Author:
Herzig Anthony F.,Ciullo M.,Deleuze Jean-François,Génin Emmanuelle,Redon Richard,Adjou Chantal,Chatel Stéphanie,Férec Claude,Goldberg Marcel,Halbout Philippe-Antoine,Le Marec Hervé,L’Helgouach David,Rouault Karen,Schott Jean-Jacques,Vogelsperger Anne,Zins Marie,Bacq Delphine,Blanchet Hélène,Boland Anne,Lindenbaum Pierre,Ludwig Thomas,Meyer Vincent,Olaso Robert,Velo-Suárez Lourdes,Alves Isabel,Bocher Ozvan,Dina Christian,Herzig Anthony F.,Karakachoff Matilde,Marenne Gaëlle,Pierre Aude Saint,Leutenegger A-L.,Perdry H.,
Abstract
Abstract
Background
Estimating relatedness is an important step for many genetic study designs. A variety of methods for estimating coefficients of pairwise relatedness from genotype data have been proposed. Both the kinship coefficient $$\varphi$$
φ
and the fraternity coefficient $$\psi$$
ψ
for all pairs of individuals are of interest. However, when dealing with low-depth sequencing or imputation data, individual level genotypes cannot be confidently called. To ignore such uncertainty is known to result in biased estimates. Accordingly, methods have recently been developed to estimate kinship from uncertain genotypes.
Results
We present new method-of-moment estimators of both the coefficients $$\varphi$$
φ
and $$\psi$$
ψ
calculated directly from genotype likelihoods. We have simulated low-depth genetic data for a sample of individuals with extensive relatedness by using the complex pedigree of the known genetic isolates of Cilento in South Italy. Through this simulation, we explore the behaviour of our estimators, demonstrate their properties, and show advantages over alternative methods. A demonstration of our method is given for a sample of 150 French individuals with down-sampled sequencing data.
Conclusions
We find that our method can provide accurate relatedness estimates whilst holding advantages over existing methods in terms of robustness, independence from external software, and required computation time. The method presented in this paper is referred to as LowKi (Low-depth Kinship) and has been made available in an R package (https://github.com/genostats/LowKi).
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Structural Biology
Cited by
1 articles.
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