Accuracy of genomic prediction of complex traits in sugarcane

• Published in: Theoretical and applied genetics 2021-05, Vol.134 (5), p.1455-1462
• Main Authors: Hayes, Ben J., Wei, Xianming, Joyce, Priya, Atkin, Felicity, Deomano, Emily, Yue, Jenny, Nguyen, Loan, Ross, Elizabeth M., Cavallaro, Tony, Aitken, Karen S., Voss-Fels, Kai P.
• Format: Article
• Language: English
• Subjects: Agriculture; Biochemistry; Biomedical and Life Sciences; Biotechnology; Chromosome Mapping - methods; Chromosomes, Plant - genetics; Flowering; Flowers - genetics; Flowers - growth & development; Flowers - metabolism; Gene Expression Regulation, Plant; Genetic markers; Genetics, Population; Genome, Plant; Genomics; Life Sciences; Multifactorial Inheritance; Original Article; Plant Biochemistry; Plant breeding; Plant Breeding - methods; Plant Breeding/Biotechnology; Plant Genetics and Genomics; Plant Proteins - genetics; Plant Proteins - metabolism; Pollen; Polymorphism, Single Nucleotide; Predictions; Quantitative Trait, Heritable; Saccharum - genetics; Saccharum - growth & development; Saccharum - metabolism; Single-nucleotide polymorphism; Sugarcane
• ISSN: 0040-5752; 1432-2242
• DOI: 10.1007/s00122-021-03782-6

Key message Complex traits in sugarcane can be accurately predicted using genome-wide DNA markers. Genomic single-step prediction is an attractive method for genomic selection in commercial breeding programs. Sugarcane breeding programs have achieved up to 1% genetic gain in key traits such as tonnes of cane per hectare (TCH), commercial cane sugar (CCS) and Fibre content over the past decades. Here, we assess the potential of genomic selection to increase the rate of genetic gain for these traits by deriving genomic estimated breeding values (GEBVs) from a reference population of 3984 clones genotyped for 26 K SNP. We evaluated the three different genomic prediction approaches GBLUP, genomic single step (GenomicSS), and BayesR. GenomicSS combining pedigree and SNP information from historic and recent breeding programs achieved the most accurate predictions for most traits (0.3–0.44). This method is attractive for routine genetic evaluation because it requires relatively little modification to the existing evaluation and results in breeding value estimates for all individuals, not only those genotyped. Adding information from early-stage trials added up to 5% accuracy for CCS and Fibre, but 0% for TCH, reflecting the importance of competition effects for TCH. These GEBV accuracies are sufficiently high that, combined with the right breeding strategy, a doubling of the rate of genetic gain could be achieved. We also assessed the flowering traits days to flowering, gender and pollen viability and found high heritabilities of 0.57, 0.78 and 0.72, respectively. The GEBV accuracies indicated that genomic selection could be used to improve these traits. This could open new avenues for breeders to manage their breeding programs, for example, by synchronising flowering time and selecting males with high pollen viability.