Omics-based hybrid prediction in maize

Omics-based hybrid prediction in maize Theor Appl Genet (2017) 130:1927–1939 DOI 10.1007/s00122-017-2934-0 ORIGINAL ARTICLE 1 1 2,3 2 1 Matthias Westhues · Tobias A. Schrag · Claas Heuer · Georg Thaller · H. Friedrich Utz · 1 4 4 2 5 5 Wolfgang Schipprack · Alexander Thiemann · Felix Seifert · Anita Ehret · Armin Schlereth · Mark Stitt · 5 5 6 4 1 Zoran Nikoloski · Lothar Willmitzer · Chris C. Schön · Stefan Scholten · Albrecht E. Melchinger Received: 9 May 2017 / Accepted: 9 June 2017 / Published online: 24 June 2017 © Springer-Verlag GmbH Germany 2017 Abstract in incorporating intricate epistatic interactions. Down- Key message Complementing genomic data with other stream “omics” data are expected to integrate interactions “omics” predictors can increase the probability of suc‑ within and between different biological strata and provide cess for predicting the best hybrid combinations using the opportunity to improve trait prediction. Yet, predicting complex agronomic traits. traits from parents to progeny has not been addressed by Abstract Accurate prediction of traits with complex a combination of “omics” data. Here, we evaluate several genetic architecture is crucial for selecting superior can- “omics” predictors—genomic, transcriptomic and meta- didates in animal and plant breeding and http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png TAG Theoretical and Applied Genetics Springer Journals

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2017 by Springer-Verlag GmbH Germany
Subject
Life Sciences; Plant Breeding/Biotechnology; Plant Genetics and Genomics; Agriculture; Plant Biochemistry; Biochemistry, general; Biotechnology
ISSN
0040-5752
eISSN
1432-2242
D.O.I.
10.1007/s00122-017-2934-0
Publisher site
See Article on Publisher Site

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