Moment estimators of relatedness from low-depth whole-genome sequencing data

Autor: Herzig, Anthony, Ciullo, M., Leutenegger, A-L., Perdry, H.
Přispěvatelé: Génétique, génomique fonctionnelle et biotechnologies (UMR 1078) (GGB), EFS-Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO), Institute of Genetics and Biophysics 'A. Buzzati Traverso' [Naples, Italy], Consiglio Nazionale delle Ricerche [Roma] (CNR), Istituto Neurologico Mediterraneo (NEUROMED I.R.C.C.S.), Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome] (UNIROMA)-University of Naples Federico II = Università degli studi di Napoli Federico II, Maladies neurodéveloppementales et neurovasculaires (NeuroDiderot (UMR_S_1141 / U1141)), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Centre de recherche en épidémiologie et santé des populations (CESP), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay, FranceGenRef Consortium: Jean-François Deleuze, Emmanuelle Génin, Richard Redon, Chantal Adjou, Stéphanie Chatel, Claude Férec, Marcel Goldberg, Philippe-Antoine Halbout, Hervé Le Marec, David L'Helgouach, Karen Rouault, Jean-Jacques Schott, Anne Vogelsperger, Marie Zins, Delphine Bacq, Hélène Blanchet, Anne Boland, Pierre Lindenbaum, Thomas Ludwig, Vincent Meyer, Robert Olaso, Lourdes Velo-Suárez, Isabel Alves, Ozvan Bocher, Christian Dina, Anthony F Herzig, Matilde Karakachoff, Gaëlle Marenne, Aude Saint Pierre, PODEUR, Sophie
Rok vydání: 2022
Předmět:
Zdroj: BMC Bioinformatics
BMC Bioinformatics, 2022, 23 (1), pp.254. ⟨10.1186/s12859-022-04795-8⟩
ISSN: 1471-2105
DOI: 10.1186/s12859-022-04795-8
Popis: 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).
Databáze: OpenAIRE
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