Sequencing-era methods for identifying signatures of selection in the genome
Sequencing-era methods for identifying signatures of selection in the genome
Insights into genetic loci which are under selection and their functional roles contribute to increased understanding of the patterns of phenotypic variation we observe today. The availability of whole genome sequence data, for humans and other species, provides opportunities to investigate adaptation and evolution at unprecedented resolution. Many analytical methods have been developed to interrogate these large datasets and characterise signatures of selection in the genome. We review here recently developed methods and consider the impact of increased computing power and data availability on the detection of selection signatures. Consideration of demography, recombination and other confounding factors is important, and use of a range of methods in combination is a powerful route to resolving different forms of selection in genome sequence data. Overall, a substantial improvement in methods for application to whole genome sequencing is evident, although further work is required to develop robust and computationally efficient approaches which may increase reproducibility across studies.
natural selection, Machine Learning, selective sweep, genome sequence, recombination
1997-2008
Horscroft, Clare
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Ennis, Sarah
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Pengelly, Reuben J.
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Sluckin, T.J.
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Collins, Andrew
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November 2019
Horscroft, Clare
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Ennis, Sarah
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Pengelly, Reuben J.
af97c0c1-b568-415c-9f59-1823b65be76d
Sluckin, T.J.
8dbb6b08-7034-4ae2-aa65-6b80072202f6
Collins, Andrew
7daa83eb-0b21-43b2-af1a-e38fb36e2a64
Horscroft, Clare, Ennis, Sarah, Pengelly, Reuben J., Sluckin, T.J. and Collins, Andrew
(2019)
Sequencing-era methods for identifying signatures of selection in the genome.
Briefings in Bioinformatics, 20 (6), .
(doi:10.1093/bib/bby064).
Abstract
Insights into genetic loci which are under selection and their functional roles contribute to increased understanding of the patterns of phenotypic variation we observe today. The availability of whole genome sequence data, for humans and other species, provides opportunities to investigate adaptation and evolution at unprecedented resolution. Many analytical methods have been developed to interrogate these large datasets and characterise signatures of selection in the genome. We review here recently developed methods and consider the impact of increased computing power and data availability on the detection of selection signatures. Consideration of demography, recombination and other confounding factors is important, and use of a range of methods in combination is a powerful route to resolving different forms of selection in genome sequence data. Overall, a substantial improvement in methods for application to whole genome sequencing is evident, although further work is required to develop robust and computationally efficient approaches which may increase reproducibility across studies.
Text
Sequencing-era methods for identifying signatures of selection in the genome
- Accepted Manuscript
More information
Accepted/In Press date: 25 June 2018
e-pub ahead of print date: 24 July 2018
Published date: November 2019
Keywords:
natural selection, Machine Learning, selective sweep, genome sequence, recombination
Identifiers
Local EPrints ID: 422836
URI: http://eprints.soton.ac.uk/id/eprint/422836
ISSN: 1467-5463
PURE UUID: 18194131-999c-4a3a-9a06-2161815b25d0
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Date deposited: 06 Aug 2018 16:30
Last modified: 16 Mar 2024 06:52
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Author:
Clare Horscroft
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