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Faster inference of complex demographic models from large allele frequency spectra.

ABSTRACT: We present momi3, a new method for inferring complex demographic models using genetic variation data sampled from many populations. momi3 features many improvements over its predecessor momi2 (Kamm, Terhorst, Durbin, et al., 2020), including support for continuous migration, just-in-time compilation, and execution on GPUs; a standardized interface for specifying demographic models; and a novel importance sampling strategy that enables it to efficiently analyze data from a large number of samples. Together, these improvements lead to speedups of as much as 1000× over existing state-of-the-art methods such as ∂a∂i, moments, and momi2. We illustrate the usefulness of our method by revisiting a model of archaic admixture using a large, recent dataset containing hundreds of human genomes from many populations.

SUBMITTER: Dilber E

PROVIDER: S-EPMC10996679 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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Faster inference of complex demographic models from large allele frequency spectra.

Dilber Enes E Terhorst Jonathan J

bioRxiv : the preprint server for biology 20240329

We present momi3, a new method for inferring complex demographic models using genetic variation data sampled from many populations. momi3 features many improvements over its predecessor momi2 (Kamm, Terhorst, Durbin, et al., 2020), including support for continuous migration, just-in-time compilation, and execution on GPUs; a standardized interface for specifying demographic models; and a novel importance sampling strategy that enables it to efficiently analyze data from a large number of samples ...[more]

PMID: 38586047

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