MegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traits
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AbstractLarge-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 , 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 can leverage thousands of traits at once to significantly improve genetic value prediction accuracy.
2020 ◽
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2013 ◽
Vol 33
(suppl_1)
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2020 ◽
Keyword(s):
2020 ◽