Calvin Lab Auditorium
We present a flexible and robust simulation-based framework to infer demographic parameters from the site frequency spectrum (SFS) computed on large genomic data sets. This composite-likelihood approach allows the study of arbitrarily complex evolutionary models. We compare our approach to dadi, the current reference in the field, and show that our approach has better convergence properties for complex models. We present an application of our methodology to non-coding genomic SNP data from four human populations and further show the versatility of our framework by extending it to the inference of demographic parameters from SNP chips with known ascertainment, such as that recently released by Affymetrix to study human origins. We discuss potential extensions of our approach, which appears generally well suited to study complex scenarios from large genomic data sets.
Joint work with Vitor C. Sousa.