Robust inference of population size histories from genomic sequencing data.

ABSTRACT: Unraveling the complex demographic histories of natural populations is a central problem in population genetics. Understanding past demographic events is of general anthropological interest, but is also an important step in establishing accurate null models when identifying adaptive or disease-associated genetic variation. An important class of tools for inferring past population size changes from genomic sequence data are Coalescent Hidden Markov Models (CHMMs). These models make efficient use of the linkage information in population genomic datasets by using the local genealogies relating sampled individuals as latent states that evolve along the chromosome in an HMM framework. Extending these models to large sample sizes is challenging, since the number of possible latent states increas

SUBMITTER: Upadhya G 

PROVIDER: S-EPMC9518926 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature