BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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 .

UNLABELLED: : The open source environment R is one of the most widely used software for statistical computing. It provides a variety of applications including statistical genetics. Most of the powerful tools for quantitative genetic analyses are stand-alone free programs developed by researchers in academia. SOLAR is one of the standard software programs to perform linkage and association mappings of the quantitative trait loci (QTLs) in pedigrees of arbitrary size and complexity. solarius allows the user to exploit the variance component methods implemented in SOLAR. It automates such routine operations as formatting pedigree and phenotype data. It parses also the model output and contains summary and plotting functions for exploration of the results. In addition, solarius enables parallel computing of the linkage and association analyses that makes the calculation of genome-wide scans more efficient. AVAILABILITY AND IMPLEMENTATION: solarius is available on CRAN and on GitHub https://github.com/ugcd/solarius CONTACT: : [email protected].