ABSTRACT
Large-scale phenotype data can enhance the power of genomic prediction in plant and animal breeding, as well as human genetics. However, the statistical foundation of multi-trait genomic prediction is based on the multivariate linear mixed effect model, a tool notorious for its fragility when applied to more than a handful of traits. We present MegaLMM, a statistical framework and associated software package for mixed model analyses of a virtually unlimited number of traits. Using three examples with real plant data, we show that MegaLMM can leverage thousands of traits at once to significantly improve genetic value prediction accuracy.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
deruncie{at}ucdavis.edu
jyqqu{at}ucdavis.edu
qtlcheng{at}ucdavis.edu
lorin_crawford{at}brown.edu
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