Conditional coalescent and its applications in population genomics

IMA Data Science Seminar

Wai-Tong (Louis) Fan (Indiana University)

Abstract

Understanding genetic diversity in genomic data and the associated evolutionary processes is an important goal in population genomics. However, the complexity of genomic data poses significant theoretical and computational challenges. Coalescent theory is a powerful tool in population genetics that has been widely used to study genetic data, but the assumptions underlying the theory must be understood with care. For example, understanding the relationships between a population’s forward-time dynamics and its backward-time genealogies is crucial to determining what is robust against model assumptions and what is not.

In this talk, I will first focus on recent applications of conditional coalescent theory to large human genomics datasets. Based on the conditional coalescent theory, we developed a sampling theory that not only explains dramatic differences in the site frequency spectra (SFS) across human genomes that standard theories cannot explain, but also enables estimation of the mutation rates, the numbers of latent recurrent mutations and the ages and the sizes of these mutations. Our method is robust against, or insensitive to, details of the population dynamics and weak selection. If time permits, I will also highlight the key role and challenges of understanding models with spatial structure and their potential applications to virus co-infection spread. Joint work with John Wakeley, Evan Koch and Shamil Sunyaev.