High density SNP mapping and QTL analysis for fruit quality characteristics in peach (Prunus persica L.)

Single nucleotide polymorphisms (SNPs) were used to construct an integrated SNP linkage map of peach (Prunus persica (L.) Batsch). A set of 1,536 SNPs were evaluated with the GoldenGate® Genotyping assay in two mapping populations, Pop-DF, and Pop-DG. After genotyping and filtering, a final set of 1,400 high quality SNPs in Pop-DF and 962 in Pop-DG with full map coverage were selected and used to construct two linkage maps with JoinMap®4.0. The Pop-DF map covered 422 cM of the peach genome and included 1,037 SNP markers, and Pop-DG map covered 369 cM and included 738 SNPs. A consensus map was constructed with 588 SNP markers placed in eight linkage groups (n?=?8 for peach), with map coverage of 454 cM and an average distance of 0.81 cM/marker site. Placements of SNPs on the “peach v1.0” physical map were compared to placement on the linkage maps and several differences were observed. Using the SNP linkage map of Pop-DG and phenotypic data collected for three harvest seasons, a QTL analysis for fruit quality traits and chilling injury symptoms was carried out with the mapped SNPs. Significant QTL effects were detected for mealiness (M) and flesh bleeding (FBL) QTLs on linkage group 4 and flesh browning (FBr) on linkage group 5. This study represents one of the first examples of QTL detection for quality traits and chilling injury symptoms using a high-density SNP map in a single peach F1 family.     

QTL mapping of fruit mineral contents provides new chances for molecular breeding of tomato nutritional traits

Key message

Agronomical characterization of a RIL population for fruit mineral contents allowed for the identification of QTL controlling these fruit quality traits, flanked by co-dominant markers useful for marker-assisted breeding.

Abstract

Tomato quality is a multi-variant attribute directly depending on fruit chemical composition, which in turn determines the benefits of tomato consumption for human health. Commercially available tomato varieties possess limited variability in fruit quality traits. Wild species, such as Solanum pimpinellifolium, could provide different nutritional advantages and can be used for tomato breeding to improve overall fruit quality. Determining the genetic basis of the inheritance of all the traits that contribute to tomato fruit quality will increase the efficiency of the breeding program necessary to take advantage of the wild species variability. A high-density linkage map has been constructed from a recombinant inbred line (RIL) population derived from a cross between tomato Solanum lycopersicum and the wild-relative species S. pimpinellifolium. The RIL population was evaluated for fruit mineral contents during three consecutive growing seasons. The data obtained allowed for the identification of main QTL and novel epistatic interaction among QTL controlling fruit mineral contents on the basis of a multiple-environment analysis. Most of the QTL were flanked by candidate genes providing valuable information for both tomato breeding for new varieties with novel nutritional properties and the starting point to identify the genes underlying these QTL, which will help to reveal the genetic basis of tomato fruit nutritional properties.

     

Analysing the genetic control of peach fruit quality through an ecophysiological model combined with a QTL approach

Ecophysiological models are increasingly expected to include genetic information via genotype-dependent parameters. These parameters could be considered as quantitative traits and submitted to analysis. A pre-existing ecophysiological model of fruit quality was used and the distribution of the genotypic parameters in a second backcross population derived from a clone of a wild peach (Prunus davidiana) and commercial nectarine varieties (P. persica (L.) Batsch) was analysed. The correlations between the two years of experimentation were higher for the genotypic parameters than for the quality traits commonly studied by breeders. The correlations between the genotypic parameters and the quality traits were low. Quantitative trait loci (QTLs) for the genotypic key parameters of the ecophysiological model were detected by linear regression. Co-locations of QTLs for parameters were observed as well as co-locations of QTLs for parameters and quality traits. The ecophysiological model and the results of the QTL analysis were combined by substituting each parameter in the model by the sum of QTL effects. This combined model can simulate the behaviour of genotypes carrying diverse combinations of alleles. The quality of this combined model was moderately suitable, but had some shortcomings. Improvements are suggested and further use of this combined model as a tool for breeders is discussed.     

