Genomic inference using diffusion models and the allele frequency spectrum

Evolutionary, biological, and demographic processes together shape observed variation in populations. Understanding how these processes influence variation allows us to infer past demography and the nature of selection in populations. Forward in time models such as the diffusion approximation provide a powerful tool for performing inference based on the distribution of allele frequencies. Here, we discuss recent computational developments and their application to reconstructing human demographic history. Using whole-genome sequence data for 797 French Canadian individuals, we assess the neutrality of synonymous variants and show that selection can bias inferred demography, mutation rates, and distributions of fitness effects. We argue that the simple evolutionary models investigated by Kimura and Ohta still provide important insight into modern genetic research.