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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 #358006

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

Location: Plant, Soil and Nutrition Research

Title: Accuracies of univariate and multivariate genomic prediction models in African cassava

Author
item Okeke, Uche - Cornell University - New York
item Akdemir, Deniz - 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: Genetics Selection Evolution
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/16/2017
Publication Date: 12/4/2017
Citation: Okeke, U., Akdemir, D., Rabbi, I., Kulakow, P., Jannink, J. 2017. Accuracies of univariate and multivariate genomic prediction models in African cassava. Genetics Selection Evolution. 49:88. https://doi.org/10.1186/s12711-017-0361-y
DOI: https://doi.org/10.1186/s12711-017-0361-y

Interpretive Summary: Genomic selection (GS), the breeding practice of making selections on the basis of predictions from genomic markers, promises to accelerate genetic gain in plant breeding programs especially for crop species such as cassava that have long breeding cycles. Practically, breeders always select crops for multiple traits simultaneously. In this paper, we compared (Scenario 1) prediction accuracies from single-trait (uT) and single-environment multi-trait (MT) models, and (Scenario 2) accuracies from simple (uE) versus complete (ME) multi-environment. For these analyses, we used 16 years of public cassava breeding data for six target cassava traits. In Scenario 1, the MT models had higher prediction accuracies than the uT models for all traits and locations analyzed, which amounted to on average a 40% improved prediction accuracy. For Scenario 2, the ME model had on average (across all locations and traits) a 12% improved prediction accuracy compared to the uE model. We recommend the use of multivariate mixed models (MT and ME) for cassava genetic evaluation. These models may be useful for other plant species.

Technical Abstract: Background Genomic selection (GS) promises to accelerate genetic gain in plant breeding programs especially for crop species such as cassava that have long breeding cycles. Practically, to implement GS in cassava breeding, it is necessary to evaluate different GS models and to develop suitable models for an optimized breeding pipeline. In this paper, we compared (1) prediction accuracies from a single-trait (uT) and a multi-trait (MT) mixed model for a single-environment genetic evaluation (Scenario 1), and (2) accuracies from a compound symmetric multi-environment model (uE) parameterized as a univariate multi-kernel model to a multivariate (ME) multi-environment mixed model that accounts for genotype-by-environment interaction for multi-environment genetic evaluation (Scenario 2). For these analyses, we used 16 years of public cassava breeding data for six target cassava traits and a fivefold cross-validation scheme with 10-repeat cycles to assess model prediction accuracies. Results In Scenario 1, the MT models had higher prediction accuracies than the uT models for all traits and locations analyzed, which amounted to on average a 40% improved prediction accuracy. For Scenario 2, we observed that the ME model had on average (across all locations and traits) a 12% improved prediction accuracy compared to the uE model. Conclusions We recommend the use of multivariate mixed models (MT and ME) for cassava genetic evaluation. These models may be useful for other plant species.