Efficient Algorithms for Multivariate Linear Mixed Models in Genome-wide Association Studies

By Xiaoping Zhou, Matthew Stephens. Nature Methods, 2013

730 citations recorded by the source corpus.

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.

Subfields: Genetics, Plant Science

View Paper: https://www.semanticscholar.org/paper/e85267db9c0a31e065ed1f34375b9e1197de0d34

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. Robert Plomin
2. Ian J. Deary
3. A. Singh
4. B. Singh

In Plant Science

1. Ian J. Deary
2. A. Singh
3. B. Singh
4. Robert Plomin

Citations in the corpus

• Marker-Assisted Plant Breeding: Principles and Practices (2015, background): “GEMMA has improved speed and power and can handle more than two phenotypes (Zhou and Stephens 2014).”
• rMVP: A Memory-efficient, Visualization-enhanced, and Parallel-accelerated Tool for Genome-wide Association Study (2020, neutral): “Furthermore, with the development of multi-trait methods, such as GSA-SNP2 [20], MTMM [21], mvLMMs [22], and mtSet [23], results from multiple-group GWAS need to be displayed simultaneously for easier comparisons.”
• Genetics and Intelligence Differences: Five Special Findings (2014, positive): “The value of GCTA has been greatly increased by extending it beyond the univariate analysis of the variance of a single trait to the bivariate analysis of the covariance between two traits or the ‘same’ trait at two ages 44, 45 ; a recent approach is multivariate rather than just bivariate 46 .”