Abstract
Crop improvement is a long-term, expensive institutional endeavor. Genomic selection (GS), which uses single nucleotide polymorphism (SNP) information to estimate genomic breeding values, has proven efficient to increasing genetic gain by accelerating the breeding process in animal breeding programs. As for crop improvement, with few exceptions, GS applicability remains in the evaluation of algorithm performance. In this study, we examined factors related to GS applicability in line development stage for grain yield using a hard red winter wheat (Triticum aestivum L.) doubled-haploid population. The performance of GS was evaluated in two consecutive years to predict grain yield. In general, the semi-parametric reproducing kernel Hilbert space prediction algorithm outperformed parametric genomic best linear unbiased prediction. For both parametric and semi-parametric algorithms, an upward bias in predictability was apparent in within-year cross-validation, suggesting the prerequisite of cross-year validation for a more reliable prediction. Adjusting the training population’s phenotype for genotype by environment effect had a positive impact on GS model’s predictive ability. Possibly due to marker redundancy, a selected subset of SNPs at an absolute pairwise correlation coefficient threshold value of 0.4 produced comparable results and reduced the computational burden of considering the full SNP set. Finally, in the context of an ongoing breeding and selection effort, the present study has provided a measure of confidence based on the deviation of line selection from GS results, supporting the implementation of GS in wheat variety development.
Highlights
As the most globally planted cereal crop, wheat is one of the world’s most important food and protein source and among the top-traded agricultural commodities internationally (United States Department of Agriculture, 2016a, b)
Genomic selection (GS), which employs single nucleotide polymorphism (SNP) markers across the entire genome to predict an individual’s performance (Meuwissen et al 2001), is a proven method to optimize and potentially accelerate the breeding process when centered on grain yield improvement; its return of investment could even be greater for the traits that are difficult or expensive to measure (Calus and Veerkamp 2011)
Evidences for GS’s potential in wheat breeding programs were demonstrated by a number of studies (e.g., Crossa et al 2010, 2011; Poland et al, 2012a; He et al 2016; Huang et al 2016; Michel et al 2016; Saint Pierre et al 2016); few of these studies were focused on the prediction the best individual’s ranking distance and taking the mean until all individuals in the population were included (18 individuals were removed from the G × E models due to the missing replicates) performance across breeding cycles or considered the application in actual breeding programs
Summary
As the demand for wheat consumption exceeds current supply (United States Department of Agriculture 2016b), it is imperative to incorporate emerging technologies into wheat breeding programs to ensure productivity meets these challenges. Genomic selection (GS), which employs single nucleotide polymorphism (SNP) markers across the entire genome to predict an individual’s performance (Meuwissen et al 2001), is a proven method to optimize and potentially accelerate the breeding process when centered on grain yield improvement; its return of investment could even be greater for the traits that are difficult or expensive to measure (Calus and Veerkamp 2011). Bernardo and Yu (2007) carried out a simulation study demonstrating the advantage of GS in comparison to marker-assisted selection in maize; de los Campos et al (2010) were the first to incorporate GS in wheat breeding by confirming that the inclusion of SNP markers resulted in improvement of GS model’s performance in predicting average grain yield.
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