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

Abstract

Background 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. Results Narrow-sense heritability of yield and yield stability was low (h2 = 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. Conclusions 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.

Multi-environment genomic prediction for soluble solids content in peach (Prunus persica)

“…However, estimates of th realised prediction accuracy in this study are unlikely to be upwardly biased by spurious correlations between unlinked markers and QTLs induced by historical selection history (sensu Yu et al, 2006;Toosi et al, 2010;Guo et al, 2014) as the breeding population was confounded with trial, and prediction accuracy was estimated for each trial-bygenomic environment. However, studies (Windhausen et al, 2012;Hickey et al, 2014;Werner et al, 2020) demonstrated that the realised prediction accuracy is driven by parental genetic values for training populations with strong family structures, such as in this and other horticultural tree crop studies (Kumar et al, 2012;O'Connor et al, 2021). While not undertaken here, training models within families (e.g., Biscarini et al, 2017) might avoid bias arising from strong family structure (Hickey et al, 2014;Werner et al, 2020), but such training might be expensive with little utility due to poor prediction accuracy in unrelated families (Riedelsheimer et al, 2013;Schopp et al, 2017).…”

Genome-Wide Association Study for Abscission Failure of Fruit Pericarps (Stick-Tights) in Wild Macadamia Germplasm

“…Alternatively, given genomic-based heritability estimates for STC were moderate, there may be evidence for many markers of small effect. As such, it may be worth investigating the use of genomicselection for stick-tight traits, as previously demonstrated for yield in macadamia [55].…”

Genome-Wide SNP Markers Accelerate Perennial Forest Tree Breeding Rate for Disease Resistance through Marker-Assisted and Genome-Wide Selection

“…The application of different methods depends on the conditions of the research in question, for example, when there is less kinship between the breeding and training population, the Bayesian method is preferred to GBLUP, and on the other hand, for traits having high heritability and traits that are controlled by a moderate number of genes, it is better to use the Bayesian A and B method, respectively [ 92 ]. Since GEBVs can be estimated in seedlings, the GS approach can play an essential role in increasing genetic gain per time unit and reducing the breeding period in forest trees [ 94 ].…”

“…In a recent study on the nut yield of Macadamia integrifolia Maiden and Betche, moderate and high prediction accuracy was obtained for yield and yield stability traits, respectively. The results of this study showed that GS can reduce the selection cycle for yield by seven years (from 21 to 14 years) and double the genetic gain for this trait [ 94 ]. Some reports showed the predictive accuracy of traits related to the growth and quality of wood in trees such as fire tree, Pinus pinaster Ait.…”

“…and Eucalyptus pellita F. Muell to be moderate [ 95 , 96 , 97 ]. High prediction accuracy increases the confidence level in selecting the top individuals and depends on various factors such as population size, nature of a trait, model type, number of markers used and the amount of LD [ 94 ]. Preliminary studies regarding the evaluation of factors affecting the GS accuracy in forest trees have shown that among them, LD between marker-trait and heritability, and the number of QTLs have the greatest and least impact on the accuracy of GS, respectively.…”