An experimental validation of genomic selection in octoploid strawberry. (11th January 2017)

• Record Type: Journal Article
• Authors: Gezan, Salvador A; Osorio, Luis F; Verma, Sujeet; Whitaker, Vance M
• Is Part Of: Horticulture research. Volume 4(2017)
• Publication Date: 2017-01-11
• Subjects: Plant breeding -- Plant hybridization -- Rosaceae genomics; Horticulture -- Research -- Periodicals; 635.072
• DOI: 10.1038/hortres.2016.70
• Languages: English
• ISSNs: 2052-7276

Abstract

Abstract: The primary goal of genomic selection is to increase genetic gains for complex traits by predicting performance of individuals for which phenotypic data are not available. The objective of this study was to experimentally evaluate the potential of genomic selection in strawberry breeding and to define a strategy for its implementation. Four clonally replicated field trials, two in each of 2 years comprised of a total of 1628 individuals, were established in 2013–2014 and 2014–2015. Five complex yield and fruit quality traits with moderate to low heritability were assessed in each trial. High-density genotyping was performed with the Affymetrix Axiom IStraw90 single-nucleotide polymorphism array, and 17 479 polymorphic markers were chosen for analysis. Several methods were compared, including Genomic BLUP, Bayes B, Bayes C, Bayesian LASSO Regression, Bayesian Ridge Regression and Reproducing Kernel Hilbert Spaces. Cross-validation within training populations resulted in higher values than for true validations across trials. For true validations, Bayes B gave the highest predictive abilities on average and also the highest selection efficiencies, particularly for yield traits that were the lowest heritability traits. Selection efficiencies using Bayes B for parent selection ranged from 74% for average fruit weight to 34% for early marketable yield. A breeding strategy is proposed in which advanced selection trials are utilized as training populations and in which genomic selection can reduce the breeding cycle from 3 to 2 years for a subset of untested parents based on their predicted genomic breeding values. Abstract : Crop improvement: Speeding up strawberry selection An exploration of multiple methods for analysing genomic data suggests a strategy for accelerating strawberry breeding. It is becoming widely accepted that 'genomic selection' can be more efficient than growing crops until mature to determine their commercially significant traits, particularly for perennial horticultural crops. However, there are multiple methods for predicting traits of interest from genetic data. Vance Whitaker and colleagues at the University of Florida tested several methods on a population of over 1, 600 strawberry plants, using almost 17, 500 genetic markers and six traits including total yield and fruit weight. Their results detail the accuracy of the various methods at predicting a particular plant's characteristics from its genome. They suggest this will help to significantly reduce the time needed for selection of complex traits in strawberries.