Prioritising positively selected variants in whole-genome sequencing data using FineMAV

BMC Bioinformatics

Michał Szpak¹  Farhang Aghakhanian²  Yik-Ying Teo³  Jasbir Dhaliwal⁴  Fadilla Wahyudi⁵  Qasim Ayub⁶  Sadequr Rahman⁷ 

[1] European Bioinformatics Institute, CB10 1SA, Hinxton, UK;Wellcome Sanger Institute, Wellcome Genome Campus, CB10 1SA, Hinxton, UK;Monash University Malaysia Genomics Facility, 47500, Bandar Sunway, Selangor Darul Ehsan, Malaysia;Genes and Human Disease Research Program, Oklahoma Medical Research Foundation,, 73104, Oklahoma City, OK, USA;Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore;School of Information Technology, Monash University Malaysia, 47500, Bandar Sunway, Selangor Darul Ehsan, Malaysia;School of Science, Monash University Malaysia, 47500, Bandar Sunway, Selangor Darul Ehsan, Malaysia;School of Science, Monash University Malaysia, 47500, Bandar Sunway, Selangor Darul Ehsan, Malaysia;Monash University Malaysia Genomics Facility, 47500, Bandar Sunway, Selangor Darul Ehsan, Malaysia;Tropical Medicine and Biology Multidisciplinary Platform, Monash University Malaysia, 47500, Bandar Sunway, Selangor Darul Ehsan, Malaysia;School of Science, Monash University Malaysia, 47500, Bandar Sunway, Selangor Darul Ehsan, Malaysia;Tropical Medicine and Biology Multidisciplinary Platform, Monash University Malaysia, 47500, Bandar Sunway, Selangor Darul Ehsan, Malaysia;

关键词: Adaption;    Evolutionary genomics;    Human evolution;    Population differentiation;    Selective sweep;   

DOI  :  10.1186/s12859-021-04506-9

来源: Springer

【 摘 要 】

BackgroundIn population genomics, polymorphisms that are highly differentiated between geographically separated populations are often suggestive of Darwinian positive selection. Genomic scans have highlighted several such regions in African and non-African populations, but only a handful of these have functional data that clearly associates candidate variations driving the selection process. Fine-Mapping of Adaptive Variation (FineMAV) was developed to address this in a high-throughput manner using population based whole-genome sequences generated by the 1000 Genomes Project. It pinpoints positively selected genetic variants in sequencing data by prioritizing high frequency, population-specific and functional derived alleles.ResultsWe developed a stand-alone software that implements the FineMAV statistic. To graphically visualise the FineMAV scores, it outputs the statistics as bigWig files, which is a common file format supported by many genome browsers. It is available as a command-line and graphical user interface. The software was tested by replicating the FineMAV scores obtained using 1000 Genomes Project African, European, East and South Asian populations and subsequently applied to whole-genome sequencing datasets from Singapore and China to highlight population specific variants that can be subsequently modelled. The software tool is publicly available at https://github.com/fadilla-wahyudi/finemav.ConclusionsThe software tool described here determines genome-wide FineMAV scores, using low or high-coverage whole-genome sequencing datasets, that can be used to prioritize a list of population specific, highly differentiated candidate variants for in vitro or in vivo functional screens. The tool displays these scores on the human genome browsers for easy visualisation, annotation and comparison between different genomic regions in worldwide human populations.

【 授权许可】

CC BY