Not Just $N_e$ $N_e$-more: New Applications for SMC from Ecology to Phylogenies

TL;DR

This paper reviews recent advancements in sequentially Markovian coalescent (SMC) methods, highlighting their expanded applications in ecology and phylogenetics, and discusses their assumptions, benefits, and limitations.

Contribution

It provides a comprehensive overview of new SMC extensions, illustrating their use in diverse biological contexts beyond traditional demographic inference.

Findings

Inclusion of gene flow and structural variation improves SMC models.

Integration with ecological variables enhances biological inference.

Recent computational advances enable analysis of complex genomic data.

Abstract

Genomes contain the mutational footprint of an organism's evolutionary history, shaped by diverse forces including ecological factors, selective pressures, and life history traits. The sequentially Markovian coalescent (SMC) is a versatile and tractable model for the genetic genealogy of a sample of genomes, which captures this shared history. Methods that utilize the SMC, such as PSMC and MSMC, have been widely used in evolution and ecology to infer demographic histories. However, these methods ignore common biological features, such as gene flow events and structural variation. Recently, there have been several advancements that widen the applicability of SMC-based methods: inclusion of an isolation with migration model, integration with the multi-species coalescent, incorporation of ecological variables (such as selfing and dormancy), inference of dispersal rates, and many computational advances in applying these models to data. We give an overview of the SMC model and its various recent extensions, discuss examples of biological discoveries through SMC-based inference, and comment on the assumptions, benefits and drawbacks of various methods.