LiMMBo: a simple, scalable approach for linear mixed models in high-dimensional genetic association studies

Meyer, Hannah Verena, Casale, Francesco Paolo, Stegle, Oliver, Birney, Ewan (January 2018) LiMMBo: a simple, scalable approach for linear mixed models in high-dimensional genetic association studies. bioRxiv. p. 255497. (Unpublished)

URL: https://www.biorxiv.org/content/10.1101/255497v1
DOI: 10.1101/255497

Abstract

Genome-wide association studies have helped to shed light on the genetic architecture of complex traits and diseases. Deep phenotyping of population cohorts is increasingly applied, where multi- to high-dimensional phenotypes are recorded in the individuals. Whilst these rich datasets provide important opportunities to analyse complex trait structures and pleiotropic effects at a genome-wide scale, existing statistical methods for joint genetic analyses are hampered by computational limitations posed by high-dimensional phenotypes. Consequently, such multivariate analyses are currently limited to a moderate number of traits. Here, we introduce a method that combines linear mixed models with bootstrapping (LiMMBo) to enable computationally efficient joint genetic analysis of high-dimensional phenotypes. Our method builds on linear mixed models, thereby providing robust control for population structure and other confounding factors, and the model scales to larger datasets with up to hundreds of phenotypes. We first validate LiMMBo using simulations, demonstrating consistent covariance estimates at greatly reduced computational cost compared to existing methods. We also find LiMMBo yields consistent power advantages compared to univariate modelling strategies, where the advantages of multivariate mapping increases substantially with the phenotype dimensionality. Finally, we applied LiMMBo to 41 yeast growth traits to map their genetic determinants, finding previously known and novel pleiotropic relationships in this high-dimensional phenotype space. LiMMBo is accessible as open source software (https://github.com/HannahVMeyer/limmbo).

Item Type: Paper
Subjects: bioinformatics > computational biology
bioinformatics > genomics and proteomics > computers > computer software
Investigative techniques and equipment > assays > genome wide association studies
CSHL Authors:
Communities: CSHL labs > Meyer Lab
Depositing User: Matthew Dunn
Date: 30 January 2018
Date Deposited: 25 Mar 2019 13:56
Last Modified: 25 Mar 2019 13:56
URI: http://repository.cshl.edu/id/eprint/37735

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