Genomic selection and genetic gain for nut yield in an Australian macadamia breeding population

• Published in: BMC genomics 2021-05, Vol.22 (1), p.370-370, Article 370
• Main Authors: O'Connor, Katie M, Hayes, Ben J, Hardner, Craig M, Alam, Mobashwer, Henry, Robert J, Topp, Bruce L
• Format: Article
• Language: English
• Subjects: Accuracy; Agricultural estimating and reporting; Agricultural production; Agricultural research; Breeding methods; Candidates; Crop yield; Crop yields; Crops; Cultivars; Fruit tree; Fruits; Genetic aspects; Genetic crosses; Genome-based prediction; Genomes; Genomics; Genotypes; Genotyping; Heritability; Horticulture; Macadamia nut; Methods; Nuts; Phenotype; Plant breeding; Population; Population genetics; Populations; Predictions; Production processes; Progeny; Single-nucleotide polymorphism; Stability; Trees
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/s12864-021-07694-z

Improving yield prediction and selection efficiency is critical for tree breeding. This is vital for macadamia trees with the time from crossing to production of new cultivars being almost a quarter of a century. Genomic selection (GS) is a useful tool in plant breeding, particularly with perennial trees, contributing to an increased rate of genetic gain and reducing the length of the breeding cycle. We investigated the potential of using GS methods to increase genetic gain and accelerate selection efficiency in the Australian macadamia breeding program with comparison to traditional breeding methods. This study evaluated the prediction accuracy of GS in a macadamia breeding population of 295 full-sib progeny from 32 families (29 parents, reciprocals combined), along with a subset of parents. Historical yield data for tree ages 5 to 8 years were used in the study, along with a set of 4113 SNP markers. The traits of focus were average nut yield from tree ages 5 to 8 years and yield stability, measured as the standard deviation of yield over these 4 years. GBLUP GS models were used to obtain genomic estimated breeding values for each genotype, with a five-fold cross-validation method and two techniques: prediction across related populations and prediction across unrelated populations. Narrow-sense heritability of yield and yield stability was low (h  = 0.30 and 0.04, respectively). Prediction accuracy for yield was 0.57 for predictions across related populations and 0.14 when predicted across unrelated populations. Accuracy of prediction of yield stability was high (r = 0.79) for predictions across related populations. Predicted genetic gain of yield using GS in related populations was 474 g/year, more than double that of traditional breeding methods (226 g/year), due to the halving of generation length from 8 to 4 years. The results of this study indicate that the incorporation of GS for yield into the Australian macadamia breeding program may accelerate genetic gain due to reduction in generation length, though the cost of genotyping appears to be a constraint at present.