Inference of Population History using Coalescent HMMs: Review and Outlook

TL;DR

This paper reviews coalescent hidden Markov models used for inferring population history from genomic data, discussing recent advances, practical advice, potential pitfalls, and future research directions.

Contribution

It provides a comprehensive review of coalescent HMMs, highlighting recent methodological advances and offering guidance for practitioners in population genetics.

Findings

Recent advances improve modeling flexibility and scalability.

Coalescent HMMs effectively utilize linkage disequilibrium.

The review identifies future research directions.

Abstract

Studying how diverse human populations are related is of historical and anthropological interest, in addition to providing a realistic null model for testing for signatures of natural selection or disease associations. Furthermore, understanding the demographic histories of other species is playing an increasingly important role in conservation genetics. A number of statistical methods have been developed to infer population demographic histories using whole-genome sequence data, with recent advances focusing on allowing for more flexible modeling choices, scaling to larger data sets, and increasing statistical power. Here we review coalescent hidden Markov models, a powerful class of population genetic inference methods that can effectively utilize linkage disequilibrium information. We highlight recent advances, give advice for practitioners, point out potential pitfalls, and present possible future research directions.