QTL analysis of fruit quality traits in two peach intraspecific populations and importance of maturity date pleiotropic effect

Two intraspecific peach breeding populations have been used to conduct a quantitative trait locus (QTL) analysis of fruit quality traits: an F₁ from the cross Bolero (B) x OroA (O) and an F₂ from the cross Contender (C) x Ambra (A). A total of 344 Prunus simple sequence repeats (SSRs) were analyzed in B, O, C, A parents and CxA F₁ hybrid. Eight SSR were mapped for the first time in peach. A multiplex-ready polymerase chain reaction (PCR) protocol has allowed considerable time and cost saving during genotyping steps. Two maps (B map and O map) were produced for BxO population following the pseudo-test cross strategy and one for CxA. No marker could be mapped on G6 for the B map, on G4 and G8 for the O map and on G5 for the CxA map. Both populations were phenotyped over 2 years for maturity date (MD), fruit weight, external fruit skin overcolor, juice total soluble solids (SSC, Brix degree), juice titrable acidity and juice pH. Data for blooming time and flower type were scored only for BxO in 2007. All traits had a normal distribution, except for MD which was bimodal in BxO and trimodal in CxA, where it was scored as a co-dominant trait. Up to two QTLs per trait were detected in each population, and most of them were located in the same region forming clusters of QTLs, especially on G4. This is likely due to a major pleiotropic effect of MD masking the identification of other QTLs for different traits.     

Effect of population size and unbalanced data sets on QTL detection using genome-wide association mapping in barley breeding germplasm

Over the past two decades many quantitative trait loci (QTL) have been detected; however, very few have been incorporated into breeding programs. The recent development of genome-wide association studies (GWAS) in plants provides the opportunity to detect QTL in germplasm collections such as unstructured populations from breeding programs. The overall goal of the barley Coordinated Agricultural Project was to conduct GWAS with the intent to couple QTL detection and breeding. The basic idea is that breeding programs generate a vast amount of phenotypic data and combined with cheap genotyping it should be possible to use GWAS to detect QTL that would be immediately accessible and used by breeding programs. There are several constraints to using breeding program-derived phenotype data for conducting GWAS namely: limited population size and unbalanced data sets. We chose the highly heritable trait heading date to study these two variables. We examined 766 spring barley breeding lines (panel #1) grown in balanced trials and a subset of 384 spring barley breeding lines (panel #2) grown in balanced and unbalanced trials. In panel #1, we detected three major QTL for heading date that have been detected in previous bi-parental mapping studies. Simulation studies showed that population sizes greater than 384 individuals are required to consistently detect QTL. We also showed that unbalanced data sets from panel #2 can be used to detect the three major QTL. However, unbalanced data sets resulted in an increase in the false-positive rate. Interestingly, one-step analysis performed better than two-step analysis in reducing the false-positive rate. The results of this work show that it is possible to use phenotypic data from breeding programs to detect QTL, but that careful consideration of population size and experimental design are required.     

Genetic analysis and major QTL detection for maize kernel size and weight in multi-environments

Key Message

Twelve major QTL in five optimal clusters and several epistatic QTL are identified for maize kernel size and weight, some with pleiotropic will be promising for fine-mapping and yield improvement.

Abstract

Kernel size and weight are important target traits in maize (Zea mays L.) breeding programs. Here, we report a set of quantitative trait loci (QTL) scattered through the genome and significantly controlled the performance of four kernel traits including length, width, thickness and weight. From the cross V671 (large kernel) × Mc (small kernel), 270 derived F_2:3 families were used to identify QTL of maize kernel-size traits and kernel weight in five environments, using composite interval mapping (CIM) for single-environment analysis along with mixed linear model-based CIM for joint analysis. These two mapping strategies identified 55 and 28 QTL, respectively. Among them, 6 of 23 coincident were detected as interacting with environment. Single-environment analysis showed that 8 genetic regions on chromosomes 1, 2, 4, 5 and 9 clustered more than 60 % of the identified QTL. Twelve stable major QTLs accounting for over 10 % of phenotypic variation were included in five optimal clusters on the genetic region of bins 1.02–1.03, 1.04–1.06, 2.05–2.07, 4.07–4.08 and 9.03–9.04; the addition and partial dominance effects of significant QTL play an important role in controlling the development of maize kernel. These putative QTL may have great promising for further fine-mapping with more markers, and genetic improvement of maize kernel size and weight through marker-assisted breeding.     

