What paths do advantageous alleles take during short‐term evolutionary change?

Combining experimental evolution with whole‐genome resequencing is a promising new strategy for investigating the dynamics of evolutionary change. Published studies that have resequenced laboratory‐selected populations of sexual organisms have typically focused on populations sampled at the end of an evolution experiment. These studies have attempted to associate particular alleles with phenotypic change and attempted to distinguish between different theoretical models of adaptation. However, neither the population used to initiate the experiment nor multiple time points sampled during the evolutionary trajectory are generally available for examination. In this issue of Molecular Ecology, Orozco‐terWengel et al. (2012) take a significant step forward by estimating genome‐wide allele frequencies at the start, 15 generations into and at the end of a 37‐generation Drosophila experimental evolution study. The authors identify regions of the genome that have responded to laboratory selection and describe the temporal dynamics of allele frequency change. They identify two common trajectories for putatively adaptive alleles: alleles either gradually increase in frequency throughout the entire 37 generations or alleles plateau at a new frequency by generation 15. The identification of complex trajectories of alleles under selection contributes to a growing body of literature suggesting that simple models of adaptation, whereby beneficial alleles arise and increase in frequency unimpeded until they become fixed, may not adequately describe short‐term response to selection.     

Predicting epistasis: an experimental test of metabolic control theory with bacterial transcription and translation

Epistatic interactions between mutations are thought to play a crucial role in a number of evolutionary processes, including adaptation and sex. Evidence for epistasis is abundant, but tests of general theoretical models that can predict epistasis are lacking. In this study, I test the ability of metabolic control theory to predict epistasis using a novel experimental approach that combines phenotypic and genetic perturbations of enzymes involved in gene expression and protein synthesis in the bacterium Pseudomonas aeruginosa. These experiments provide experimental support for two key predictions of metabolic control theory: (i) epistasis between genes involved in the same pathway is antagonistic; (ii) epistasis becomes increasingly antagonistic as mutational severity increases. Metabolic control theory is a general theory that applies to any set of genes that are involved in the same linear processing chain, not just metabolic pathways, and I argue that this theory is likely to have important implications for predicting epistasis between functionally coupled genes, such as those involved in antibiotic resistance. Finally, this study highlights the fact that phenotypic manipulations of gene activity provide a powerful method for studying epistasis that complements existing genetic methods.     

Genetic diversity of small populations: Not always “doom and gloom”?

Is a key theory of evolutionary and conservation biology—that loss of genetic diversity can be predicted from population size—on shaky ground? In the face of increasing human‐induced species depletion and habitat fragmentation, this question and the study of genetic diversity in small populations are paramount to understanding the limits of species’ responses to environmental change and to providing remedies to endangered species conservation. Few empirical studies have investigated to what degree some small populations might be buffered against losses of genetic diversity. Even fewer studies have experimentally tested the potential underlying mechanisms. The study of Schou, Loeschcke, Bechsgaard, Schlotterer, and Kristensen ( 2017 ) in this issue of Molecular Ecology is elegant in combining classic common garden experimentation with population genomics on an iconic experimental model species (Drosophila melanogaster). The authors reveal a slower rate of loss of genetic diversity in small populations under varying thermal regimes than theoretically expected and hence an unexpected retention of genetic diversity. They are further able to hone in on a plausible mechanism: associative overdominance, wherein homozygosity of deleterious recessive alleles is especially disfavoured in genomic regions of low recombination. These results contribute to a budding literature on the varying mechanisms underlying genetic diversity in small populations and encourage further such research towards the effective management and conservation of fragmented or endangered populations.     

How to infer reliable diploid genotypes from NGS or traditional sequence data: from basic probability to experimental optimization

The use of diploid sequence markers is still challenging despite the good quality of the information they provide. There is a common problem to all sequencing approaches [traditional cloning and sequencing of PCR amplicons as well as next-generation sequencing (NGS)]: when no variation is found within the sequences from a given individual, homozygozity can never be asserted with certainty. As a consequence, sequence data from diploid markers are mostly analysed at the population (not the individual level) particularly in animal studies. This study aims at contributing to solve this. Using the Bayes theorem and the binomial law, useful results are derived, among which: (i) the number of sequence reads per individual (or sequencing depth) which is required to ensure, at a given probability threshold, that some heterozygotes are not considered erroneously as homozygotes, as a function of the observed heterozygozity (H(o) ) of the locus in the population; (ii) a way of estimating H(o) from low coverage NGS data; (iii) a way of testing the null hypothesis that a genetic marker corresponds to a single and diploid locus, in the absence of data from controlled crosses; (iv) strategies for characterizing sequence genotypes in populations minimizing the average number of sequence reads per individual; (v) a rationale to decide which are the variations that one needs to consider along the sequence, as a function of the sequencing depth affordable, the level of polymorphism desired and the risk of sequencing error. For traditional sequencing technology, optimal strategies appear surprisingly different from the usual empirical ones. The average number of sequence reads required to obtain 99% of fully determined genotypes never exceeds six, this value corresponding to the worst situation when H(o) equals 0.6. This threshold value of H(o) is strikingly stable when the tolerated proportion of nonfully resolved genotypes varies in a reasonable range. These results do not rely on the Hardy-Weinberg equilibrium assumption or on diallelism of nucleotidic sites.