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- W4225574660 endingPage "112" @default.
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- W4225574660 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." @default.
- W4225574660 created "2022-05-05" @default.
- W4225574660 creator A5005899389 @default.
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- W4225574660 creator A5075690910 @default.
- W4225574660 creator A5077263561 @default.
- W4225574660 creator A5077365463 @default.
- W4225574660 date "2022-01-01" @default.
- W4225574660 modified "2023-10-10" @default.
- W4225574660 title "Building a Calibration Set for Genomic Prediction, Characteristics to Be Considered, and Optimization Approaches" @default.
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