期刊
LOBACHEVSKII JOURNAL OF MATHEMATICS
卷 43, 期 8, 页码 2092-2098出版社
MAIK NAUKA/INTERPERIODICA/SPRINGER
DOI: 10.1134/S1995080222110075
关键词
deep learning; population genomics; LCA; effective population size; demography; genome; chromosome
类别
资金
- Russian Science Foundation [20-71-00143]
This work proposes a new deep learning framework for inferring local last common ancestor (LCA) time at the full genome scale. The accuracy of the method in both local LCA time and LCA time distribution is demonstrated, which translates into effective population size trajectory.
Genomic data is a rich source of information about population history. In particular, for actively recombining species the time to the last common ancestor (LCA) between two chromosomes might be different in different chromosome loci. Estimating local LCA time is important for many problems: it can be used to infer genes under selection, or to infer effective population size changes. The current state-of-the art method PSMC to infer local LCA time and effective population size is based on a Hidden Markov Model. In this work we propose a new deep learning framework for local LCA time inference at the full genome scale. We demonstrate that our method is accurate in both local LCA time and, as a consequence, at the LCA time distribution which in turn translates into effective population size trajectory. In future our approach can be generalised for complex population scenarios.
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