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Quantitative Biology

, Volume 4, Issue 3, pp 207–216 | Cite as

Revisiting the false positive rate in detecting recent positive selection

  • Jinggong Xiang-Yu
  • Zongfeng Yang
  • Kun Tang
  • Haipeng LiEmail author
Review

Abstract

There is increasing interest in studying the molecular mechanisms of recent adaptations caused by positive selection in the genomics era. Such endeavors to detect recent positive selection, however, have been severely handicapped by false positives due to the confounding impact of demography and the population structure. To reduce false positives, it is critical to conduct a functional analysis to identify the true candidate genes/mutations from those that are filtered through neutrality tests. However, the extremely high cost of such functional analysis may restrict studies within a small number of model species. In particular, when the false positive rate of neutrality tests is high, the efficiency of the functional analysis will also be very low. Therefore, although the recent improvements have been made in the (joint) inference of demography and selection, our ultimate goal, which is to understand the mechanism of adaptation generally in a wide variety of natural populations, may not be achieved using the currently available approaches. More attention should thus be spent on the development of more reliable tests that could not only free themselves from the confounding impact of demography and the population structure but also have reasonable power to detect selection.

Keywords

recent positive selection selective sweep demography population structure false positive 

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Copyright information

© Higher Education Press and Springer-Verlag GmbH 2016

Authors and Affiliations

  • Jinggong Xiang-Yu
    • 1
  • Zongfeng Yang
    • 1
    • 2
  • Kun Tang
    • 1
  • Haipeng Li
    • 1
    Email author
  1. 1.CAS Key Laboratory of Computational Biology, CAS-MPG Parter Institute for Computational Biology, Shanghai Institutes for Biological SciencesChinese Academy of SciencesShanghaiChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

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