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Genome Biol. 2019 May 28;20(1):106. doi: 10.1186/s13059-019-1697-0.

WhoGEM: an admixture-based prediction machine accurately predicts quantitative functional traits in plants.

Author information

1
EcoLab, Université de Toulouse, CNRS, Avenue de l'Agrobiopole BP 32607, Auzeville-Tolosane, F-31326, Castanet-Tolosan, France. gentz@ensat.fr.
2
EcoLab, Université de Toulouse, CNRS, Avenue de l'Agrobiopole BP 32607, Auzeville-Tolosane, F-31326, Castanet-Tolosan, France.
3
University of Southern California, 1050 Childs Way (USC), Los Angeles, CA, 90089-0371, USA.
4
University of La Verne, 1950 3rd Street, La Verne, CA, 91750, USA.
5
Department of Fundamental Biology and Biotechnology, Siberian Federal University, 660074, Krasnoyarsk, Russia.

Abstract

The explosive growth of genomic data provides an opportunity to make increased use of sequence variations for phenotype prediction. We have developed a prediction machine for quantitative phenotypes (WhoGEM) that overcomes some of the bottlenecks limiting the current methods. We demonstrated its performance by predicting quantitative disease resistance and quantitative functional traits in the wild model plant species, Medicago truncatula, using geographical locations as covariates for admixture analysis. The method's prediction reliability equals or outperforms all existing algorithms for quantitative phenotype prediction. WhoGEM analysis produces evidence that variation in genome admixture proportions explains most of the phenotypic variation for quantitative phenotypes.

KEYWORDS:

Adaptation; Breeding; Genomic prediction; Medicago truncatula; Molecular ecology; Quantitative disease resistance

PMID:
31138283
PMCID:
PMC6537182
DOI:
10.1186/s13059-019-1697-0
[Indexed for MEDLINE]
Free PMC Article

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