Methods for Detecting Adaptive Molecular Evolution

Associate Professor of Animal Health A primary aim in evolutionary biology is to understand the processes and mechanisms shaping the patterns of genetic variation across populations and species. Investigating adaptive variation in the genome to identify targets of natural selection is fundamental to identifying the basis for evolution and, consequently, for predicting the potential for a population or species to adapt to environmental change. Therefore methods aimed at detecting adaptive molecular evolution in genes or genetic loci are a subject of great interest to evolutionary biologists and ecologists. Methods for examining adaptation at the molecular level have been used to identify genes under selection to elucidate their biological function. These methods often apply population genetic theory and data, considering both an organism’s demographic history and natural selection—including negative, positive, and balancing selection—as forces shaping the genetic variation across populations and species. Most approaches focus on detecting signatures of positive selection—the primary mechanism of adaptation—by comparing observed data to expectations under a neutral evolutionary model. There have been great advancements in methods to detect adaptive molecular evolution over the past several decades. These advances have accelerated with the development of next generation sequencing technologies, which have led to the generation of massive amounts of genomic data. The temporal scale of these comparative genetic methods ranges from within species (microevolutionary) to between species (macroevolutionary). They may also target single genes, particular genetic regions, or whole genomes, and make use of polymorphism data and/or divergence data. This bibliography identifies and describes key literature and texts on methods for adaptive evolution, organized by scale (micro- versus macroevolutionary methods), as well as more recent literature for detecting selection using more advanced statistical and computational approaches and large-scale genomic datasets. In addition, relevant foundational texts on biological concepts (e.g., selection at linked sites) and the development of the null model are included below. Literature on experimental evolution-based methods for investigating selection are not covered here. In addition, the separate Oxford Bibliographies in Evolutionary Biology articles “Natural Selection” by Mark McPeek, “Adaptation” by Jonathan Losos, and “Detecting Natural Selection in the Genome” by Joanna Kelley may also be referred to for further background information on this topic.