Local ancestry inference in heterogeneous populations-Are recent recombination events more relevant?

complex ancestry genetic map recombination recombination inference methods

Journal

Briefings in bioinformatics
ISSN: 1477-4054
Titre abrégé: Brief Bioinform
Pays: England
ID NLM: 100912837

Informations de publication

Date de publication:
05 11 2021
Historique:
received: 04 05 2021
revised: 29 06 2021
accepted: 14 07 2021
pubmed: 4 8 2021
medline: 11 3 2022
entrez: 3 8 2021
Statut: ppublish

Résumé

To date, numerous software tools have been developed to infer recombination maps. Many of these software tools infer the recombination rate from linkage disequilibrium, and therefore they infer recombination many generations into the past. Other recently developed methods rely on the inference of recent recombination events to determine the recombination rate, such as identity by descent- and local ancestry inference (LAI)-based tools. Methods that mainly use recent recombination events to infer the recombination rate might be more relevant for certain analyses like LAI. We therefore describe a protocol for creating high-resolution, population-specific recombination maps using methods that mainly use recent recombination events and a method that uses recent and distant recombination events for recombination rate inference. Subsequently, we compared the effect of using maps inferred by these two paradigms on LAI accuracy.

Identifiants

pubmed: 34343255
pii: 6337894
doi: 10.1093/bib/bbab300
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Auteurs

Gerald van Eeden (G)

Stellenbosch University, South Africa.

Caitlin Uren (C)

Stellenbosch University, South Africa.

Gian van der Spuy (G)

Department of Molecular Biology and Human Genetics, Stellenbosch University, South Africa.

Gerard Tromp (G)

South African Tuberculosis Bioinformatics Initiative (SATBBI), South Africa.

Marlo Möller (M)

Department of Molecular Biology and Human Genetics, Stellenbosch University, South Africa.

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