Genomic and metabolic prediction of complex heterotic traits in hybrid maize View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2012-02

AUTHORS

Christian Riedelsheimer, Angelika Czedik-Eysenberg, Christoph Grieder, Jan Lisec, Frank Technow, Ronan Sulpice, Thomas Altmann, Mark Stitt, Lothar Willmitzer, Albrecht E Melchinger

ABSTRACT

Maize is both an exciting model organism in plant genetics and also the most important crop worldwide for food, animal feed and bioenergy production. Recent genome-wide association and metabolic profiling studies aimed to resolve quantitative traits to their causal genetic loci and key metabolic regulators. Here we present a complementary approach that exploits large-scale genomic and metabolic information to predict complex, highly polygenic traits in hybrid testcrosses. We crossed 285 diverse Dent inbred lines from worldwide sources with two testers and predicted their combining abilities for seven biomass- and bioenergy-related traits using 56,110 SNPs and 130 metabolites. Whole-genome and metabolic prediction models were built by fitting effects for all SNPs or metabolites. Prediction accuracies ranged from 0.72 to 0.81 for SNPs and from 0.60 to 0.80 for metabolites, allowing a reliable screening of large collections of diverse inbred lines for their potential to create superior hybrids. More... »

PAGES

217

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/ng.1033

DOI

http://dx.doi.org/10.1038/ng.1033

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1027256894

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/22246502


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