Modeling correlated marker effects in genome-wide prediction via Gaussian concentration graph models.

Abstract excerpt

In genome-wide prediction, independence of marker allele substitution effects is typically assumed; however, since early stages in the evolution of this technology it has been known that nature points to correlated effects. In statistics, graphical models have been identified as a useful and powerful tool for covariance estimation in high dimensional problems and it is an area that has recently experienced a...

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