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.

Genomic regions associated with tuber traits in tetraploid potatoes and identification of superior clones for breeding purposes

“…The lower heritability estimates can be due to the underrepresentation of significant causal loci by markers (Speed et al, 2012) or dominance. Sood et al (2020) showed that narrow-sense heritability was moderate for eye depth, average tuber weight, and average tubers per plant. In our study, we obtained similar results.…”

Optimal implementation of genomic selection in clone breeding programs - exemplified in potato: I. Effect of selection strategy, implementation stage, and selection intensity on short-term genetic gain

“…Several empirical studies have explored the potential of implementing GS in potato breeding for different traits by determining the prediction accuracy (Slater et al, 2016; Sverrisdóttir et al, 2017; Enciso-Rodriguez et al, 2018; Endelman et al, 2018; Stich and Van Inghelandt, 2018; Sverrisdóttir et al, 2018; Caruana et al, 2019; Byrne et al, 2020; Gemenet et al, 2020; Sood et al, 2020; Wilson et al, 2021). Different degrees of prediction accuracies from low to high depending on the studied traits have been reported, which could be caused by the different genetic architectures, prediction models, but also the considered genetic material.…”

TubAR: an R Package for Quantifying Tuber Shape and Skin Traits from Images

“…The more accurate the information used in breeder decisions, the more effective selection can be. This is particularly important as potato breeders begin implementing genomic selection cycles, where inaccurate predictions can drive populations in the wrong direction (Caruana et al 2019;Enciso-Rodriguez et al 2018;Endelman et al 2018;Gemenet et al 2020;Habyarimana et al 2017;Selga et al 2021;Sood et al 2020;Stich and Van Inghelandt 2018;Sverrisdóttir et al 2017;Sverrisdóttir et al 2018).…”