Literature DB >> 30053138

Sequencing era methods for identifying signatures of selection in the genome.

Clare Horscroft1,2, Sarah Ennis1,2, Reuben J Pengelly1,2, Timothy J Sluckin2,3, Andrew Collins1,2.   

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 data sets and characterize 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.
© The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  genome sequencing; machine learning; natural selection; recombination; selective sweep

Year:  2019        PMID: 30053138     DOI: 10.1093/bib/bby064

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


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