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 wh...

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Main Authors:	Daniel E. Runcie, Jiayi Qu, Hao Cheng, Lorin Crawford
Format:	Article
Language:	English
Published:	BMC 2021-07-01
Series:	Genome Biology
Subjects:	Multi-trait Linear Mixed Model Genomic prediction High-throughput phenotyping Multi-environment trial
Online Access:	https://doi.org/10.1186/s13059-021-02416-w

id	doaj-aac15e59778c49368fcc0b6698c8ad53
record_format	Article
spelling	doaj-aac15e59778c49368fcc0b6698c8ad532021-07-25T11:45:44ZengBMCGenome Biology1474-760X2021-07-0122112510.1186/s13059-021-02416-wMegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traitsDaniel E. Runcie0Jiayi Qu1Hao Cheng2Lorin Crawford3Department of Plant Sciences, University of California DavisDepartment of Plant Sciences, University of California DavisDepartment of Plant Sciences, University of California DavisMicrosoft Research New EnglandAbstract 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.https://doi.org/10.1186/s13059-021-02416-wMulti-trait Linear Mixed ModelGenomic predictionHigh-throughput phenotypingMulti-environment trial
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Daniel E. Runcie Jiayi Qu Hao Cheng Lorin Crawford
spellingShingle	Daniel E. Runcie Jiayi Qu Hao Cheng Lorin Crawford MegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traits Genome Biology Multi-trait Linear Mixed Model Genomic prediction High-throughput phenotyping Multi-environment trial
author_facet	Daniel E. Runcie Jiayi Qu Hao Cheng Lorin Crawford
author_sort	Daniel E. Runcie
title	MegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traits
title_short	MegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traits
title_full	MegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traits
title_fullStr	MegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traits
title_full_unstemmed	MegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traits
title_sort	megalmm: mega-scale linear mixed models for genomic predictions with thousands of traits
publisher	BMC
series	Genome Biology
issn	1474-760X
publishDate	2021-07-01
description	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.
topic	Multi-trait Linear Mixed Model Genomic prediction High-throughput phenotyping Multi-environment trial
url	https://doi.org/10.1186/s13059-021-02416-w
work_keys_str_mv	AT danieleruncie megalmmmegascalelinearmixedmodelsforgenomicpredictionswiththousandsoftraits AT jiayiqu megalmmmegascalelinearmixedmodelsforgenomicpredictionswiththousandsoftraits AT haocheng megalmmmegascalelinearmixedmodelsforgenomicpredictionswiththousandsoftraits AT lorincrawford megalmmmegascalelinearmixedmodelsforgenomicpredictionswiththousandsoftraits
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MegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traits

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