Fast and flexible linear mixed models for genome-wide genetics

Linear mixed effect models are powerful tools used to account for population structure in genome-wide association studies (GWASs) and estimate the genetic architecture of complex traits. However, fully-specified models are computationally demanding and common simplifications often lead to reduced po...

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Bibliographic Details
Published in:PLoS genetics 2019-02, Vol.15 (2), p.e1007978-e1007978
Main Authors: Runcie, Daniel E, Crawford, Lorin
Format: Article
Language:English
Subjects:
Algorithms
Animals
Arabidopsis - genetics
Arabidopsis - growth & development
Bayes Theorem
Bayesian analysis
Biological complexity
Biology and Life Sciences
Body Weight - genetics
Computer Simulation
DNA methylation
Economic models
Estimates
Flowers - genetics
Flowers - growth & development
Gene expression
Gene-Environment Interaction
Generalized linear models
Genetic analysis
Genetic aspects
Genetic diversity
Genetic Markers
Genetic research
Genetic Variation
Genome-wide association studies
Genome-Wide Association Study - statistics & numerical data
Genomes
Genotype-environment interactions
Genotypes
Humans
Independent sample
Linear Models
Mathematical models
Methods
Mice
Models, Genetic
Physical Sciences
Population structure
Quantitative genetics
Quantitative Trait Loci
Research and Analysis Methods
Software
Spatial heterogeneity
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Summary:Linear mixed effect models are powerful tools used to account for population structure in genome-wide association studies (GWASs) and estimate the genetic architecture of complex traits. However, fully-specified models are computationally demanding and common simplifications often lead to reduced power or biased inference. We describe Grid-LMM (https://github.com/deruncie/GridLMM), an extendable algorithm for repeatedly fitting complex linear models that account for multiple sources of heterogeneity, such as additive and non-additive genetic variance, spatial heterogeneity, and genotype-environment interactions. Grid-LMM can compute approximate (yet highly accurate) frequentist test statistics or Bayesian posterior summaries at a genome-wide scale in a fraction of the time compared to existing general-purpose methods. We apply Grid-LMM to two types of quantitative genetic analyses. The first is focused on accounting for spatial variability and non-additive genetic variance while scanning for QTL; and the second aims to identify gene expression traits affected by non-additive genetic variation. In both cases, modeling multiple sources of heterogeneity leads to new discoveries.
ISSN:1553-7404
1553-7390
1553-7404
DOI:10.1371/journal.pgen.1007978