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Trends Genet. 2014 Dec;30(12):540-6. doi: 10.1016/j.tig.2014.09.010. Epub 2014 Nov 19.

Thinking too positive? Revisiting current methods of population genetic selection inference.

Author information

1
School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics (SIB), 1015 Lausanne, Switzerland. Electronic address: claudia.bank@epfl.ch.
2
School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics (SIB), 1015 Lausanne, Switzerland.
3
School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics (SIB), 1015 Lausanne, Switzerland; Department of Biology and Biochemistry, University of Fribourg, 1700 Fribourg, Switzerland.

Abstract

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.

KEYWORDS:

background selection; computational biology; evolution; natural selection; population genetic inference

PMID:
25438719
DOI:
10.1016/j.tig.2014.09.010
[Indexed for MEDLINE]

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