that the deterministic approximation is applicable for genes under strong selection, including genes with ⅛ = 0.05, but drift affects selection estimates for the minority of human genes evolving under weak negative selection. Considering a sample of size N chromosomes, the variance of the number of PTV counts on a gene, n, can be expressed as a sum of sampling variance and variance due to genetic drift in the population. For the final exponential-growth phase, we matched properties of rare alleles in the ExAC NFE sample to gauge the final effective population size and corresponding growth rate. Christopher A. Cassa1'2'3'11, Donate Weghorn©12,10,11, Daniel J. Balick1,2,11, Daniel M. Jordan©4,11, David Nusinow1, Kaitlin E. Samocha5,6, Anne O'Donnell-Luria5,7, Daniel G. MacArthur3'5, Mark J. Daly©3'5, David R. Beier8'9· and Shamil R. Sunyaev©1'2'3· 1Division of Genetics, Brigham and Womens Hospital, Harvard Medical School, Boston, MA, USA. 2Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. 3Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA. department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA. 5Analytic and Translational Genetics Unit, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. 6Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA. 7Division of Genetics and Genomics, Boston Childrens Hospital, Boston, MA, USA. 8 Center for Developmental Biology and Regenerative Medicine, Seattle Childrens Research Institute, Seattle, WA, USA. 9Department of Pediatrics, University of Washington School of Medicine, Seattle, WA, USA. 10Present address:

David Reich, Shamil Sunyaev and colleagues report an analysis of the per-genome accumulation of nonsynonymous substitutions across diverse pairs of human populations. They find no evidence for a higher load of deleterious mutations in non-Africans than in West Africans and show that the observed patterns are not likely to reflect changes in natural selection. Non-African populations have experienced size reductions in the time since their split from West Africans, leading to the hypothesis that natural selection to remove weakly deleterious mutations has been less effective in the history of non-Africans. To test this hypothesis, we measured the per-genome accumulation of nonsynonymous substitutions across diverse pairs of populations. We find no evidence for a higher load of deleterious mutations in non-Africans. However, we detect significant differences among more divergent populations, as archaic Denisovans have accumulated nonsynonymous mutations faster than either modern humans or Neanderthals. To reconcile these findings with patterns that have been interpreted as evidence of the less effective removal of deleterious mutations in non-Africans than in West Africans, we use simulations to show that the observed patterns are not likely to reflect changes in the effectiveness of selection after the populations split but are instead likely to be driven by other population genetic factors.