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.
Highlights
Genomic selection (GS) was originally proposed in animal breeding[1] as a methodology to efficiently use the vast amount of marker information that was generated by new DNA technologies to predict the genetic merit of individuals traditionally estimated by best linear unbiased prediction, BLUP.[2]
The conditional distribution of marker effects of these models differ in the allocation of priors, which determine the type of shrinkage or variable selection imposed on the estimates.[32]
The highest average h2 across trials was found for average weight (AWT), and the lowest levels were detected for TMY and early marketable yield (EMY)
Summary
Genomic selection (GS) was originally proposed in animal breeding[1] as a methodology to efficiently use the vast amount of marker information that was generated by new DNA technologies to predict the genetic merit of individuals traditionally estimated by best linear unbiased prediction, BLUP.[2] GS uses a training population of individuals with known phenotypes and marker data to build a model for the prediction of performance in a population of untested individuals based on marker data. Other potential benefits can be obtained with the incorporation of GS into the breeding strategy, such as: (i) increasing selection intensity by having larger populations for prediction; (ii) reducing testing effort by eliminating partially or completely the establishment of some field experiments; (iii) better planning of crosses by more effective control of inbreeding and relatedness;[9] (iv) predicting hard-tomeasure traits using correlated traits as predictors;[10] and (v) controlled reduction of genetic diversity in the short term.[9]
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