1. FineMAV: prioritizing candidate genetic variants driving local adaptations in human populations
- Author
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Yali Xue, Chris Tyler-Smith, Qasim Ayub, Massimo Mezzavilla, Yuan Chen, and Michal Szpak
- Subjects
0301 basic medicine ,False discovery rate ,lcsh:QH426-470 ,Local adaptation ,Population ,Adaptation, Biological ,Method ,Computational biology ,Biology ,Polymorphism, Single Nucleotide ,White People ,FineMAV ,Mice ,03 medical and health sciences ,0302 clinical medicine ,Asian People ,Gene Frequency ,Animals ,Humans ,SNP ,Computer Simulation ,Selection, Genetic ,1000 Genomes Project ,education ,lcsh:QH301-705.5 ,Selective sweep ,Allele frequency ,Human evolution ,education.field_of_study ,Asia, Eastern ,United States ,Human genetics ,Europe ,Positive selection ,lcsh:Genetics ,030104 developmental biology ,lcsh:Biology (General) ,Africa ,030217 neurology & neurosurgery - Abstract
We present a new method, Fine-Mapping of Adaptive Variation (FineMAV), which combines population differentiation, derived allele frequency, and molecular functionality to prioritize positively selected candidate variants for functional follow-up. We calibrate and test FineMAV using eight experimentally validated “gold standard” positively selected variants and simulations. FineMAV has good sensitivity and a low false discovery rate. Applying FineMAV to the 1000 Genomes Project Phase 3 SNP dataset, we report many novel selected variants, including ones in TGM3 and PRSS53 associated with hair phenotypes that we validate using available independent data. FineMAV is widely applicable to sequence data from both human and other species. Electronic supplementary material The online version of this article (10.1186/s13059-017-1380-2) contains supplementary material, which is available to authorized users.
- Published
- 2018
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