Interrelations between productive, vegetative, and fruit quality traits in Tahiti acid lime through Bayesian inference

ABSTRACT: Although the fruit yield has a core importance in Tahiti acid lime breeding programs, other traits stand out among the quality fruit and vegetative traits as ones that still need to be improved in selection of superior genotypes. Appling efficient tools aiming selection, such as the Bayesian inference, becomes an alternative in perennial crops. This study applied Bayesian inference in the genetic evaluation of Tahiti acid lime genotypes and estimated the interrelation between vegetative, productive and fruit quality traits. Twenty-four acid lime genotypes were evaluated for number of fruits, fruit yield, canopy volume, stem diameter, soluble solids content, shell thickness, and juice yield traits. The genotypic values were estimated through Bayesian inference and models with different residual structure were tested via deviance information criterion. Pearson’s correlation and the path analysis were estimated, removing the multicollinearity effect. The Bayesian inference estimates genotypic values with high selective accuracy. The correlations obtained between traits from different groups can be useful in selection strategies for improvement of Tahiti acid lime. The Bayesian inference demonstrated to be an important tool and should be considered in perennial breeding programs.

High-density linkage map construction and identification of loci regulating fruit quality traits in blueberry

Fruit quality traits play a significant role in consumer preferences and consumption in blueberry (Vaccinium corymbosum L). The objectives of this study were to construct a high-density linkage map and to identify the underlying genetic basis of fruit quality traits in blueberry. A total of 287 F1 individuals derived from a cross between two southern highbush blueberry cultivars, ‘Reveille’ and ‘Arlen’, were phenotyped over three years (2016–2018) for fruit quality-related traits, including titratable acidity, pH, total soluble solids, and fruit weight. A high-density linkage map was constructed using 17k single nucleotide polymorphisms markers. The linkage map spanned a total of 1397 cM with an average inter-loci distance of 0.08 cM. The quantitative trait loci interval mapping based on the hidden Markov model identified 18 loci for fruit quality traits, including seven loci for fruit weight, three loci for titratable acidity, five loci for pH, and three loci for total soluble solids. Ten of these loci were detected in more than one year. These loci explained phenotypic variance ranging from 7 to 28% for titratable acidity and total soluble solid, and 8–13% for pH. However, the loci identified for fruit weight did not explain more than 10% of the phenotypic variance. We also reported the association between fruit quality traits and metabolites detected by Proton nuclear magnetic resonance analysis directly responsible for these fruit quality traits. Organic acids, citric acid, and quinic acid were significantly (P < 0.05) and positively correlated with titratable acidity. Sugar molecules showed a strong and positive correlation with total soluble solids. Overall, the study dissected the genetic basis of fruit quality traits and established an association between these fruit quality traits and metabolites.

Analysis of phenotypic diversity of apricot (Prunus armeniaca L.) accessions from Jammu and Kashmir, India

Apricot (Prunus armeniaca L.) is a world-wide highly appreciated fruit, with its attractive colour, soft texture and typical flavour. In the current investigation, 68 apricot accessions collected from Jammu and Kashmir, India were analysed to determine the measure of variation using 12 qualitative and 16 quantitative traits with an aim to identify superior apricot accessions with excellent fruit quality traits. High phenotypic variability was observed among the studied apricot accessions with significant differences among most of the qualitative and quantitative traits. Fruit-related characteristics including fruit weight, fruit length, fruit ratio, fruit firmness, fruit shape, fruit suture, stone weight, kernel weight and stone shape were the most diverse with a high coefficient of variation (>30%). One way analysis of variance showed significant differences (P < 0.0001) among all the quantitative traits. Significant positive and negative correlations were observed between all the agronomically important fruit quality traits. The principal component analysis (PCA) revealed that 75.34% of the variability was defined by the first eight components. The unweighted pair group method with arithmetic mean (UPGMA) dendrogram, based on all traits measured grouped the accessions into two main clusters with several sub-clusters. Both UPGMA dendrogram and PCA scatter plot formed a cluster of 14 accessions, having the highest values regarding most important fruit quality traits such as fruit weight, fruit length and width, fruit ratio, stone weight and kernel weight can be treated as potentially superior accessions. These accessions can be used directly for cultivation and in future apricot breeding programmes. The present findings are promising for genetic resource management, cultivar improvement and commercial applications of apricot in Jammu and Kashmir, India.