Efficient and flexible Integration of variant characteristics in rare variant association studies using integrated nested Laplace approximation

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• dc.contributor.author Sušak, Hana, 1985-
• dc.contributor.author Serra Saurina, Laura
• dc.contributor.author Demidov, German, 1990-
• dc.contributor.author Rabionet, Raquel
• dc.contributor.author Domènech Salgado, Laura, 1989-
• dc.contributor.author Bosio, Mattia
• dc.contributor.author Muyas Remolar, Francesc, 1992-
• dc.contributor.author Estivill, Xavier, 1955-
• dc.contributor.author Escaramís, Geòrgia
• dc.contributor.author Ossowski, Stephan
• dc.date.accessioned 2021-03-23T11:10:53Z
• dc.date.available 2021-03-23T11:10:53Z
• dc.date.issued 2021
• dc.description.abstract Rare variants are thought to play an important role in the etiology of complex diseases and may explain a significant fraction of the missing heritability in genetic disease studies. Next-generation sequencing facilitates the association of rare variants in coding or regulatory regions with complex diseases in large cohorts at genome-wide scale. However, rare variant association studies (RVAS) still lack power when cohorts are small to medium-sized and if genetic variation explains a small fraction of phenotypic variance. Here we present a novel Bayesian rare variant Association Test using Integrated Nested Laplace Approximation (BATI). Unlike existing RVAS tests, BATI allows integration of individual or variant-specific features as covariates, while efficiently performing inference based on full model estimation. We demonstrate that BATI outperforms established RVAS methods on realistic, semi-synthetic whole-exome sequencing cohorts, especially when using meaningful biological context, such as functional annotation. We show that BATI achieves power above 70% in scenarios in which competing tests fail to identify risk genes, e.g. when risk variants in sum explain less than 0.5% of phenotypic variance. We have integrated BATI, together with five existing RVAS tests in the 'Rare Variant Genome Wide Association Study' (rvGWAS) framework for data analyzed by whole-exome or whole genome sequencing. rvGWAS supports rare variant association for genes or any other biological unit such as promoters, while allowing the analysis of essential functionalities like quality control or filtering. Applying rvGWAS to a Chronic Lymphocytic Leukemia study we identified eight candidate predisposition genes, including EHMT2 and COPS7A.
• dc.description.sponsorship Funding: SO, HS, XE, FM and GD received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 635290 (PanCanRisk). SO and GD received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 779257 (Solve-RD). RR received support from the Fundació La Marató 70/307:201726. HS, GD, RR, LD, MB, FM, XE, GE and SO received support of the Spanish Ministry of Economy and Competitiveness, ‘Centro de Excelencia Severo Ochoa 2013-2017, and the CERCA Programme / Generalitat de Catalunya
• dc.format.mimetype application/pdf
• dc.identifier.citation Susak H, Serrano-Sauria L, Demidov G, Rabionet R, Domènech L, Bosio M et al. Efficient and flexible Integration of variant characteristics in rare variant association studies using integrated nested Laplace approximation. PLoS Comput Biol. 2021 Feb 19;17(2):e1007784. DOI: 10.1371/journal.pcbi.1007784
• dc.identifier.doi http://dx.doi.org/10.1371/journal.pcbi.1007784
• dc.identifier.issn 1553-734X
• dc.identifier.uri http://hdl.handle.net/10230/46908
• dc.language.iso eng
• dc.publisher Public Library of Science (PLoS)
• dc.relation.ispartof PLOS Computational Biology. 2021 Feb 19;17(2):e1007784
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/635290
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/779257
• dc.rights © 2021 Susak et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri https://creativecommons.org/licenses/by/4.0/
• dc.subject.other Genètica
• dc.subject.other Genòmica
• dc.subject.other Malalties
• dc.subject.other Malalties congènites
• dc.title Efficient and flexible Integration of variant characteristics in rare variant association studies using integrated nested Laplace approximation
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion