A Population Genetic Hidden Markov Model for Detecting Genomic Regions Under Selection

Overview

Journal Mol Biol Evol

Publisher Oxford University Press

Specialty Biology

Date 2010 Feb 27

PMID 20185453

Citations 9

Authors

Andrew D Kern

David Haussler

Affiliations

Soon will be listed here.

Abstract

Recently, hidden Markov models have been applied to numerous problems in genomics. Here, we introduce an explicit population genetics hidden Markov model (popGenHMM) that uses single nucleotide polymorphism (SNP) frequency data to identify genomic regions that have experienced recent selection. Our popGenHMM assumes that SNP frequencies are emitted independently following diffusion approximation expectations but that neighboring SNP frequencies are partially correlated by selective state. We give results from the training and application of our popGenHMM to a set of early release data from the Drosophila Population Genomics Project (dpgp.org) that consists of approximately 7.8 Mb of resequencing from 32 North American Drosophila melanogaster lines. These results demonstrate the potential utility of our model, making predictions based on the site frequency spectrum (SFS) for regions of the genome that represent selected elements.

Citing Articles

Identification of natural selection in genomic data with deep convolutional neural network.

Fadja A, Riguzzi F, Bertorelle G, Trucchi E BioData Min. 2021; 14(1):51.

PMID: 34863217 PMC: 8642854. DOI: 10.1186/s13040-021-00280-9.

Detecting Selection from Linked Sites Using an -Model.

Galimberti M, Leuenberger C, Wolf B, Szilagyi S, Foll M, Wegmann D Genetics. 2020; 216(4):1205-1215.

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The Unreasonable Effectiveness of Convolutional Neural Networks in Population Genetic Inference.

Flagel L, Brandvain Y, Schrider D Mol Biol Evol. 2018; 36(2):220-238.

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Supervised Machine Learning for Population Genetics: A New Paradigm.

Schrider D, Kern A Trends Genet. 2018; 34(4):301-312.

PMID: 29331490 PMC: 5905713. DOI: 10.1016/j.tig.2017.12.005.

zipHMMlib: a highly optimised HMM library exploiting repetitions in the input to speed up the forward algorithm.

Sand A, Kristiansen M, Pedersen C, Mailund T BMC Bioinformatics. 2013; 14:339.

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