Project description:The McDonald and Kreitman test (MKT) is one of the most powerful and widely used methods to detect and quantify recurrent natural selection using DNA sequence data. Here we present iMKT (acronym for integrative McDonald and Kreitman test), a novel web-based service performing four distinct MKT types. It allows the detection and estimation of four different selection regimes -adaptive, neutral, strongly deleterious and weakly deleterious- acting on any genomic sequence. iMKT can analyze both user's own population genomic data and pre-loaded Drosophila melanogaster and human sequences of protein-coding genes obtained from the largest population genomic datasets to date. Advanced options in the website allow testing complex hypotheses such as the application example showed here: do genes located in high recombination regions undergo higher rates of adaptation? We aim that iMKT will become a reference site tool for the study of evolutionary adaptation in massive population genomics datasets, especially in Drosophila and humans. iMKT is a free resource online at https://imkt.uab.cat.
Project description:Population genomic studies have shown that genetic draft and background selection can profoundly affect the genome-wide patterns of molecular variation. We performed forward simulations under realistic gene-structure and selection scenarios to investigate whether such linkage effects impinge on the ability of the McDonald-Kreitman (MK) test to infer the rate of positive selection (α) from polymorphism and divergence data. We find that in the presence of slightly deleterious mutations, MK estimates of α severely underestimate the true rate of adaptation even if all polymorphisms with population frequencies under 50% are excluded. Furthermore, already under intermediate rates of adaptation, genetic draft substantially distorts the site frequency spectra at neutral and functional sites from the expectations under mutation-selection-drift balance. MK-type approaches that first infer demography from synonymous sites and then use the inferred demography to correct the estimation of α obtain almost the correct α in our simulations. However, these approaches typically infer a severe past population expansion although there was no such expansion in the simulations, casting doubt on the accuracy of methods that infer demography from synonymous polymorphism data. We propose a simple asymptotic extension of the MK test that yields accurate estimates of α in our simulations and should provide a fruitful direction for future studies.