The Value of Expanding the Training Population to Improve Genomic Selection Models in Tetraploid Potato

Abstract

Genomic selection (GS) is becoming increasingly applicable to crops as the genotyping costs continue to decrease, which makes it an attractive alternative to traditional selective breeding based on observed phenotypes. With genome-wide molecular markers, selection based on predictions from genotypes can be made in the absence of direct phenotyping. The reliability of predictions depends strongly on the number of individuals used for training the predictive algorithms, particularly in a highly genetically diverse organism such as potatoes; however, the relationship between the individuals also has an enormous impact on prediction accuracy. Here we have studied genomic prediction in three different panels of potato cultivars, varying in size, design, and phenotypic profile. We have developed genomic prediction models for two important agronomic traits of potato, dry matter content and chipping quality. We used genotyping-by-sequencing to genotype 1,146 individuals and generated genomic prediction models from 167,637 markers to calculate genomic estimated breeding values with genomic best linear unbiased prediction. Cross-validated prediction correlations of 0.75–0.83 and 0.39–0.79 were obtained for dry matter content and chipping quality, respectively, when combining the three populations. These prediction accuracies were similar to those obtained when predicting performance within each panel. In contrast, but not unexpectedly, predictions across populations were generally lower, 0.37–0.71 and 0.28–0.48 for dry matter content and chipping quality, respectively. These predictions are not limited by the number of markers included, since similar prediction accuracies could be obtained when using merely 7,800 markers (<5%). Our results suggest that predictions across breeding populations in tetraploid potato are presently unreliable, but that individual prediction models within populations can be combined in an additive fashion to obtain high quality prediction models relevant for several breeding populations.

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

“…Although disease resistance is expected to show high prediction accuracy as observed for late blight (Enciso‐Rodriguez et al., 2018; Stich & Van Inghelandt, 2018), low prediction accuracy for early blight resistance despite moderate heritability estimate is consonant with the observation that for early blight the relationship between expected and observed values was especially low due to inconsistency in the phenotypic score of the disease resistance measured for the two populations (progenies and cultivars). Finally, moderate accuracies were observed for specific gravity and fry colour, which were comparable to other studies (Endelman et al., 2018; Sverrisdóttir et al., 2018).…”

“…The level of prediction accuracy for different traits in both the scenarios were similar to previous studies (Caruana et al., 2019; Enciso‐Rodriguez, Douches, Lopez‐Cruz, Coombs, & de Los Campos, 2018; Endelman et al., 2018; Slater et al., 2016; Stich & Van Inghelandt, 2018; Sverrisdóttir et al., 2017; Sverrisdóttir et al., 2018) on genomic selection in potato, except plant maturity and disease resistance. Stich and Van Inghelandt (2018) observed moderate (0.5) to high (0.8) cross prediction accuracies for six key performance traits including tuber yield using GBLUP and three Bayesian approaches, and suggested the use of Bayesian methods for oligogenic traits.…”

“…As a further challenge to plant breeders, shrinking land resources due to population growth and climate change add another layer of complexity to meet the future productivity of new cultivars. Accelerating the genetic gain in potato using molecular tools is of great interest to potato breeders and for future global food security (Caruana et al, 2019;Endelman et al, 2018;Gebhardt, 2013;Slater et al, 2018;Slater, Cogan, Forster, Hayes, & Daetwyler, 2016;Slater, Wilson, Cogan, Forster, & Hayes, 2014b;Sverrisdóttir et al, 2018). Since the sequencing of the potato reference genome in 2011 (Potato Genome Sequencing Consortium 2011), the application of molecular marker techniques in potato breeding has become more accessible.…”

Comparison of Representative and Custom Methods of Generating Core Subsets of a Carrot Germplasm Collection

“…The quality of a training population is determined by the degree of relatedness between members of the training population and members of the population to be predicted (Clark et al, 2012), as well as the degree of genetic and phenotypic diversity of the prediction population captured by the training population (Sverrisdóttir et al, 2018). Information on both phenotypic and genetic diversity was used in multiple-factor analysis.…”

A Bioinformatics Pipeline to Identify a Subset of SNPs for Genomics-Assisted Potato Breeding

“…Recently, several reports indicate the applicability of GEBVs as a method of selection for potato breeding. High accuracies of GEBV prediction have been achieved for host plant resistance to late blight and common scab [17], and tuber quality traits, such as starch content or chipping after frying [18,19]. They are based on large training populations to capture the genetic diversity of elite germplasm.…”