Genomic prediction models for grain yield of spring bread wheat in diverse agro-ecological zones View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2016-07

AUTHORS

C. Saint Pierre, J. Burgueño, J. Crossa, G. Fuentes Dávila, P. Figueroa López, E. Solís Moya, J. Ireta Moreno, V. M. Hernández Muela, V. M. Zamora Villa, P. Vikram, K. Mathews, C. Sansaloni, D. Sehgal, D. Jarquin, P. Wenzl, Sukhwinder Singh

ABSTRACT

Genomic and pedigree predictions for grain yield and agronomic traits were carried out using high density molecular data on a set of 803 spring wheat lines that were evaluated in 5 sites characterized by several environmental co-variables. Seven statistical models were tested using two random cross-validations schemes. Two other prediction problems were studied, namely predicting the lines' performance at one site with another (pairwise-site) and at untested sites (leave-one-site-out). Grain yield ranged from 3.7 to 9.0 t ha(-1) across sites. The best predictability was observed when genotypic and pedigree data were included in the models and their interaction with sites and the environmental co-variables. The leave-one-site-out increased average prediction accuracy over pairwise-site for all the traits, specifically from 0.27 to 0.36 for grain yield. Days to anthesis, maturity, and plant height predictions had high heritability and gave the highest accuracy for prediction models. Genomic and pedigree models coupled with environmental co-variables gave high prediction accuracy due to high genetic correlation between sites. This study provides an example of model prediction considering climate data along-with genomic and pedigree information. Such comprehensive models can be used to achieve rapid enhancement of wheat yield enhancement in current and future climate change scenario. More... »

PAGES

27312

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/srep27312

DOI

http://dx.doi.org/10.1038/srep27312

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1023190037

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/27311707


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