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Description

GEMMA Multivariate Linear Mixed Model.

Fits a multivariate linear mixed effects model that uses a polygenic term, after Zhou & Stephens (2014) (<https://www.nature.com/articles/nmeth.2848>). Of particular interest is the estimation of variance components with restricted maximum likelihood (REML) methods. Genome-wide efficient mixed-model association (GEMMA), as implemented in the package 'gemma2', uses an expectation-maximization algorithm for variance components inference for use in quantitative trait locus studies.

Statuses

Travis-CI BuildStatus

codecov

CRAN RStudio mirrordownloads

Overview

gemma2 is an implementation in R of the GEMMA v 0.97 EM algorithm that is part of the GEMMA algorithm for REML estimation of multivariate linear mixed effects models of the form:

[vec(Y) = X vec(B) + vec(G) + vec(E)]

where (E) is a n by 2 matrix of random effects that follows the matrix-variate normal distribution

[G \sim MN(0, K, V_g)]

where (K) is a relatedness matrix and (V_g) is a 2 by 2 covariance matrix for the two traits of interest.

Additionally, the random errors matrix (E) follows the distribution:

[E \sim MN(0, I_n, V_e)]

and (G) and (E) are independent.

Installation

To install gemma2, use the devtools R package from CRAN. If you haven’t installed devtools, please run this line of code:

install.packages("devtools")

Then, run this line of code to install gemma2:

devtools::install_github("fboehm/gemma2")

References

X. Zhou & M. Stephens. Efficient multivariate linear mixed model algorithms for genome-wide association studies. Nature Methods volume 11, pages 407–409 (2014). https://www.nature.com/articles/nmeth.2848

https://github.com/genetics-statistics/GEMMA.

Metadata

Version

0.1.3

License

Unknown

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