Bayesian QTL analyses using pedigreed families of an outcrossing species, with application to fruit firmness in apple.

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.

A new versatile database created for geneticists and breeders to link molecular and phenotypic data in perennial crops: the AppleBreed DataBase.

OBJECTIVE: AppleBreed DataBase (DB) aims to store genotypic and phenotypic data from multiple pedigree verified plant populations (crosses, breeding selections and commercial cultivars) so that they are easily accessible for geneticists and breeders. It will help in elucidating the genetics of economically important traits, in identifying molecular markers associated with agronomic traits, in allele mining and in choosing the best parental cultivars for breeding. It also provides high traceability of data over generations, years and localities. AppleBreed DB could serve as a generic database design for other perennial crops with long economic lifespans, long juvenile periods and clonal propagation. RESULTS: AppleBreed DB is organized as a relational database. The core element is the GENOTYPE entity, which has two sub-classes at the physical level: TREE and DNA-SAMPLE. This approach facilitates all links between plant material, phenotypic and molecular data. The entities TREE, DNA-SAMPLE, PHENOTYPE and MOLECULAR DATA allow multi-annual observations to be stored as individual samples of individual trees, even if the nature of these observations differs greatly (e.g. molecular data on parts of the apple genome, physico-chemical measurements of fruit quality traits, and evaluation of disease resistance). AppleBreed DB also includes synonyms for cultivars and pedigrees. Finally, it can be loaded and explored through the web, and comes with tools to present basic statistical overviews and with validation procedures for phenotypic and marker data to certify data quality. AppleBreed DB was developed initially as a tool for scientists involved in apple genetics within the framework of the European project, 'High-quality Disease Resistance in Apples for Sustainable Agriculture' (HiDRAS), but it is also applicable to many other perennial crops.