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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 #357703

Research Project: Database Tools for Managing and Analyzing Big Data Sets to Enhance Small Grains Breeding

Location: Plant, Soil and Nutrition Research

Title: Improving genomic prediction in Cassava field experiments by accounting for interplot competition

Author
item Elias, Ani - Cornell University - New York
item Rabbi, Ismail - International Institute Of Tropical Agriculture (IITA)
item Kulakow, Peter - International Institute Of Tropical Agriculture (IITA)
item Jannink, Jean-luc

Submitted to: G3, Genes/Genomes/Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/9/2018
Publication Date: 1/22/2018
Citation: Elias, A., Rabbi, I., Kulakow, P., Jannink, J. 2018. Improving genomic prediction in Cassava field experiments by accounting for interplot competition. G3, Genes/Genomes/Genetics. https://doi.org/10.1534/g3.117.300354
DOI: https://doi.org/10.1534/g3.117.300354

Interpretive Summary: Breeders must evaluate the performance of new breeding genotypes in field experiments. Evaluation errors can occur because genotypes from adjacent plots compete with each other. We developed models that accounted for such competitive effects due to genetic differences in competitive ability as well as due to random differences. These models were incorporated into methods using genomic markers to predict genotype performance. Predictability of the models was tested. The best model was chosen as the one with the lowest prediction root mean squared error compared to that of the base model having no competitive component. Results from real data studies in cassava (Manihot esculenta, Crantz) indicated that less than a 10% increase in accuracy was achieved with competition model but this value reached up to 25% with a GS competition error model. We also found that the competitive influence of a cassava plot is not limited to adjacent plots but spreads beyond them. Through simulations we found that a 25% increase of accuracy in estimating trait genotypic effect can be achieved in the presence of high competitive variance.

Technical Abstract: Plants' competing for available resources is an unavoidable phenomenon in a field. We conducted studies in cassava (Manihot esculenta Crantz) in order to understand the pattern of this competition. Taking into account the competitive ability of genotypes while selecting parents for breeding advancement or commercialization can be very useful. We assumed that competition could occur in two levels i) at the genotypic level, which we called as inter-clonal, and ii) at the plot level irrespective of the type of genotype, which we call as inter-plot competition or competition error. Modification in incidence matrices was applied in order to relate neighboring genotype/plot to the performance of a target genotype/plot with respect to its competitive ability. This was added into a genomic selection model to simultaneously predict the direct and competitive ability of a genotype. Predictability of the models was tested through a 10-fold cross-validation method repeated five times. The best model was chosen as the one with the lowest prediction root mean squared error (pRMSE) compared to that of the base model having no competitive component. Results from our real data studies indicated that less than 10% increase in accuracy was achieved with GS-inter-clonal competition model but this value reached up to 25% with a GS-competition error model. We also found that the competitive influence of a cassava clone is not just limited to the adjacent neighbors but spreads beyond them. Through simulations we found that a 26% increase of accuracy in estimating trait genotypic effect can be achieved even in the presence of high competitive variance.