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 demonstrate...

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Main Authors: Laurent Gentzbittel, Cécile Ben, Mélanie Mazurier, Min-Gyoung Shin, Todd Lorenz, Martina Rickauer, Paul Marjoram, Sergey V. Nuzhdin, Tatiana V. Tatarinova
Format: Article
Language:English
Published: BMC 2019-05-01
Series:Genome Biology
Subjects:
Online Access:http://link.springer.com/article/10.1186/s13059-019-1697-0
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author Laurent Gentzbittel
Cécile Ben
Mélanie Mazurier
Min-Gyoung Shin
Todd Lorenz
Martina Rickauer
Paul Marjoram
Sergey V. Nuzhdin
Tatiana V. Tatarinova
author_facet Laurent Gentzbittel
Cécile Ben
Mélanie Mazurier
Min-Gyoung Shin
Todd Lorenz
Martina Rickauer
Paul Marjoram
Sergey V. Nuzhdin
Tatiana V. Tatarinova
author_sort Laurent Gentzbittel
collection DOAJ
description 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.
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spelling doaj.art-5b581194cf0f419baf77c6d80d5d69932022-12-22T01:04:13ZengBMCGenome Biology1474-760X2019-05-0120112010.1186/s13059-019-1697-0WhoGEM: an admixture-based prediction machine accurately predicts quantitative functional traits in plantsLaurent Gentzbittel0Cécile Ben1Mélanie Mazurier2Min-Gyoung Shin3Todd Lorenz4Martina Rickauer5Paul Marjoram6Sergey V. Nuzhdin7Tatiana V. Tatarinova8EcoLab, Université de Toulouse, CNRSEcoLab, Université de Toulouse, CNRSEcoLab, Université de Toulouse, CNRSUniversity of Southern CaliforniaUniversity of La VerneEcoLab, Université de Toulouse, CNRSUniversity of Southern CaliforniaUniversity of Southern CaliforniaUniversity of La VerneAbstract 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.http://link.springer.com/article/10.1186/s13059-019-1697-0Genomic predictionMolecular ecologyAdaptationQuantitative disease resistanceBreedingMedicago truncatula
spellingShingle Laurent Gentzbittel
Cécile Ben
Mélanie Mazurier
Min-Gyoung Shin
Todd Lorenz
Martina Rickauer
Paul Marjoram
Sergey V. Nuzhdin
Tatiana V. Tatarinova
WhoGEM: an admixture-based prediction machine accurately predicts quantitative functional traits in plants
Genome Biology
Genomic prediction
Molecular ecology
Adaptation
Quantitative disease resistance
Breeding
Medicago truncatula
title WhoGEM: an admixture-based prediction machine accurately predicts quantitative functional traits in plants
title_full WhoGEM: an admixture-based prediction machine accurately predicts quantitative functional traits in plants
title_fullStr WhoGEM: an admixture-based prediction machine accurately predicts quantitative functional traits in plants
title_full_unstemmed WhoGEM: an admixture-based prediction machine accurately predicts quantitative functional traits in plants
title_short WhoGEM: an admixture-based prediction machine accurately predicts quantitative functional traits in plants
title_sort whogem an admixture based prediction machine accurately predicts quantitative functional traits in plants
topic Genomic prediction
Molecular ecology
Adaptation
Quantitative disease resistance
Breeding
Medicago truncatula
url http://link.springer.com/article/10.1186/s13059-019-1697-0
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