Selection strategies to improve yield in macadamia using component traits and genomics

Abstract

Macadamias (Macadamia integrifolia, M. tetraphylla, and their hybrids) are native to the east coast of Australia, and are grown commercially around the world for their high quality edible nut kernel. Breeding new cultivars for high nut yield is a lengthy and laborious process that can take over two decades. Evaluations are time-consuming due to the long juvenile period of four or more years, and the low correlation between young and mature tree yield means that at least eight years of evaluations are required. Furthermore, yield has low heritability, and the large tree size necessitates low field planting density, and thus increases land use and evaluation costs. It is hypothesised that genetic gain for yield may be increased, compared to traditional breeding approaches, through the use of strategies including (i) indirect selection using yield component traits, (ii) marker-assisted selection and (iii) genomic selection. This thesis employed an experimental population of 295 seedling progeny and their 29 parents, at four sites across southeast Queensland, that were genotyped for 4,113 SNPs and 16,171 silicoDArT markers detected using Diversity Arrays Technology (DArT) methods. Population structure, genetic diversity and linkage disequilibrium (LD) between SNP markers was quantified to inform subsequent genomics analysis. LD decay was initially rapid at short distances (r 2 = 0.124 for SNPs within 1 kb of each other), but low level LD persisted for long distances. The seedling population was relatively genetically diverse (HE = 0.255), and very similar in diversity to that of the 29 parents (0.250). Furthermore, progeny with M. integrifolia x M. tetraphylla ancestry were more genetically diverse (HE = 0.278) than M. integrifolia seedlings (HE = 0.189). Progeny were moderately differentiated and clustered into three distinct groups, which represented M. integrifolia germplasm and two hybrid groups. Flowering and nut characteristics, tree growth and yield were measured on each tree. Estimations of trait heritability and genetic correlations between each component trait and yield were used to calculate selection efficiency of indirectly selecting for yield using component traits. Kernel recovery, an economically important trait, had high heritability (h 2 = 0.76) but was negatively genetically correlated with yield (rg =-0.27). Trunk circumference correlated strongly with yield (rg = 0.72) and was moderately heritable (h 2 = 0.44); however, a breeding aim is to reduce tree size without compromising yield. No component traits were appropriate or effective for indirectly selecting for high yield.

Phenotypic Characterisation for Growth and Nut Characteristics Revealed the Extent of Genetic Diversity in Wild Macadamia Germplasm

“…Based on Model 1, the variance component analysis revealed that genetic factors played a key role in the variation of traits of this study. The broad-sense heritability for tree height (0.60), trunk circumference (0.54), and canopy width (0.51) were higher than reported in previous studies of cultivars and breeding progeny [36,37,40]. This may partly be due to the inflation of genetic variance due to the evaluation of accessions from multiple species which differ in average performance.…”

“…These results suggested that the genetic improvement for nut size could contribute to the profitability of the growers and processors by increasing kernel yield. In another study on mature age breeding progeny, O'Connor [40] identified a strong genetic correlation between individual nut weight and nut-in-shell yield (r = 0.40) and indicated that larger nuts can be a major genetic determinant of increased yield. Nut weight also correlated positively with shell weight (0.95) and correlated negatively with kernel recovery (−0.28).…”

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

“…The implementation of GS in macadamia may include prediction and deployment across environments. The current study population had limited replication of genotypes across environments and did not include G x E interactions in prediction models as preliminary results found no evidence of G x E in this experimental material [48]. Previous studies have found some evidence that G x E may affect macadamia yield [5,12].…”

“…Jia and Jannink [63] found that prediction accuracy was increased for a trait with low heritability by including information for a correlated trait with high heritability. Estimates of heritability and genetic correlations between yield and various component traits have been made [48] and, thus, this information could be used to inform multi-trait GS. Distinctions can also be made between linked QTLs (linkage between multiple QTLs affecting different traits) and pleiotropic QTL (one gene affecting multiple traits), using multi-trait methods, like those employed by Bolormaa, Pryce [64].…”

“…GBLUP models were used to estimate GEBVs for each tree using ASReml-R [68]. Preliminary analyses indicated no significant difference in prediction accuracy between additive genomic effects and sites [48], and thus G x E was not included in analyses.…”