FineMAV: prioritizing candidate genetic variants driving local adaptations in human populations

Michał Szpak; Massimo Mezzavilla; Qasim Ayub; Yuan Chen; Yali Xue; Chris Tyler-Smith

doi:10.1186/s13059-017-1380-2

FineMAV: prioritizing candidate genetic variants driving local adaptations in human populations

Michał Szpak, Massimo Mezzavilla, Qasim Ayub, Yuan Chen, Yali Xue, Chris Tyler-Smith

Malaysia Sch of Science

Research output: Contribution to journal › Article › Research › peer-review

14 Citations (Scopus)

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.

Original language	English
Article number	5
Number of pages	18
Journal	Genome Biology
Volume	19
Issue number	1
DOIs	https://doi.org/10.1186/s13059-017-1380-2
Publication status	Published - 17 Jan 2018

Keywords

FineMAV
Human evolution
Local adaptation
Positive selection
Selective sweep

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344995767-oaFinal published version, 1.87 MBLicence: CC BY

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title = "FineMAV: prioritizing candidate genetic variants driving local adaptations in human populations",

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AU - Mezzavilla, Massimo

AU - Ayub, Qasim

AU - Chen, Yuan

AU - Xue, Yali

AU - Tyler-Smith, Chris

PY - 2018/1/17

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FineMAV: prioritizing candidate genetic variants driving local adaptations in human populations

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Keywords

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