Making the most of all data: Combining non-genotyped and genotyped potato individuals with HBLUP

Abstract

Using genomic information to predict phenotypes can improve the accuracy of estimated breeding values and can potentially increase genetic gain over conventional breeding. In this study, we investigated the prediction accuracies achieved by best linear unbiased prediction (BLUP) for nine potato phenotypic traits using three types of relationship matrices pedigree ABLUP, genomic GBLUP, and a hybrid matrix (H) combining pedigree and genomic information (HBLUP). Deep pedigree information was available for >3000 different potato breeding clones evaluated over four years. Genomic relationships were estimated from >180,000 informative SNPs generated using a genotyping-by-sequencing transcriptome (GBS-t) protocol for 168 cultivars, many of which were parents of clones. Two validation scenarios were implemented, namely "Genotyped Cultivars Validation" (a subset of genotyped lines as validation set) and "Non-genotyped 2009 Progenies Validation". Most of the traits showed moderate to high narrow sense heritabilities (range 0.22-0.72). In the Genotyped Cultivars Validation, HBLUP outperformed ABLUP on prediction accuracies for all traits except early blight, and outperformed GBLUP for most of the traits except tuber shape, tuber eye depth and boil after-cooking darkening. This is evidence that the in-depth relationship within the H matrix could potentially result in better prediction accuracy in comparison to using A or G matrix individually. The prediction accuracies of the Non-genotyped 2009 Progenies Validation were comparable between ABLUP and HBLUP, varying from 0.17-0.70 and 0.18-0.69, respectively. Better prediction accuracy and less bias in prediction using HBLUP is of practical utility to breeders as all breeding material is ranked on the same scale leading to improved selection decisions. In addition, our approach provides an economical alternative to utilize historic breeding data with current genotyped individuals in implementing genomic selection.