MegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traits.
Genome Biol
; 22(1): 213, 2021 07 23.
Article
in 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.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Triticum
/
Software
/
Arabidopsis
/
Genome, Plant
/
Zea mays
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Quantitative Trait, Heritable
/
Models, Genetic
Type of study:
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Language:
En
Journal:
Genome Biol
Year:
2021
Document type:
Article