Comparing strategies for genomic predictions in interspecific biparental populations: a case study with the Rubus genus

Abstract

Genomic selection (GS) is becoming increasingly widespread and applied due to the promising results obtained, cost savings in generating single nucleotide polymorphism (SNP) markers, and the development of statistical models that allow to improve the analysis robustness and accuracy. The composition and size of the training population have a major influence on GS, which poses challenges for interspecific biparental populations. Another factor is the use of different reference genomes from other species to perform SNP calling, which could make it possible to explore variability in interspecific crosses comprehensively. Late leaf rust is a disease caused by the pathogen Acculeastrum americanum, and there are reports on genetic resistance in Rubus occidentalis, which leads to the need for interspecific hybridizations, aiming to combine the fruit quality of R. idaeus with the resistance of R. occidentalis. The present study was carried out with a population of 94 interspecific raspberry hybrids. We evaluated the effect of different reference genomes on the SNP markers discovery, as well as training population optimization strategies on the accuracy of genomic predictions, namely the CV-α, leaving-one-family-out (LOFO), pairwise families, and stratified k-fold. The average predictive accuracies ranged from − 0.33 to 0.44 and We demonstrated higher prediction accuracy and more precise estimates when we combined stratified sampling to compose the training set (CV-α and k-fold stratified CV) and the panel of Unique markers. These results corroborate that genomic prediction aligned with SNP calling and training population optimization strategies can significantly increase genetic gains in interspecific biparental crosses.