Identification and mapping of fruit rot resistance QTL in American cranberry using GBS

Sustainability of the cranberry industry is threatened by widespread and increasing losses due to fruit rot in the field as well as increasing restrictions on fungicide inputs. Breeding for resistance offers a partial solution but is challenging because fruit rot is caused by a complex of pathogenic fungi that can vary by location and from year to year. We identified four genetically diverse germplasm accessions that exhibit broad-spectrum fruit rot resistance under field conditions. Three of these accessions were used in biparental crosses to develop four populations segregating for resistance. Genotyping by sequencing was used to generate single-nucleotide polymorphism (SNP) markers for development of high-density genetic maps and quantitative trait locus (QTL) analyses. Nineteen QTL associated with fruit rot resistance, distributed on nine linkage groups, were discovered in our populations. Three of these QTL matched previously reported fruit rot resistance QTL. Four newly reported QTL found on linkage group 8 (Vm8), which explain between 21 and 33% of the phenotypic variance for fruit rot, are of particular interest to our breeding program. The populations described herein were also phenotyped for other horticulturally important traits, and QTL associated with yield and berry weight were identified. These QTL provide markers for candidate gene discovery and for future breeding efforts to enhance and pyramid disease resistance and other traits into elite horticultural backgrounds.     

Ecophysiological analysis of genotypic variation in peach fruit growth

Cultivated varieties generally differ greatly from wild genotypes of the same closely related species. However, the processes responsible for these differences have not been elucidated. To analyse variations in fruit mass, fruit growth was characterized in a peach cultivar, a wild related species non-cultivated, and four hybrids derived by crossing them. These genotypes offer a wide range of agronomic values. An ecophysiological model of peach fruit growth in dry mass was used. This model simulates carbon partitioning at the 'shoot-bearing fruit' level by considering three compartments: fruits, 1-year-old stems and leafy shoots. The experimental measurements showed considerable variation between genotypes for fruit mass at maturity, fruit growth and source activity. The parameters of the ecophysiological model for each genotype were estimated from experimental data,. The model made it possible to account for genotypic variations in fruit growth and for genotype x fruit load interactions. Using the model, it was shown that the main processes explaining fruit growth variations among the genotypes studied were differences in potential fruit growth.     

Identification of molecular markers for aluminium tolerance in diploid oat through comparative mapping and QTL analysis

The degree of aluminium tolerance varies widely across cereal species, with oats (Avena spp.) being among the most tolerant. The objective of this study was to identify molecular markers linked to aluminium tolerance in the diploid oat A. strigosa. Restriction fragment length polymorphism markers were tested in regions where comparative mapping indicated the potential for orthologous quantitative trait loci (QTL) for aluminium tolerance in other grass species. Amplified fragment length polymorphism (AFLP) and sequence-characterized amplified region (SCAR) markers were used to provide additional coverage of the genome. Four QTL were identified. The largest QTL explained 39% of the variation and is possibly orthologous to the major gene found in the Triticeae as well as Alm1 in maize and a minor gene in rice. A second QTL may be orthologous to the Alm2 gene in maize. Two other QTL were associated with anonymous markers. Together, these QTL accounted for 55% of the variation. A SCAR marker linked to the major QTL identified in this study could be used to introgress the aluminium tolerance trait from A. strigosa into cultivated oat germplasm. Electronic Supplementary Material Supplementary material is available for this article at and is accessible for authorized users. S. Kibite: In Memoriam     

QTL analysis of flower and fruit traits in sour cherry

The map locations and effects of quantitative trait loci (QTLs) were estimated for eight flower and fruit traits in sour cherry (Prunus cerasus L.) using a restriction fragment length polymorphism (RFLP) genetic linkage map constructed from a double pseudo-testcross. The mapping population consisted of 86 progeny from the cross between two sour cherry cultivars, Rheinische Schattenmorelle (RS)×Erdi Botermo (EB). The genetic linkage maps for RS and EB were 398.2 cM and 222.2 cM, respectively, with an average interval length of 9.8 cM. The RS/EB linkage map that was generated with shared segregating markers consisted of 17 linkage groups covering 272.9 cM with an average interval length of 4.8 cM. Eleven putatively significant QTLs (LOD >2.4) were detected for six characters (bloom time, ripening time, % pistil death, % pollen germination, fruit weight, and soluble solids concentration). The percentage of phenotypic variation explained by a single QTL ranged from 12.9% to 25.9%. Of the QTLs identified for the traits in which the two parents differed significantly, 50% had allelic effects opposite to those predicted from the parental phenotype. Three QTLs affecting flower traits (bloom time, % pistil death, and % pollen germination) mapped to a single linkage group, EB 1. The RFLP closest to the bloom time QTL on EB 1 was detected by a sweet cherry cDNA clone pS141 whose partial amino acid sequence was 81% identical to that of a Japanese pear stylar RNase. Received: 4 March 1999 / Accepted: 27 August 1999     

