Building a Calibration Set for Genomic Prediction, Characteristics to Be Considered, and Optimization Approaches - INRAE - Institut national de recherche pour l’agriculture, l’alimentation et l’environnement Access content directly
Book Sections Year : 2022

Building a Calibration Set for Genomic Prediction, Characteristics to Be Considered, and Optimization Approaches

Abstract

Abstract The efficiency of genomic selection strongly depends on the prediction accuracy of the genetic merit of candidates. Numerous papers have shown that the composition of the calibration set is a key contributor to prediction accuracy. A poorly defined calibration set can result in low accuracies, whereas an optimized one can considerably increase accuracy compared to random sampling, for a same size. Alternatively, optimizing the calibration set can be a way of decreasing the costs of phenotyping by enabling similar levels of accuracy compared to random sampling but with fewer phenotypic units. We present here the different factors that have to be considered when designing a calibration set, and review the different criteria proposed in the literature. We classified these criteria into two groups: model-free criteria based on relatedness, and criteria derived from the linear mixed model. We introduce criteria targeting specific prediction objectives including the prediction of highly diverse panels, biparental families, or hybrids. We also review different ways of updating the calibration set, and different procedures for optimizing phenotyping experimental designs.

Dates and versions

hal-04343930 , version 1 (14-12-2023)

Identifiers

Cite

Simon Rio, Alain Charcosset, Tristan Mary-Huard, Laurence Moreau, Renaud Rincent. Building a Calibration Set for Genomic Prediction, Characteristics to Be Considered, and Optimization Approaches. Genomic Prediction of Complex Traits, 2467, Springer US, pp.77-112, 2022, Methods in Molecular Biology, ⟨10.1007/978-1-0716-2205-6_3⟩. ⟨hal-04343930⟩
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