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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 #357700

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 using spatial analysis

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: 10/13/2017
Publication Date: 1/1/2018
Citation: Elias, A., Rabbi, I., Kulakow, P., Jannink, J. 2018. Improving genomic prediction in cassava field experiments using spatial analysis. G3, Genes/Genomes/Genetics. 8:53-62. https://doi.org/10.1534/g3.117.300323
DOI: https://doi.org/10.1534/g3.117.300323

Interpretive Summary: Genomic selection, the breeding practice of making selections on the basis of predictions from genomic markers, requires careful field evaluations for data to train the model. Cassava (Manihot esculenta Crantz) is an important staple food in subSaharan Africa. Breeding experiments were conducted at the International Institute of Tropical Agriculture in cassava to select elite parents. Taking into account the spatial heterogeneity in the field while evaluating these trials can improve the quality of data being used to train genomic selection models, and increase the accuracy of those models. We used an exploratory approach to test different models to account for spatial heterogeneity in the field. The spatial effect was fit simultaneously with a genomic effect in a genomic prediction model, and we compared spatial to baseline models without a spatial component. Results from our real and simulated data studies indicated that predictability can be increased by accounting for spatial variation irrespective of the heritability of the trait. In real data scenarios we observed that the accuracy was increased by a median value of 3.4%. Through simulations we showed that a 21% increase in accuracy can be achieved. These improvements make it worthwhile to invest the effort to implement more complicated models.

Technical Abstract: Cassava (Manihot esculenta Crantz) is an important staple food in sub-Saharan Africa. Breeding experiments were conducted at the International Institute of Tropical Agriculture in cassava to select elite parents. Taking into account the heterogeneity in the field while evaluating these trials can increase the accuracy in estimation of breeding values. We used an exploratory approach using the parametric spatial kernels Power, Spherical, and Gaussian to determine the best kernel for a given scenario. The spatial kernel was fit simultaneously with a genomic kernel in a genomic selection model. Predictability of these 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 compared to that of the base model having no spatial kernel. Results from our real and simulated data studies indicated that predictability can be increased by accounting for spatial variation irrespective of the heritability of the trait. In real data scenarios we observed that the accuracy can be increased by a median value of 3.4%. Through simulations we showed that a 21% increase in accuracy can be achieved. We also found that Range (row) directional spatial kernels, mostly Gaussian, explained the spatial variance in 71% of the scenarios when spatial correlation was significant.