Efficient multivariate linear mixed model algorithms for genome-wide association studies.
- Authors
- Zhou, Xiang; Stephens, Matthew
- Year
- 2014
- Journal
- Nature methods
- PMID
- 24531419
- DOI
- 10.1038/nmeth.2848
- PMCID
- PMC4211878
Multivariate linear mixed models (mvLMMs) are powerful tools for testing associations between single-nucleotide polymorphisms and multiple correlated phenotypes while controlling for population stratification in genome-wide association studies. We present efficient algorithms in the genome-wide efficient mixed model association (GEMMA) software for fitting mvLMMs and computing likelihood ratio tests. These algorithms offer improved computation speed, power and P-value calibration over existing methods, and can deal with more than two phenotypes.
Illustration of the statistical benefits of our new algorithms implemented in GEMMA. (a) A QQ-plot showing the improved calibration of GEMMA p values compared with those from MTMM for simulated null data. Gray shaded area indicates 0.025 and 0.975 point-wise quantiles of the ordered p values under the null distribution. (b) GEMMA p values are consistently more significant than MTMM p values for the HMDP data. (c) Gain in power for GEMMA compared with MTMM in four different simulation scenarios based on the HMDP data. x-axis in shows the proportion of phenotypic variance in the first phenotype explained (PVE) by the SNP, while the point symbol and line type indicate the SNP effect direction (compared with its effect on the first phenotype) and size (quantified by PVE) on the second phenotype (+: opposite direction, 0.8PVE; Γ: opposite direction, 0.2PVE; o: same direction, 0.8PVE; Ξ: same direction, 0.2PVE). (d) Simulation results illustrating the potential gain in power from four-phenotype vs two-phenotype analyses.
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| Genome-wide association study suggests an independent genetic basis of zinc and cadmium concentrations in fresh sweet corn kernels. | Baseggio M et al. | β | 2021 | β |
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