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

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.

SUBMITTER: Runcie DE

PROVIDER: S-EPMC8299638 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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

Runcie Daniel E DE Qu Jiayi J Cheng Hao H Crawford Lorin L

Genome biology 20210723 1

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 ...[more]

PMID: 34301310

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