Carolina Font i Forcada

Population structure and marker–trait associations for pomological traits in peach and nectarine cultivars

Marker–trait associations based on populations from controlled crosses have been established in peach using markers mapped on the peach consensus map. In this study, we explored the utility of unstructured populations for association mapping to determine useful marker–trait associations in peach/nectarine cultivars. We used 94 peach cultivars representing local Spanish and modern cultivars from international breeding programs that are maintained at the Experimental Station of Aula Dei, Spain. This collection was characterized for pomological traits and was screened with 40 SSR markers that span the peach genome. Population structure analysis using STRUCTURE software identified two subpopulations, the local and modern cultivars, with admixture within both groups. The local Spanish cultivars were somewhat less diverse than modern cultivars. Marker–trait associations were determined in TASSEL with and without modelling coefficient of membership (Q) values as covariates. The results showed significant associations with pomological traits. We chose three markers on LG4 because of their proximity to the endoPG locus (freestone–melting flesh) that strongly affects pomological traits. Two genotypes of BPPCT015 marker showed significant associations with harvest date, flavonoids and sorbitol. Also, two genotypes of CPPCT028 showed associations with harvest date, total phenolics, RAC, and total sugars. Finally, two genotypes of endoPG1 showed associations with flesh firmness and total sugars. The analysis of linkage disequilibrium (LD) revealed a high level of LD up to 20xa0cM, and decay at farther distances. Therefore, association mapping could be a powerful tool for identifying marker–trait associations and would be useful for marker-assisted selection in peach breeding.

Phenotypic diversity among local Spanish and foreign peach and nectarine [Prunus persica (L.) Batsch] accessions

Phenotypic data for tree and fruit characteristics was collected over three consecutive years from a germplasm collection of 94 peach and nectarine accessions representing both traditional Spanish as well as foreign cultivars with widespread global plantings. All accessions were grown at the Experimental Station of Aula Dei located in the Ebro Valley (Northern Spain, Zaragoza) under a Mediterranean climate. Tree traits evaluated included bloom and harvest date, vigor, yield, yield efficiency and flower and leaf characteristics. Fruit traits included fresh weight, firmness, soluble solids, titratable acidity, levels of individual soluble sugars (sucrose, glucose, fructose and sorbitol), vitamin C, total phenolics, flavonoids, anthocyanins, relative antioxidant capacity and ripening index. Extensive variability was observed for most qualitative and quantitative traits with significant correlations identified between many traits. While the traditional Spanish accessions demonstrated good adaptability to the northern Spain evaluation site, opportunities for continued improvement in tree and fruit quality traits were demonstrated by an extensive phenotypic variability within the germplasm collection.

Mapping quantitative trait loci for kernel composition in almond

Background: Almond breeding is increasingly taking into account kernel quality as a breeding objective. Information on the parameters to be considered in evaluating almond quality, such as protein and oil content, as well as oleic acid and tocopherol concentration, has been recently compiled. The genetic control of these traits has not yet been studied in almond, although this information would improve the efficiency of almond breeding programs. Results: A map with 56 simple sequence repeat or microsatellite (SSR) markers was constructed for an almond population showing a wide range of variability for the chemical components of the almond kernel. A total of 12 putative quantitative trait loci (QTL) controlling these chemical traits have been detected in this analysis, corresponding to seven genomic regions of the eight almond linkage groups (LG). Some QTL were clustered in the same region or shared the same molecular markers, according to the correlations already found between the chemical traits. The logarithm of the odds (LOD) values for any given trait ranged from 2.12 to 4.87, explaining from 11.0 to 33.1 % of the phenotypic variance of the trait. Conclusions: The results produced in the study offer the opportunity to include the new genetic information in almond breeding programs. Increases in the positive traits of kernel quality may be looked for simultaneously whenever they are genetically independent, even if they are negatively correlated. We have provided the first genetic framework for the chemical components of the almond kernel, with twelve QTL in agreement with the large number of genes controlling their metabolism.BackgroundAlmond breeding is increasingly taking into account kernel quality as a breeding objective. Information on the parameters to be considered in evaluating almond quality, such as protein and oil content, as well as oleic acid and tocopherol concentration, has been recently compiled. The genetic control of these traits has not yet been studied in almond, although this information would improve the efficiency of almond breeding programs.ResultsA map with 56 simple sequence repeat or microsatellite (SSR) markers was constructed for an almond population showing a wide range of variability for the chemical components of the almond kernel. A total of 12 putative quantitative trait loci (QTL) controlling these chemical traits have been detected in this analysis, corresponding to seven genomic regions of the eight almond linkage groups (LG). Some QTL were clustered in the same region or shared the same molecular markers, according to the correlations already found between the chemical traits. The logarithm of the odds (LOD) values for any given trait ranged from 2.12 to 4.87, explaining from 11.0 to 33.1u2009% of the phenotypic variance of the trait.ConclusionsThe results produced in the study offer the opportunity to include the new genetic information in almond breeding programs. Increases in the positive traits of kernel quality may be looked for simultaneously whenever they are genetically independent, even if they are negatively correlated. We have provided the first genetic framework for the chemical components of the almond kernel, with twelve QTL in agreement with the large number of genes controlling their metabolism.BACKGROUNDnAlmond breeding is increasingly taking into account kernel quality as a breeding objective. Information on the parameters to be considered in evaluating almond quality, such as protein and oil content, as well as oleic acid and tocopherol concentration, has been recently compiled. The genetic control of these traits has not yet been studied in almond, although this information would improve the efficiency of almond breeding programs.nnnRESULTSnA map with 56 simple sequence repeat or microsatellite (SSR) markers was constructed for an almond population showing a wide range of variability for the chemical components of the almond kernel. A total of 12 putative quantitative trait loci (QTL) controlling these chemical traits have been detected in this analysis, corresponding to seven genomic regions of the eight almond linkage groups (LG). Some QTL were clustered in the same region or shared the same molecular markers, according to the correlations already found between the chemical traits. The logarithm of the odds (LOD) values for any given trait ranged from 2.12 to 4.87, explaining from 11.0 to 33.1u2009% of the phenotypic variance of the trait.nnnCONCLUSIONSnThe results produced in the study offer the opportunity to include the new genetic information in almond breeding programs. Increases in the positive traits of kernel quality may be looked for simultaneously whenever they are genetically independent, even if they are negatively correlated. We have provided the first genetic framework for the chemical components of the almond kernel, with twelve QTL in agreement with the large number of genes controlling their metabolism.

