Inferring the history of population size change from genome-wide SNP data.

Dense, genome-wide single-nucleotide polymorphism (SNP) data can be used to reconstruct the demographic history of human populations. However, demographic inferences from such data are complicated by recombination and ascertainment bias. We introduce two new statistics, allele frequency-identity by descent (AF-IBD) and allele frequency-identity by state (AF-IBS), that make use of linkage disequilibrium information and show defined relationships to the time of coalescence. These statistics, when conditioned on the derived allele frequency, are able to infer complex population size changes. Moreover, the AF-IBS statistic, which is based on genome-wide SNP data, is robust to varying ascertainment conditions. We constructed an efficient approximate Bayesian computation (ABC) pipeline based on AF-IBD and AF-IBS that can accurately estimate demographic parameters, even for fairly complex models. Finally, we applied this ABC approach to genome-wide SNP data and inferred the demographic histories of two human populations, Yoruba and French. Our results suggest a rather stable ancestral population size with a mild recent expansion for Yoruba, whereas the French seemingly experienced a long-lasting severe bottleneck followed by a drastic population growth. This approach should prove useful for new insights into populations, especially those with complex demographic histories.