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Optimization of Selective Phenotyping and Population Design for Genomic Prediction

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  • Nicolas Heslot

    (Chappes Research Center)

  • Vitaliy Feoktistov

    (Chappes Research Center)

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.

Suggested Citation

  • Nicolas Heslot & Vitaliy Feoktistov, 2020. "Optimization of Selective Phenotyping and Population Design for Genomic Prediction," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(4), pages 579-600, December.
    • Handle: RePEc:spr:jagbes:v:25:y:2020:i:4:d:10.1007_s13253-020-00415-1
    DOI: 10.1007/s13253-020-00415-1
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    References listed on IDEAS

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    1. Vitaliy Feoktistov, 2006. "Differential Evolution," Springer Optimization and Its Applications, Springer, number 978-0-387-36896-2, September.
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    Cited by:

    1. Martin P. Boer & Hans-Peter Piepho & Emlyn R. Williams, 2020. "Linear Variance, P-splines and Neighbour Differences for Spatial Adjustment in Field Trials: How are they Related?," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(4), pages 676-698, December.
    2. Maryna Prus & Hans-Peter Piepho, 2021. "Optimizing the Allocation of Trials to Sub-regions in Multi-environment Crop Variety Testing," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(2), pages 267-288, June.
    3. Shin-Fu Tsai & Chih-Chien Shen & Chen-Tuo Liao, 2021. "Bayesian Optimization Approaches for Identifying the Best Genotype from a Candidate Population," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(4), pages 519-537, December.
    4. Hans-Peter Piepho & Robert J. Tempelman & Emlyn R. Williams, 2020. "Guest Editors’ Introduction to the Special Issue on “Recent Advances in Design and Analysis of Experiments and Observational Studies in Agriculture”," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(4), pages 453-456, December.

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