Journal
TRENDS IN GENETICS
Volume 30, Issue 12, Pages 540-546Publisher
ELSEVIER SCIENCE LONDON
DOI: 10.1016/j.tig.2014.09.010
Keywords
natural selection; background selection; population genetic inference; evolution; computational biology
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Funding
- Swiss National Science Foundation
- European Research Council
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In the age of next-generation sequencing, the availability of increasing amounts and improved quality of data at decreasing cost ought to allow for a better understanding of how natural selection is shaping the genome than ever before. However, alternative forces, such as demography and background selection (BGS), obscure the footprints of positive selection that we would like to identify. In this review, we illustrate recent developments in this area, and outline a roadmap for improved selection inference. We argue (i) that the development and obligatory use of advanced simulation tools is necessary for improved identification of selected loci, (ii) that genomic information from multiple time points will enhance the power of inference, and (iii) that results from experimental evolution should be utilized to better inform population genomic studies.
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