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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 #357921

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

Location: Plant, Soil and Nutrition Research

Title: Accuracy of genomic selection to predict maize single-crosses obtained through different mating designs

Author
item Fritsche-neto, Roberto - Universidad De Sao Paulo
item Akdemir, Deniz - Cornell University - New York
item Jannink, Jean-luc

Submitted to: Theoretical and Applied Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/8/2018
Publication Date: 2/14/2018
Citation: Fritsche-Neto, R., Akdemir, D., Jannink, J. 2018. Accuracy of genomic selection to predict maize single-crosses obtained through different mating designs. Theoretical and Applied Genetics. 131:1153-1162. https://doi.org/10.1007/s00122-018-3068-8
DOI: https://doi.org/10.1007/s00122-018-3068-8

Interpretive Summary: Genomic selection, the breeding practice of making selections on the basis of predications from genomic markers, requires a training population of individuals that have been evaluated in the field and that have been scored with genomic markers. In maize, the best mating design to build the training population has not yet been defined. The design must maximize the prediction accuracy given constraints on costs and on the logistics of the crosses to be made. In this study, a number of different crossing designs were evaluated for the prediction accuracy that they conferred. Of the regular mating designs testing, the so-called “North Carolina design II” worked best. However, non-regular mating designs developed using algorithms can also lead to high accuracy while also reducing the total number of crosses to be made. Nevertheless, the number of parents and the crosses per parent in the training sets should be maximized.

Technical Abstract: Even though many papers have been published about genomic prediction (GP) in maize, the best mating design to build the training population has not been defined yet. Such design must maximize the accuracy given constraints on costs and on the logistics of the crosses to be made. Hence, the aims of this work were: (1) empirically evaluate the effect of the mating designs, used as training set, on genomic selection to predict maize single-crosses obtained through full diallel and North Carolina design II, (2) and identify the possibility of reducing the number of crosses and parents to compose these training sets. Our results suggest that testcross is the worst mating design to use as a training set to predict maize single-crosses that would be obtained through full diallel or North Carolina design II. Moreover, North Carolina design II is the best training set to predict hybrids taken from full diallel. However, hybrids from full diallel and North Carolina design II can be well predicted using optimized training sets, which also allow reducing the total number of crosses to be made. Nevertheless, the number of parents and the crosses per parent in the training sets should be maximized.