Association mapping of common bacterial blight resistance QTL in Ontario bean breeding populations

Background  

Common bacterial blight (CBB), incited by Xanthomonas axonopodis pv. phaseoli (Xap), is a major yield-limiting factor of common bean (Phaseolus vulgaris L.) production around the world. Host resistance is practically the most effective and environmentally-sound approach to control CBB. Unlike conventional QTL discovery strategies, in which bi-parental populations (F₂, RIL, or DH) need to be developed, association mapping-based strategies can use plant breeding populations to synchronize QTL discovery and cultivar development.     

Methodologies for segregation analysis and QTL mapping in plants

Most characters of biological interest and economic importance are quantitative traits. To uncover the genetic architecture of quantitative traits, two approaches have become popular in China. One is the establishment of an analytical model for mixed major-gene plus polygenes inheritance and the other the discovery of quantitative trait locus (QTL). Here we review our progress employing these two approaches. First, we proposed joint segregation analysis of multiple generations for mixed major-gene plus polygenes inheritance. Second, we extended the multilocus method of Lander and Green (1987), Jiang and Zeng (1997) to a more generalized approach. Our methodology handles distorted, dominant and missing markers, including the effect of linked segregation distortion loci on the estimation of map distance. Finally, we developed several QTL mapping methods. In the Bayesian shrinkage estimation (BSE) method, we suggested a method to test the significance of QTL effects and studied the effect of the prior distribution of the variance of QTL effect on QTL mapping. To reduce running time, a penalized maximum likelihood method was adopted. To mine novel genes in crop inbred lines generated in the course of normal crop breeding work, three methods were introduced. If a well-documented genealogical history of the lines is available, two-stage variance component analysis and multi-QTL Haseman-Elston regression were suggested; if unavailable, multiple loci in silico mapping was proposed.     

QTL mapping of grain weight in rice and the validation of the QTL qTGW3.2

A recombinant inbred line (RIL) population bred from a cross between a javanica type (cv. D50) and an indica type (cv. HB277) rice was used to map seven quantitative trait loci (QTLs) for thousand grain weight (TGW). The loci were distributed on chromosomes 2, 3, 5, 6, 8 and 10. The chromosome 3 QTL qTGW3.2 was stably expressed over two years, and contributed 9–10% of the phenotypic variance. A residual heterozygous line (RHL) was selected from the RIL population and its selfed progeny was used to fine map qTGW3.2. In this “F₂” population, the QTL explained about 23% of the variance, rising to nearly 33% in the subsequent “F_2:3” generation. The physical location of qTGW3.2 was confined to a ~ 556 kb region flanked by the microsatellite loci RM16162 and RM16194. The region also contains other factors influencing certain yield-related traits, although it is also possible that qTGW3.2 affects these in a pleiotropic fashion.     

Fine mapping of fw3.2 controlling fruit weight in tomato

Tomato (Solanum lycopersicum) is an important crop in the Solanaceae family. One of the key traits selected during domestication is fruit mass which is controlled by many quantitative trait loci. The fruit weight locus fw3.2 is one of the major loci responsible for fruit mass in tomato. Identification of the underlying gene will improve our understanding of the molecular mechanism of fruit development while also providing insights into genes that were selected during domestication. We fine mapped fw3.2 to a 51.4-kb interval corresponding to a region comprising seven candidate genes. Gene action showed that the allele from cultivated tomato was additive to dominant in giving rise to an enlarged fruit. Fruit shape analysis indicated that fw3.2 primarily played a role in controlling fruit weight, with a minor effect on fruit shape. Gene expression and nucleotide diversity were investigated and the likelihood of the genes control fruit mass is discussed.