Lme4qtl: linear mixed models with flexible covariance structure for genetic studies of related individuals.

ABSTRACT:

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 i

SUBMITTER: Ziyatdinov A 

PROVIDER: S-EPMC5830078 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature