Affiliation:
1. Department of Biology, The Bioinformatics Centre, University of Copenhagen, 2200 Copenhagen N, Denmark
2. H. Lundbeck A/S, 2500 Valby, Denmark
Abstract
Abstract
Estimation of relatedness between pairs of individuals is important in many genetic research areas. When estimating relatedness, it is important to account for admixture if this is present. However, the methods that can account for admixture are all based on genotype data as input, which is a problem for low-depth next-generation sequencing (NGS) data from which genotypes are called with high uncertainty. Here, we present a software tool, NGSremix, for maximum likelihood estimation of relatedness between pairs of admixed individuals from low-depth NGS data, which takes the uncertainty of the genotypes into account via genotype likelihoods. Using both simulated and real NGS data for admixed individuals with an average depth of 4x or below we show that our method works well and clearly outperforms all the commonly used state-of-the-art relatedness estimation methods PLINK, KING, relateAdmix, and ngsRelate that all perform quite poorly. Hence, NGSremix is a useful new tool for estimating relatedness in admixed populations from low-depth NGS data. NGSremix is implemented in C/C++ in a multi-threaded software and is freely available on Github https://github.com/KHanghoj/NGSremix.
Funder
Innovation Fund Denmark
European Research Council
European Union’s Horizon 2020 research and innovation programme
Lundbeck foundation
Novo Nordisk Foundation
Publisher
Oxford University Press (OUP)
Subject
Genetics(clinical),Genetics,Molecular Biology
Cited by
11 articles.
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