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

被引:89
作者
Bank, Claudia [1 ,2 ]
Ewing, Gregory B. [1 ,2 ]
Ferrer-Admettla, Anna [1 ,2 ,3 ]
Foll, Matthieu [1 ,2 ]
Jensen, Jeffrey D. [1 ,2 ]
机构
[1] Ecole Polytech Fed Lausanne, Sch Life Sci, CH-1015 Lausanne, Switzerland
[2] SIB, CH-1015 Lausanne, Switzerland
[3] Univ Fribourg, Dept Biol & Biochem, CH-1700 Fribourg, Switzerland
基金
欧洲研究理事会; 瑞士国家科学基金会;
关键词
natural selection; background selection; population genetic inference; evolution; computational biology; BACKGROUND SELECTION; DELETERIOUS MUTATIONS; TEMPORAL-CHANGES; MONTE-CARLO; LIKELIHOOD; RECOMBINATION; FREQUENCY; EVOLUTION; SWEEPS; POLYMORPHISM;
D O I
10.1016/j.tig.2014.09.010
中图分类号
Q3 [遗传学];
学科分类号
071007 [遗传学];
摘要
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.
引用
收藏
页码:540 / 546
页数:7
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