Optimization of Selective Phenotyping and Population Design for Genomic Prediction

Nicolas Heslot () and Vitaliy Feoktistov
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Nicolas Heslot: Chappes Research Center
Vitaliy Feoktistov: Chappes Research Center

Journal of Agricultural, Biological and Environmental Statistics, 2020, vol. 25, issue 4, No 7, 579-600

Abstract: Abstract Genomic prediction, the joint analysis of high-density molecular marker data and phenotype to predict the performance of individuals for breeding purpose, is now a method used in routine in many plant and animal breeding programs. This opens several new design questions such as how to select a subset of preexisting individuals for phenotyping based on the molecular marker data to estimate marker effects with the highest precision, in hybrid species, how to choose the hybrids combination to create and phenotype to best predict the performance of the unobserved hybrid combinations and last from a list of individuals, which new populations of individuals to create to optimize marker effects estimation with a budget constraint. Those three designs questions are interrelated and critical to improve the efficiency of breeding. In this article we present efficient optimization methods to answer those three designs questions. Validation results using real data and simulations are presented. Results show that in several situations significant gain in precision of evaluation of selection candidates and marker effects are possible to help increase further the efficiency of plant breeding.

Keywords: Training population; Experimental design; Population design; Genomic selection; Global optimization (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s13253-020-00415-1

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