ABSTRACT
Rose is the world's most important ornamental plant, with economic, cultural and symbolic value. Roses are cultivated worldwide and sold as garden roses, cut flowers and potted plants. Roses are outbred and can have various ploidy levels. Our objectives were to develop a high-quality reference genome sequence for the genus Rosa by sequencing a doubled haploid, combining long and short reads, and anchoring to a high-density genetic map, and to study the genome structure and genetic basis of major ornamental traits. We produced a doubled haploid rose line ('HapOB') from Rosa chinensis 'Old Blush' and generated a rose genome assembly anchored to seven pseudo-chromosomes (512 Mb with N50 of 3.4 Mb and 564 contigs). The length of 512 Mb represents 90.1-96.1% of the estimated haploid genome size of rose. Of the assembly, 95% is contained in only 196 contigs. The anchoring was validated using high-density diploid and tetraploid genetic maps. We delineated hallmark chromosomal features, including the pericentromeric regions, through annotation of transposable element families and positioned centromeric repeats using fluorescent in situ hybridization. The rose genome displays extensive synteny with the Fragaria vesca genome, and we delineated only two major rearrangements. Genetic diversity was analysed using resequencing data of seven diploid and one tetraploid Rosa species selected from various sections of the genus. Combining genetic and genomic approaches, we identified potential genetic regulators of key ornamental traits, including prickle density and the number of flower petals. A rose APETALA2/TOE homologue is proposed to be the major regulator of petal number in rose. This reference sequence is an important resource for studying polyploidization, meiosis and developmental processes, as we demonstrated for flower and prickle development. It will also accelerate breeding through the development of molecular markers linked to traits, the identification of the genes underlying them and the exploitation of synteny across Rosaceae.
Subject(s)
Genome, Plant/genetics , Rosa/genetics , Centromere/genetics , Chromosomes, Plant/genetics , Flowers/anatomy & histology , Flowers/genetics , Fragaria/genetics , Genetic Variation/genetics , Haploidy , In Situ Hybridization, Fluorescence , Phylogeny , Quantitative Trait Loci/genetics , Quantitative Trait, Heritable , Rosa/anatomy & histology , Sequence Analysis, DNA , Synteny/geneticsABSTRACT
KEY MESSAGE: Proof of concept of Bayesian integrated QTL analyses across pedigree-related families from breeding programs of an outbreeding species. Results include QTL confidence intervals, individuals' genotype probabilities and genomic breeding values. Bayesian QTL linkage mapping approaches offer the flexibility to study multiple full sib families with known pedigrees simultaneously. Such a joint analysis increases the probability of detecting these quantitative trait loci (QTL) and provide insight of the magnitude of QTL across different genetic backgrounds. Here, we present an improved Bayesian multi-QTL pedigree-based approach on an outcrossing species using progenies with different (complex) genetic relationships. Different modeling assumptions were studied in the QTL analyses, i.e., the a priori expected number of QTL varied and polygenic effects were considered. The inferences include number of QTL, additive QTL effect sizes and supporting credible intervals, posterior probabilities of QTL genotypes for all individuals in the dataset, and QTL-based as well as genome-wide breeding values. All these features have been implemented in the FlexQTL(™) software. We analyzed fruit firmness in a large apple dataset that comprised 1,347 individuals forming 27 full sib families and their known ancestral pedigrees, with genotypes for 87 SSR markers on 17 chromosomes. We report strong or positive evidence for 14 QTL for fruit firmness on eight chromosomes, validating our approach as several of these QTL were reported previously, though dispersed over a series of studies based on single mapping populations. Interpretation of linked QTL was possible via individuals' QTL genotypes. The correlation between the genomic breeding values and phenotypes was on average 90 %, but varied with the number of detected QTL in a family. The detailed posterior knowledge on QTL of potential parents is critical for the efficiency of marker-assisted breeding.
Subject(s)
Crosses, Genetic , Malus/genetics , Quantitative Trait Loci , Bayes Theorem , Breeding , Chromosome Mapping , Chromosomes, Plant , Fruit/anatomy & histology , Fruit/genetics , Genetic Association Studies , Genetic Linkage , Genotype , Malus/anatomy & histology , PedigreeABSTRACT
Alternaria solani (Ellis and Martin) Sorauer, the causal agent of early blight (EB) disease, infects aerial parts of tomato at both seedling and adult plant stages. Resistant cultivars would facilitate a sustainable EB management. EB resistance is a quantitatively expressed character, a fact that has hampered effective breeding. In order to identify and estimate the effect of genes conditioning resistance to EB, a quantitative trait loci (QTL) mapping study was performed in F2 and F3 populations derived from the cross between the susceptible Solanum lycopersicum (syn. Lycopersicon esculentum) cv. 'Solentos' and the resistant Solanum arcanum (syn. Lycopersicon peruvianum) LA2157 and genotyped with AFLP, microsatellite and SNP markers. Two evaluation criteria of resistance were used: measurements of EB lesion growth on the F2 plants in glasshouse tests and visual ratings of EB severity on foliage of the F3 lines in a field test. A total of six QTL regions were mapped on chromosomes 1, 2, 5-7, and 9 with LOD scores ranging from 3.4 to 17.5. Three EB QTL also confer resistance to stem lesions in the field, which has not been reported before. All QTL displayed significant additive gene action; in some cases a dominance effect was found. Additive x additive epistatic interactions were detected between one pair of QTL. For two QTL, the susceptible parent contributed resistance alleles to both EB and stem lesion resistance. Three of the QTL showed an effect in all tests despite methodological and environmental differences.
Subject(s)
Alternaria/physiology , Crosses, Genetic , Plant Diseases/genetics , Plant Diseases/immunology , Quantitative Trait Loci/genetics , Solanum lycopersicum/genetics , Solanum lycopersicum/microbiology , Chromosome Mapping , Chromosome Segregation , Chromosomes, Plant/genetics , Genetic Linkage , Genotype , Immunity, Innate/genetics , Models, Genetic , Phenotype , Plant Leaves/genetics , Plant Leaves/microbiology , Plant Stems/genetics , Plant Stems/microbiologyABSTRACT
A genetic map covering 615 cM in 12 linkage groups was assembled based on 92 RFLP and AFLP markers segregating in a population of 107 doubled haploid lines (DH lines) of Brassica oleracea. The DH-line population was obtained through microspore culture from the F(1) of two homozygous parents: DH-line Bi derived from the cabbage landrace Bindsachsener, and DH-line Gr from broccoli cv 'Greenia'. Sixty-five percent of the loci, and in some cases complete linkage groups, displayed distorted segregation ratios, a frequency much higher than that observed in F(2) populations of the same species. DH-line Bi was resistant to clubroot, which is caused by a Dutch field isolate of Plasmodiophora brassicae. Resistance in the DH-line population was determined in two ways: by assigning symptom grades to each plant, and by measuring the fresh weights of the healthy and affected parts of the root system of each plant. Using a multiple QTL mapping approach to analyze the fresh weight data, we found two loci for clubroot resistance; these were designated pb-3 and pb-4. The additive effects of these loci were responsible for 68% of the difference between the parents and for 60% of the genetic variance among DH-line means. Also, indications for the presence of two additional, minor QTLs were found. Analysis of symptom grades revealed the two QTLs pb-3 and pb-4, as well as one of the two minor QTLs indicated by analysis of the fresh weight data.