A comprehensive survey of models for dissecting local ancestry deconvolution in human genome. (29th June 2018)

• Record Type: Journal Article
• Authors: Geza, Ephifania; Mugo, Jacquiline; Mulder, Nicola J; Wonkam, Ambroise; Chimusa, Emile R; Mazandu, Gaston K
• Is Part Of: Briefings in bioinformatics. Volume 20:Number 5(2020)
• Page Start: 1709
• Page End: 1724
• Publication Date: 2018-06-29
• Subjects: local ancestry -- linkage disequilibrium -- admixture -- hidden Markov models -- ancestry informative markers; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bby044
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Over the past decade, studies of admixed populations have increasingly gained interest in both medical and population genetics. These studies have so far shed light on the patterns of genetic variation throughout modern human evolution and have improved our understanding of the demographics and adaptive processes of human populations. To date, there exist about 20 methods or tools to deconvolve local ancestry. These methods have merits and drawbacks in estimating local ancestry in multiway admixed populations. In this article, we survey existing ancestry deconvolution methods, with special emphasis on multiway admixture, and compare these methods based on simulation results reported by different studies, computational approaches used, including mathematical and statistical models, and biological challenges related to each method. This should orient users on the choice of an appropriate method or tool for given population admixture characteristics and update researchers on current advances, challenges and opportunities behind existing ancestry deconvolution methods.