Prospects and Potential Uses of Genomic Prediction of Key Performance Traits in Tetraploid Potato.

Benjamin Stich,Delphine Van Inghelandt

doi:10.3389/fpls.2018.00159

Abstract

Genomic prediction is a routine tool in breeding programs of most major animal and plant species. However, its usefulness for potato breeding has not yet been evaluated in detail. The objectives of this study were to (i) examine the prospects of genomic prediction of key performance traits in a diversity panel of tetraploid potato modeling additive, dominance, and epistatic effects, (ii) investigate the effects of size and make up of training set, number of test environments and molecular markers on prediction accuracy, and (iii) assess the effect of including markers from candidate genes on the prediction accuracy. With genomic best linear unbiased prediction (GBLUP), BayesA, BayesCπ, and Bayesian LASSO, four different prediction methods were used for genomic prediction of relative area under disease progress curve after a Phytophthora infestans infection, plant maturity, maturity corrected resistance, tuber starch content, tuber starch yield (TSY), and tuber yield (TY) of 184 tetraploid potato clones or subsets thereof genotyped with the SolCAP 8.3k SNP array. The cross-validated prediction accuracies with GBLUP and the three Bayesian approaches for the six evaluated traits ranged from about 0.5 to about 0.8. For traits with a high expected genetic complexity, such as TSY and TY, we observed an 8% higher prediction accuracy using a model with additive and dominance effects compared with a model with additive effects only. Our results suggest that for oligogenic traits in general and when diagnostic markers are available in particular, the use of Bayesian methods for genomic prediction is highly recommended and that the diagnostic markers should be modeled as fixed effects. The evaluation of the relative performance of genomic prediction vs. phenotypic selection indicated that the former is superior, assuming cycle lengths and selection intensities that are possible to realize in commercial potato breeding programs.

Highlights

Despite the important role of potato for securing world-wide human nutrition (FAOSTAT, 2015), potato breeding realized in the last 50 years annual gains from selection that were considerably lower than those realized for other crop species (Douches et al, 1996)
As described in detail by Pajerowska-Mukhtar et al (2009), both subsets were evaluated in 3 years each at one location for the area under disease progress curve (AUDPC) after Phytophthora infestans infection and for plant maturity (PM)
For the PIN184 population, which consists of 184 tetraploid elite potato clones, phenotypic information on four quantitative traits has been assessed across six location*year combinations

Summary

Introduction

Despite the important role of potato for securing world-wide human nutrition (FAOSTAT, 2015), potato breeding realized in the last 50 years annual gains from selection that were considerably lower than those realized for other crop species (Douches et al, 1996). Many quantitative trait loci (QTL) have been identified (for review see Gebhardt et al, 2014), the impact of marker-assisted selection for improving truly quantitative traits in potato breeding is limited (Slater et al, 2016). This is attributed to the low proportion of variance explained by most of the identified QTL as well as the fact that many identified QTL are specific to a particular phenotyping environment or genetic background

Objectives

Methods

Results