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


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.

SUBMITTER: Gentzbittel L 

PROVIDER: S-EPMC6537182 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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

Gentzbittel Laurent L   Ben Cécile C   Mazurier Mélanie M   Shin Min-Gyoung MG   Lorenz Todd T   Rickauer Martina M   Marjoram Paul P   Nuzhdin Sergey V SV   Tatarinova Tatiana V TV  

Genome biology 20190528 1


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  ...[more]

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