Relatedness and Genotype × Environment Interaction Affect Prediction Accuracies in Genomic Selection: A Study in Cassava
By D. Ly, M. Hamblin, Ismail Y. Rabbi, G. Melaku, M. Bakare, Hugh G. Jr Gauch, R. Okechukwu, A. Dixon, Peter Kulakow, Jean-Luc Jannink. 2013
113 citations recorded by the source corpus.
Before implementation of genomic selection, evaluation of the potential accuracy of prediction can be obtained by cross-validation. In this procedure, a population with both phenotypes and genotypes is split into training and validation sets. The prediction model is fi tted using the training set, and its accuracy is calculated on the validation set. The degree of genetic relatedness between the training and validation sets may infl uence the expected accuracy as may the genotype × environment (G×E) interaction in those sets. We developed a method to assess these effects and tested it in cassava (Manihot esculenta Crantz). We used historical phenotypic data available from the International Institute of Tropical Agriculture Genetic Gain trial and performed genotyping by sequencing for these clones. We tested cross-validation sampling schemes preventing the training and validation sets from sharing (i) genetically close clones or (ii) similar evaluation locations. For 19 traits, plot-basis heritabilities ranged from 0.04 to 0.66. The correlation between predicted and observed phenotypes ranged from 0.15 to 0.47. Across traits, predicting for less related clones decreased accuracy from 0 to 0.07, a small but consistent effect. For 17 traits, predicting for different locations decreased accuracy between 0.01 and 0.18. Genomic selection has potential to accelerate gains in cassava and the existing training population should give a reasonable estimate of future prediction accuracies.
Subfields: Plant Science
View Paper: https://www.semanticscholar.org/paper/cd4664528794fe021c0cb3f20aa396ae2422e46d
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In Plant Science
Citations in the corpus
- Genomic Selection for Crop Improvement (2017, unknown): “Numerous studies in both plant and animal breeding systems have shown that prediction accuracy suffers when training populations are not related to the target population (Pszczola et al. 2012; Windhausen et al. 2012; Ly et al. 2013; Technow et al. 2013; Albrecht et al. 2014; Lorenz and Smith 2015).”
- Comparing genomic selection and marker-assisted selection for Fusarium head blight resistance in wheat (Triticum aestivum L.) (2016, methodology): “Clusters were obtained in JMP Pro 12 (SAS Institute 2015b) using the k-means clustering algorithm (Hartigan and Wong 1979) on marker data (Ly et al. 2013; Arruda et al. 2015).”