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

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

Research output: Contribution to journalArticleResearchpeer-review

9 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 languageEnglish
Article number5
Number of pages18
JournalGenome Biology
Volume19
Issue number1
DOIs
Publication statusPublished - 17 Jan 2018

Keywords

  • FineMAV
  • Human evolution
  • Local adaptation
  • Positive selection
  • Selective sweep

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