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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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| Published in: | PLoS genetics 2019-02, Vol.15 (2), p.e1007978-e1007978 |
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| container_end_page | e1007978 |
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| creator | Runcie, Daniel E Crawford, Lorin |
| description | 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. |
| doi_str_mv | 10.1371/journal.pgen.1007978 |
| format | article |
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| 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 |
| title | Fast and flexible linear mixed models for genome-wide genetics |
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