Efficient inference methods for large-scale population genomics

Yun Song
University of California, Berkeley (UC Berkeley)

Many applications in population genetics utilize sampling distributions, which describe the probability of observing a sample of DNA sequences randomly drawn from a population. Unfortunately, exact sampling distributions are unknown for most models of evolution, particularly when natural selection and recombination are taken into account. In this talk, I will summarize our recent progress on this challenging problem and describe how mathematically sound approximations can be made to obtain scalable inference methods for population genomic analyses.

Back to Workshop IV: Coancestry, Association, and Population Genomics