The distribution of fitness effects (DFE) of new mutations is a fundamental entity in genetics that has implications ranging from the genetic basis of complex disease to the stability of the molecular clock. It has been studied by two different approaches: mutation accumulation and mutagenesis experiments, and the analysis of DNA sequence data. The proportion of mutations that are advantageous, effectively neutral and deleterious varies between species, and the DFE differs between coding and non-coding DNA. Despite these differences between species and genomic regions, some general principles have emerged: advantageous mutations are rare, and those that are strongly selected are exponentially distributed; and the DFE of deleterious mutations is complex and multi-modal.

The prevalence of adaptive evolution relative to genetic drift is a central problem in molecular evolution. Methods to estimate the fraction of adaptive nucleotide substitutions (alpha) have been developed, based on the McDonald-Kreitman test, that contrast polymorphism and divergence between selectively and neutrally evolving sites. However, these methods are expected to give downwardly biased estimates of alpha if there are slightly deleterious mutations, because these inflate polymorphism relative to divergence. Here, we estimate alpha by simultaneously estimating the distribution of fitness effects of new mutations at selected sites from the site frequency spectrum and the number of adaptive substitutions. We test the method using simulations. If data meet the assumptions of the analysis model, estimates of alpha show little bias, even when there is little or no recombination. However, population size differences between the divergence and polymorphism phases may cause alpha to be over or underestimated by a predictable factor that depends on the magnitude of the population size change and the shape of the distribution of effects of deleterious mutations. We analyze several data sets of protein-coding genes and noncoding regions from hominids and Drosophila. In Drosophila genes, we estimate that approximately 50% of amino acid substitutions and approximately 20% of substitutions in introns are adaptive. In protein-coding and noncoding data sets of humans, comparison to macaque sequences reveals little evidence for adaptive substitutions. However, the true frequency of adaptive substitutions in human-coding DNA could be as high as 40%, because estimates based on current polymorphism may be strongly downwardly biased by a decrease in the effective population size along the human lineage.

For over 30 years a central question in molecular evolution has been whether natural selection plays a substantial role in evolution at the DNA sequence level. Evidence has accumulated over the last decade that adaptive evolution does occur at the protein level, but it has remained unclear how prevalent adaptive evolution is. Here we present a simple method by which the number of adaptive substitutions can be estimated and apply it to data from Drosophila simulans and D. yakuba. We estimate that 45% of all amino-acid substitutions have been fixed by natural selection, and that on average one adaptive substitution occurs every 45 years in these species.

The distribution of fitness effects of new mutations (DFE) is important for addressing several questions in genetics, including the nature of quantitative variation and the evolutionary fate of small populations. Properties of the DFE can be inferred by comparing the distributions of the frequencies of segregating nucleotide polymorphisms at selected and neutral sites in a population sample, but demographic changes alter the spectrum of allele frequencies at both neutral and selected sites, so can bias estimates of the DFE if not accounted for. We have developed a maximum-likelihood approach, based on the expected allelefrequency distribution generated by transition matrix methods, to estimate parameters of the DFE while simultaneously estimating parameters of a demographic model that allows a population size change at some time in the past. We tested the method using simulations and found that it accurately recovers simulated parameter values, even if the simulated demography differs substantially from that assumed in our analysis. We use our method to estimate parameters of the DFE for amino acid-changing mutations in humans and Drosophila melanogaster. For a model of unconditionally deleterious mutations, with effects sampled from a gamma distribution, the mean estimate for the distribution shape parameter is $0.2 for human populations, which implies that the DFE is strongly leptokurtic. For Drosophila populations, we estimate that the shape parameter is $0.35. Differences in the shape of the distribution and the mean selection coefficient between humans and Drosophila result in significantly more strongly deleterious mutations in Drosophila than in humans, and, conversely, nearly neutral mutations are significantly less frequent.

It has been known for many years that the mutation rate varies across the genome. However, only with the advent of large genomic data sets is the full extent of this variation becoming apparent. The mutation rate varies over many different scales, from adjacent sites to whole chromosomes, with the strongest variation seen at the smallest scales. Some of these patterns have clear mechanistic bases, but much of the rate variation remains unexplained, and some of it is deeply perplexing. Variation in the mutation rate has important implications in evolutionary biology and underexplored implications for our understanding of hereditary disease and cancer.

The distribution of fitness effects of new mutations is a fundamental parameter in genetics. Here we present a new method by which the distribution can be estimated. The method is fairly robust to changes in population size and admixture, and it can be corrected for any residual effects if a model of the demography is available. We apply the method to extensively sampled single-nucleotide polymorphism data from humans and estimate the distribution of fitness effects for amino acid changing mutations. We show that a gamma distribution with a shape parameter of 0.23 provides a good fit to the data and we estimate that .50% of mutations are likely to have mild effects, such that they reduce fitness by between one one-thousandth and one-tenth. We also infer that ,15% of new mutations are likely to have strongly deleterious effects. We estimate that on average a nonsynonymous mutation reduces fitness by a few percent and that the average strength of selection acting against a nonsynonymous polymorphism is $9 3 10 À5. We argue that the relaxation of natural selection due to modern medicine and reduced variance in family size is not likely to lead to a rapid decline in genetic quality, but that it will be very difficult to locate most of the genes involved in complex genetic diseases.

The genomic GC-content of bacteria varies dramatically, from less than 20% to more than 70%. This variation is generally ascribed to differences in the pattern of mutation between bacteria. Here we test this hypothesis by examining patterns of synonymous polymorphism using datasets from 149 bacterial species. We find a large excess of synonymous GC→AT mutations over AT→GC mutations segregating in all but the most AT-rich bacteria, across a broad range of phylogenetically diverse species. We show that the excess of GC→AT mutations is inconsistent with mutation bias, since it would imply that most GC-rich bacteria are declining in GC-content; such a pattern would be unsustainable. We also show that the patterns are probably not due to translational selection or biased gene conversion, because optimal codons tend to be AT-rich, and the excess of GC→AT SNPs is observed in datasets with no evidence of recombination. We therefore conclude that there is selection to increase synonymous GC-content in many species. Since synonymous GC-content is highly correlated to genomic GC-content, we further conclude that there is selection on genomic base composition in many bacteria.

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