MegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traits.
Genome Biol
; 22(1): 213, 2021 07 23.
Article
em En
| MEDLINE
| ID: mdl-34301310
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.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Triticum
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Software
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Arabidopsis
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Genoma de Planta
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Zea mays
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Característica Quantitativa Herdável
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Modelos Genéticos
Tipo de estudo:
Prognostic_studies
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Risk_factors_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article