A comprehensive survey of models for dissecting local ancestry deconvolution in human genome

Author:

Geza Ephifania12,Mugo Jacquiline1,Mulder Nicola J2,Wonkam Ambroise3,Chimusa Emile R3ORCID,Mazandu Gaston K123ORCID

Affiliation:

1. African Institute for Mathematical Sciences, Muizenberg, Cape Town 7945, South Africa

2. Computational Biology Division, Department of Integrative Biomedical Sciences, Faculty of Health Sciences, IDM, University of Cape Town, Cape Town 7925, South Africa

3. Division of Human Genetics, Department of Pathology, University of Cape Town, Cape Town 7925, South Africa

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.

Funder

Organization for Women in Science for the Developing World

Swedish International Development Cooperation Agency

German Academic Exchange Service

National Institutes of Health

Wellcome Trust

Publisher

Oxford University Press (OUP)

Subject

Molecular Biology,Information Systems

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