Agronomical Parameters, Sugar Profile and Antioxidant Compounds of "Catherine" Peach Cultivar Influenced by Different Plum Rootstocks

The influence of seven plum rootstocks (Adesoto, Monpol, Montizo, Puebla de Soto 67 AD, PM 105 AD, St. Julien GF 655/2 and Constantí 1) on individual and total sugars, as well as on antioxidant content in fruit flesh of “Catherine” peaches, was evaluated for three years. Agronomical and basic fruit quality parameters were also determined. At twelve years after budding, significant differences were found between rootstocks for the different agronomic and fruit quality traits evaluated. The Pollizo plum rootstocks Adesoto and PM 105 AD seem to induce higher sweetness to peach fruits, based on soluble solids content, individual (sucrose, fructose and sorbitol) and total sugars. A clear tendency was also observed with the rootstock Adesoto, inducing the highest content of phenolics, flavonoids, vitamin C and relative antioxidant capacity (RAC). Thus, the results of this study demonstrate the significant effect of rootstock on the sugar profile and phytochemical characteristics of peach fruits. In addition, this work shows the importance of the sugar profile, because specific sugars play an important role in peach flavour quality, as well as the studied phytochemical compounds when looking for high quality peaches with enhanced health properties.

Genetic analysis for physical nut traits in almond

Almond breeding is increasingly taking into account kernel quality as a breeding objective. Although information on nut and kernel physical parameters involved in almond quality has already been compiled, the genetic control of these traits has not been studied. This genetic information would improve the efficacy of almond breeding programs. A linkage map with 56 simple-sequence repeat markers was constructed for the “Vivot” × “Blanquerna” almond population showing a wide range of variability for the physical parameters of nut and kernel. A total of 14 putative quantitative trait loci (QTLs) controlling these physical traits were detected in the current study, corresponding to six genomic regions of the eight almond linkage groups (LG). Some QTLs are colocated in the same region or shared the same molecular markers, in a manner that reflects the correlations between the physical traits, as well as with the chemical components of the almond kernel. The limit of detection values for any given trait ranged from 2.06 to 5.17, explaining between 13.0 and 44.0xa0% of the phenotypic variance of the trait. This new genetic information needs to be taken into account when breeding for physical traits in almond. Increases in the positive quality traits, both physical and chemical, need to be considered simultaneously whenever they are genetically independent, even if they are negatively correlated. This is the first complete genetic framework map for physical components of almond nut and kernel, with 14 putative QTLs associated with a large number of parameters controlling physical traits in almond.Almond breeding is increasingly taking into account kernel quality as a breeding objective. Although information on nut and kernel physical parameters involved in almond quality has already been compiled, the genetic control of these traits has not been studied. This genetic information would improve the efficacy of almond breeding programs. A linkage map with 56 simple-sequence repeat markers was constructed for the “Vivot” × “Blanquerna” almond population showing a wide range of variability for the physical parameters of nut and kernel. A total of 14 putative quantitative trait loci (QTLs) controlling these physical traits were detected in the current study, corresponding to six genomic regions of the eight almond linkage groups (LG). Some QTLs are colocated in the same region or shared the same molecular markers, in a manner that reflects the correlations between the physical traits, as well as with the chemical components of the almond kernel. The limit of detection values for any given trait ranged from 2.06 to 5.17, explaining between 13.0 and 44.0xa0% of the phenotypic variance of the trait. This new genetic information needs to be taken into account when breeding for physical traits in almond. Increases in the positive quality traits, both physical and chemical, need to be considered simultaneously whenever they are genetically independent, even if they are negatively correlated. This is the first complete genetic framework map for physical components of almond nut and kernel, with 14 putative QTLs associated with a large number of parameters controlling physical traits in almond.