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Whole-genome modeling accurately predicts quantitative traits in plants

Laurent Gentzbittel, Cécile Ben, Mélanie Mazurier, Min-Gyoung Shin, Martin Triska, Martina Rickauer, Yuri Nikolsky, Paul Marjoram, Sergey Nuzhdin, Tatiana V. Tatarinova
doi: https://doi.org/10.1101/030395
Laurent Gentzbittel
1Université Fédérale de Toulouse; INP; EcoLab (Laboratoire Écologie Fonctionnelle et Environnement), ENSAT, 18 chemin de Borde Rouge, 31326 Castanet-Tolosan, France
2CNRS-EcoLab (Laboratoire Écologie Fonctionnelle et Environnement), 31326 Castanet-Tolosan, France
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Cécile Ben
1Université Fédérale de Toulouse; INP; EcoLab (Laboratoire Écologie Fonctionnelle et Environnement), ENSAT, 18 chemin de Borde Rouge, 31326 Castanet-Tolosan, France
2CNRS-EcoLab (Laboratoire Écologie Fonctionnelle et Environnement), 31326 Castanet-Tolosan, France
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Mélanie Mazurier
1Université Fédérale de Toulouse; INP; EcoLab (Laboratoire Écologie Fonctionnelle et Environnement), ENSAT, 18 chemin de Borde Rouge, 31326 Castanet-Tolosan, France
2CNRS-EcoLab (Laboratoire Écologie Fonctionnelle et Environnement), 31326 Castanet-Tolosan, France
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Min-Gyoung Shin
3Molecular and Computational Biology Program, University of Southern California, Los Angeles, California 90089, USA
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Martin Triska
4Spatial Sciences Institute, University of Southern California, Los Angeles, California 90089, USA
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Martina Rickauer
1Université Fédérale de Toulouse; INP; EcoLab (Laboratoire Écologie Fonctionnelle et Environnement), ENSAT, 18 chemin de Borde Rouge, 31326 Castanet-Tolosan, France
2CNRS-EcoLab (Laboratoire Écologie Fonctionnelle et Environnement), 31326 Castanet-Tolosan, France
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Yuri Nikolsky
5School of Systems Biology, George Mason University, Manassas, Virginia 20110, USA
6Vavilov Institute of General Genetics, Moscow, Russian Federation
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Paul Marjoram
7Division of Biostatistics, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California 90089, USA
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Sergey Nuzhdin
3Molecular and Computational Biology Program, University of Southern California, Los Angeles, California 90089, USA
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Tatiana V. Tatarinova
4Spatial Sciences Institute, University of Southern California, Los Angeles, California 90089, USA
8Department of Pediatrics, Keck School of Medicine and Children’s Hospital Los Angeles, University of Southern California, Los Angeles, California 90027, USA
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Abstract

Understanding the relationship between genomic variation and variation in phenotypes for quantitative traits such as physiology, yield, fitness or behavior, will provide important insights for both predicting adaptive evolution and for breeding schemes. A particular question is whether the genetic variation that influences quantitative phenotypes is typically the result of one or two mutations of large effect, or multiple mutations of small effect. In this paper we explore this issue using the wild model legume Medicago truncatula. We show that phenotypes, such as quantitative disease resistance, can be well-predicted using genome-wide patterns of admixture, from which it follows that there must be many mutations of small effect.

Our findings prove the potential of our novel “whole-genome modeling” –WhoGEM– method and experimentally validate, for the first time, the infinitesimal model as a mechanism for adaptation of quantitative phenotypes in plants. This insight can accelerate breeding and biomedicine research programs.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted October 31, 2015.
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Whole-genome modeling accurately predicts quantitative traits in plants
Laurent Gentzbittel, Cécile Ben, Mélanie Mazurier, Min-Gyoung Shin, Martin Triska, Martina Rickauer, Yuri Nikolsky, Paul Marjoram, Sergey Nuzhdin, Tatiana V. Tatarinova
bioRxiv 030395; doi: https://doi.org/10.1101/030395
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Whole-genome modeling accurately predicts quantitative traits in plants
Laurent Gentzbittel, Cécile Ben, Mélanie Mazurier, Min-Gyoung Shin, Martin Triska, Martina Rickauer, Yuri Nikolsky, Paul Marjoram, Sergey Nuzhdin, Tatiana V. Tatarinova
bioRxiv 030395; doi: https://doi.org/10.1101/030395

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