Hardner, C. | Search bioRxiv

bioRxiv2026

Genomic and pedigree-based approaches to predict parental breeding values for nut and kernel traits in almond (Prunus dulcis Mill. D. A. Webb)

The self-incompatibility, perennial growth habit, large tree size, and long juvenility present challenges in applying traditional breeding approaches in almond (Prunus dulcis Mill. D. A. Webb). Moreover, nut and kernel traits in almond are mainly controlled by a large number of small-effect quantitative trait loci (QTLs) and improving complex traits through conventional breeding approaches is slow and often inefficient. Genome-wide selection represents a promising strategy to enhance the efficiency of cultivar identification and selection of superior parents in almond breeding programs by estimating the breeding values (BVs) at early maturity. The main aim of this study was to implement genomic (GBLUP) and pedigree-based (ABLUP) prediction approaches to estimate BVs to identify the superior parental candidates for improving nut and kernel traits in almond. Here, we estimated BVs for nine traits that are commonly used in the primary evaluation stage of the almond breeding using genomic data from 61 parents and phenotypic data of 15,281 progeny derived from 205 unique families. Breeding values obtained from both approaches showed a strong correlation (r [≥] 0.94) for all traits except shell seal (r = 0.87). The population structure analysis conducted using high-quality 90K single nucleotide polymorphisms (SNPs) indicated clear separation of the Californian, European and some old Australian almond cultivars, with considerable admixture across some cultivars. Following further validation, both prediction approaches could be useful in early identification of superior candidates. The slightly higher breeding values obtained using the GBLUP compared to the ABLUP approach suggest that accounting for within-family variations and realised genomic relationships can enhance prediction accuracy, reliability, and overall genomic prediction performance in almond.

genetics↗

bioRxiv2017

Genomic heritability estimates in sweet cherry reveal non-additive genetic variance is relevant for industry-prioritized traits

BackgroundSweet cherry is consumed widely across the world and provides substantial economic benefits in regions where it is grown. While cherry breeding has been conducted in the Pacific Northwest for over half a century, little is known about the genetic architecture of important traits. We used a genome-enabled mixed model to predict the genetic performance of 505 individuals for 32 phenological, disease response and fruit quality traits evaluated in the RosBREED sweet cherry crop data set. Genome-wide predictions were estimated using a repeated measures model for phenotypic data across 3 years, incorporating additive, dominance and epistatic variance components. Genomic relationship matrices were constructed with high-density SNP data and were used to estimate relatedness and account for incomplete replication across years.\n\nResultsHigh broad-sense heritabilities of 0.83, 0.77, and 0.75 were observed for days to maturity, firmness, and fruit weight, respectively. Epistatic variance exceeded 40% of the total genetic variance for maturing timing, firmness and powdery mildew response. Dominance variance was the largest for fruit weight and fruit size at 34% and 27%, respectively. Omission of non-additive sources of genetic variance from the genetic mode resulted in inflation of narrow-sense heritability but minimally influenced prediction accuracy of genetic values in validation. Predicted genetic rankings of individuals from single-year models were inconsistent across years, likely due to incomplete sampling of the population genetic variance.\n\nConclusionsPredicted breeding values and genetic values a measure revealed many high-performing individuals for use as parents and the most promising selections to advance for cultivar release consideration, respectively. This study highlights the importance of using the appropriate genetic model for calculating breeding values to avoid inflation of expected parental contribution to genetic gain. The genomic predictions obtained will enable breeders to efficiently leverage the genetic potential of North American sweet cherry germplasm by identifying high quality individuals more rapidly than with phenotypic data alone.

genetics↗