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ARS Home » Northeast Area » Ithaca, New York » Robert W. Holley Center for Agriculture & Health » Plant, Soil and Nutrition Research » Research » Publications at this Location » Publication #346573
Title: Genome-wide prediction models that incorporate de novo GWAS are a powerful new tool for tropical rice improvement

Author
item SPINDEL, J.E. - Cornell University
item BEGUM, HASINA - International Rice Research Institute
item AKDEMIR, DENIZ - Cornell University
item COLLARD, BERTRAND - International Rice Research Institute
item REDOOA, EDILBERTO - International Rice Research Institute
item Jannink, Jean-Luc
item MCCOUCH, SUSAN - Cornell University

Submitted to: Heredity
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/25/2015
Publication Date: 2/16/2016
Citation: Spindel, J., Begum, H., Akdemir, D., Collard, B., Redooa, E., Jannink, J., Mccouch, S. 2016. Genome-wide prediction models that incorporate de novo GWAS are a powerful new tool for tropical rice improvement. Heredity. 116:395-408.

Interpretive Summary: To address the multiple challenges to food security posed by global climate change, population growth and rising incomes, plant breeders are developing new crop varieties that can enhance both agricultural productivity and environmental sustainability. Current breeding practices, however, are unable to keep pace with demand. Genomic selection (GS) is a new technique that helps accelerate the rate of genetic gain in breeding by using whole-genome data to predict the value for breeding of new experimental lines. Here, we describe a new GS model that combines a whole genome prediction of new lines with predictions of the effects of specific DNA markers selected from the results of a genome-wide-association study (GWAS). We term this model GS + de novo GWAS. In a breeding population of tropical rice, GS + de novo GWAS outperformed six other models for a variety of traits and in multiple environments. On the basis of these results, we propose an extended, two-part breeding design that can be used to efficiently integrate new variation into elite breeding populations, thus expanding genetic diversity and enhancing the potential for sustainable productivity gains.

Technical Abstract: To address the multiple challenges to food security posed by global climate change, population growth and rising incomes, plant breeders are developing new crop varieties that can enhance both agricultural productivity and environmental sustainability. Current breeding practices, however, are unable to keep pace with demand. Genomic selection (GS) is a new technique that helps accelerate the rate of genetic gain in breeding by using whole-genome data to predict the breeding value of offspring. Here, we describe a new GS model that combines RR-BLUP with markers fit as fixed effects selected from the results of a genome-wide-association study (GWAS) on the RR-BLUP training data. We term this model GS + de novo GWAS. In a breeding population of tropical rice, GS + de novo GWAS outperformed six other models for a variety of traits and in multiple environments. On the basis of these results, we propose an extended, two-part breeding design that can be used to efficiently integrate novel variation into elite breeding populations, thus expanding genetic diversity and enhancing the potential for sustainable productivity gains.