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bioRxiv · 10.1101/2020.05.26.116814

Mega-scale linear mixed models for genomic predictions with thousands of traits

Runcie, D. E.·Cheng, H.·Crawford, L.
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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.

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Runcie, D. E., Cheng, H., Crawford, L.. 2020-05-29. Mega-scale linear mixed models for genomic predictions with thousands of traits. https://doi.org/10.1101/2020.05.26.116814

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