cgrm.D

lambda

Based on a marker matrix $\mathbf{X}$ with {-1,0,1} - out of which a column-wise centered dominance coefficient matrix will be constructed and a shrinkage parameter $\lambda$, <code>cgrm.D</code> returns
  the following dominance genomic relationship matrix according to Su et al. (2012):
                             $$\mathbf{G} = (1-\lambda) \frac{\mathbf{X X}^{'}}{\sum\limits_{i=1}^n 2 p_i q_i(1-2 p_i q_i) }  + \mathbf{I}\lambda$$<p></p><p>  The additive marker coefficients will be used to compute dominance coefficients as: <code>1-abs(X)</code></p>

Frequently used methods in genomic applications with emphasis on parallel computing (OpenMP).
At its core, the package has a Gibbs Sampler that allows running univariate linear
mixed models that have both, sparse and dense design matrices. The parallel sampling method
in case of dense design matrices (e.g. Genotypes) allows running Ridge Regression or BayesA for
a very large number of individuals. The Gibbs Sampler is capable of running Single Step Genomic Prediction models.
In addition, the package offers parallelized functions for common tasks like genome-wide
association studies and cross validation in a memory efficient way.

cgrm.D: Dominance Genomic Relationship Matrix

Description

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See Also

Examples