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Previous studies of the evolution of genes expressed at different life-cycle stages of Drosophila melanogaster have not been able to disentangle adaptive from nonadaptive substitutions when using nonsynonymous sites. Here, we overcome this limitation by combining whole-genome polymorphism data from D. melanogaster and divergence data between D. melanogaster and Drosophila yakuba. For the set of genes expressed at different life-cycle stages of D. melanogaster, as reported in modENCODE, we estimate the ratio of substitutions relative to polymorphism between nonsynonymous and synonymous sites (a) and then a is discomposed into the ratio of adaptive (xa) and nonadaptive (xna) substitutions to synonymous substitutions. We find that the genes expressed in mid- and late-embryonic development are the most conserved, whereas those expressed in early development and postembryonic stages are the least conserved. Importantly, we found that low conservation in early development is due to high rates of nonadaptive substitutions (high xna), whereas in postembryonic stages it is due, instead, to high rates of adaptive substitutions (high xa). By using estimates of different genomic features (codon bias, average intron length, exon number, recombination rate, among others), we also find that genes expressed in mid- and late-embryonic development show the most complex architecture: they are larger, have more exons, more transcripts, and longer introns. In addition, these genes are broadly expressed among all stages. We suggest that all these genomic features are related to the conservation of mid- and late-embryonic development. Globally, our study supports the hourglass pattern of conservation and adaptation over the life-cycle.

It is estimated that a large proportion of amino acid substitutions in Drosophila have been fixed by natural selection, and as organisms are faced with an ever-changing array of pathogens and parasites to which they must adapt, we have investigated the role of parasite-mediated selection as a likely cause. To quantify the effect, and to identify which genes and pathways are most likely to be involved in the host–parasite arms race, we have re-sequenced population samples of 136 immunity and 287 position-matched non-immunity genes in two species of Drosophila. Using these data, and a new extension of the McDonald-Kreitman approach, we estimate that natural selection fixes advantageous amino acid changes in immunity genes at nearly double the rate of other genes. We find the rate of adaptive evolution in immunity genes is also more variable than other genes, with a small subset of immune genes evolving under intense selection. These genes, which are likely to represent hotspots of host–parasite coevolution, tend to share similar functions or belong to the same pathways, such as the antiviral RNAi pathway and the IMD signalling pathway. These patterns appear to be general features of immune system evolution in both species, as rates of adaptive evolution are correlated between the D. melanogaster and D. simulans lineages. In summary, our data provide quantitative estimates of the elevated rate of adaptive evolution in immune system genes relative to the rest of the genome, and they suggest that adaptation to parasites is an important force driving molecular evolution., Author Summary All organisms are attacked by an ever-changing array of pathogens and parasites, and it is widely supposed that the ensuing host–parasite “arms race” must drive extensive adaptive evolution in genes of the immune system. Here we have taken advantage of new sequencing technologies and analytical approaches to quantify the amount of adaptation that is occurring in immunity genes relative to the rest of the genome. We sampled two species of fruit fly (D. melanogaster and D. simulans) from eight different populations around the world, and sequenced 136 immunity and 287 non-immunity genes from these samples. Based on the differences in the sequences between the two species, and the genetic diversity within each species, we have estimated that natural selection drives twice as much change in immune-related proteins as in proteins with no immune function. Interestingly, the rate of adaptation is also more variable among immunity genes than among other genes in the genome, with a small subset of immunity genes evolving under intense natural selection. We suggest that these genes may represent hotspots of host–parasite coevolution within the genome.

The recurrent fixation of newly arising, beneficial mutations in a species reduces levels of linked neutral variability. Models positing frequent weakly beneficial substitutions or, alternatively, rare, strongly selected substitutions predict similar average effects on linked neutral variability, if the product of the rate and strength of selection is held constant. We propose an approximate Bayesian (ABC) polymorphism-based estimator that can be used to distinguish between these models, and apply it to multi-locus data from Drosophila melanogaster. We investigate the extent to which inference about the strength of selection is sensitive to assumptions about the underlying distributions of the rates of substitution and recombination, the strength of selection, heterogeneity in mutation rate, as well as the population's demographic history. We show that assuming fixed values of selection parameters in estimation leads to overestimates of the strength of selection and underestimates of the rate. We estimate parameters for an African population of D. melanogaster (ŝ∼2E−03, ) and compare these to previous estimates. Finally, we show that surveying larger genomic regions is expected to lend much more discriminatory power to the approach. It will thus be of great interest to apply this method to emerging whole-genome polymorphism data sets in many taxa., Author Summary Understanding the process of adaptive evolution requires quantifying the extent to which beneficial mutations contribute to differences between species. However, fundamental parameters of adaptation, such as the rate and strength of beneficial mutations, are poorly understood and have historically been difficult to estimate from data. In particular, distinguishing a high rate of weakly selected substitutions from a low rate of strongly selected substitutions has been problematic. Here, we introduce a new method to estimate the parameters of adaptive evolution from multi-locus population genetic data. We conduct simulations to show that this method is able to discriminate the rare/strong model from the frequent/weak model. Applying this method to an African population sample of Drosophila melanogaster, we estimate selection parameters and find that recurrent adaptive evolution has reduced genome variability by ∼50% on average. The availability of genome-scale population genetic data will lend considerable discriminatory power to the approach. Thus, this new approach represents an important step towards characterizing the nature of adaptive evolution in natural populations.