ArticleMethods in molecular biology (Clifton, N.J.)2022
Building a Calibration Set for Genomic Prediction, Characteristics to Be Considered, and Optimization Approaches.
Article in Methods in molecular biology (Clifton, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Improving grain yield prediction in Southern US oat germplasm using genomics information and environmental covariates.The plant genome · 2026Article
- Rapid selection of early-maturing and low-ear position maize hybrids suitable for China using genomic prediction.Plant molecular biology · 2026Article
- Multi-trait/environment sparse genomic prediction using the SFSI R-package.The plant genome · 2025Article
- Genomic selection: Essence, applications, and prospects.The plant genome · 2025Review
- Portability of genomic predictions trained on sparse factorial designs across two maize silage breeding cycles.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Optimization of Multi-Generation Multi-location Genomic Prediction Models for Recurrent Genomic Selection in an Upland Rice Population.Rice (New York, N.Y.) · 2023Article
- Sparse multi-trait genomic prediction under balanced incomplete block design.The plant genome · 2023Article
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Authors and funding
5 authors.
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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.
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