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Whole-genome modeling accurately predicts quantitative traits, as revealed 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 Ecologie Fonctionnelle et Environnement), ENSAT, 18 chemin de Borde Rouge, 31326 Castanet-Tolosan, France
2CNRS-EcoLab (Laboratoire Ecologie Fonctionnelle et Environnement), 31326 Castanet-Tolosan, France
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  • For correspondence: gentz@ensat.fr
Cécile Ben
1Université Fédérale de Toulouse; INP; EcoLab (Laboratoire Ecologie Fonctionnelle et Environnement), ENSAT, 18 chemin de Borde Rouge, 31326 Castanet-Tolosan, France
2CNRS-EcoLab (Laboratoire Ecologie Fonctionnelle et Environnement), 31326 Castanet-Tolosan, France
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Mélanie Mazurier
1Université Fédérale de Toulouse; INP; EcoLab (Laboratoire Ecologie Fonctionnelle et Environnement), ENSAT, 18 chemin de Borde Rouge, 31326 Castanet-Tolosan, France
2CNRS-EcoLab (Laboratoire Ecologie 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 Ecologie Fonctionnelle et Environnement), ENSAT, 18 chemin de Borde Rouge, 31326 Castanet-Tolosan, France
2CNRS-EcoLab (Laboratoire Ecologie 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

Many adaptive events in natural populations, as well as response to artificial selection, are caused by polygenic action. Under selective pressure, the adaptive traits can quickly respond via small allele frequency shifts spread across numerous loci. We hypothesize that a large proportion of current phenotypic variation between individuals may be best explained by population admixture.

We thus consider the complete, genome-wide universe of genetic variability, spread across several ancestral populations originally separated. We experimentally confirmed this hypothesis by predicting the differences in quantitative disease resistance levels among accessions in the wild legume Medicago truncatula. We discovered also that variation in genome admixture proportion explains most of phenotypic variation for several quantitative functional traits, but not for symbiotic nitrogen fixation. We shown that positive selection at the species level might not explain current, rapid adaptation.

These findings prove the infinitesimal model as a mechanism for adaptation of quantitative phenotypes. Our study produced the first evidence that the whole-genome modeling of DNA variants is the best approach to describe an inherited quantitative trait in a higher eukaryote organism and proved the high potential of admixture-based analyses. This insight contribute to the understanding of polygenic adaptation, and can accelerate plant and animal 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 February 04, 2016.
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Whole-genome modeling accurately predicts quantitative traits, as revealed 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, as revealed 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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