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

Laurent Gentzbittel, Cécile Ben, Mélanie Mazurier, Min Gyoung Shin, Todd Lorenz, Martina Rickauer, Paul Marjoram, Sergey V. Nuzhdin, Tatiana V. Tatarinova

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

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.

Original languageEnglish
Article number106
JournalGenome Biology
Volume20
Issue number1
DOIs
Publication statusPublished - 28 May 2019
Externally publishedYes

Keywords

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

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