lme4qtl : Linear mixed models with flexible covariance structure for genetic studies of related individuals
Ziyatdinov, Andrey (Chan School of Public Health)
Vázquez-Santiago, Miquel (Hospital de la Santa Creu i Sant Pau (Barcelona, Catalunya))
Brunel, Helena (Hospital de la Santa Creu i Sant Pau (Barcelona, Catalunya))
Martinez-Perez, Angel (Institut d'Investigació Biomèdica Sant Pau)
Aschard, Hugues (Biostatistique et Biologie Intégrative (C3BI))
Soria Fernández, José Manuel (Institut d'Investigació Biomèdica Sant Pau)
Universitat Autònoma de Barcelona

Date:	2018
Abstract:	Quantitative trait locus (QTL) mapping in genetic data often involves analysis of correlated observations, which need to be accounted for to avoid false association signals. This is commonly performed by modeling such correlations as random effects in linear mixed models (LMMs). The R package lme4 is a well-established tool that implements major LMM features using sparse matrix methods; however, it is not fully adapted for QTL mapping association and linkage studies. In particular, two LMM features are lacking in the base version of lme4: the definition of random effects by custom covariance matrices; and parameter constraints, which are essential in advanced QTL models. Apart from applications in linkage studies of related individuals, such functionalities are of high interest for association studies in situations where multiple covariance matrices need to be modeled, a scenario not covered by many genome-wide association study (GWAS) software. To address the aforementioned limitations, we developed a new R package lme4qtl as an extension of lme4. First, lme4qtl contributes new models for genetic studies within a single tool integrated with lme4 and its companion packages. Second, lme4qtl offers a flexible framework for scenarios with multiple levels of relatedness and becomes efficient when covariance matrices are sparse. We showed the value of our package using real family-based data in the Genetic Analysis of Idiopathic Thrombophilia 2 (GAIT2) project. Our software lme4qtl enables QTL mapping models with a versatile structure of random effects and efficient computation for sparse covariances. lme4qtl is available at https://github. com/variani/lme4qtl.
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Covariance ; GWAS ; Linear mixed models ; Related individuals ; Lme4
Published in:	BMC bioinformatics, Vol. 19 Núm. 1 (27 2018) , p. 68, ISSN 1471-2105

DOI: 10.1186/s12859-018-2057-x
PMID: 29486711

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