GWAS provides new insights into the genetic mechanisms of phytochemicals production and red skin colour in apple.

Understanding the genetic architecture of apple phytochemicals, and their interplay with conventional selection traits, is critical for the development of new apple cultivars with enhanced health benefits. Apple accessions (n = 344) used for this genome-wide association study (GWAS) represented the wide diversity of metabolic profiles in the domesticated and wild Malus genepools. Fruit samples were phenotyped for 34 metabolites, including a stable vitamin C glycoside "ascorbic acid 2-ß-glucoside" (AA-2ßG), and the accessions were genotyped using the Apple 20 K SNP Array. Several fruit quality traits, including red skin over-colour (OCOL), were also assessed. Wild Malus accessions showed at least 2-fold higher average content of several metabolites (e.g. ascorbic acid, chlorogenic acid, phloridzin, and trilobatin) than Malus domestica accessions. Several new genomic regions and potential candidate genes underpinning the genetic diversity of apple phytochemicals were identified. The percentage of phenotypic variance explained by the best SNP ranged between 3% and 21% for the different metabolites. Novel association signals for OCOL in the syntenic regions on chromosomes 13 and 16 suggested that whole genome duplication has played a role in the evolution of apple red skin colour. Genetic correlations between phytochemicals and sensory traits were moderate. This study will assist in the selection of Malus accessions with specific phytochemical profiles to establish innovative genomics-based breeding strategies for the development of apple cultivars with enhanced nutritional value.

Turbocharging introgression breeding of perennial fruit crops: a case study on apple.

The allelic diversity of primitive germplasm of fruit crops provides a useful resource for introgressing novel genes to meet consumer preferences and environmental challenges. Pre-breeding facilitates the identification of novel genetic variation in the primitive germplasm and expedite its utilisation in cultivar breeding programmes. Several generations of pre-breeding could be required to minimise linkage drag from the donor parent and to maximise the genomic content of the recipient parent. In this study we investigated the potential of genomic selection (GS) as a tool for rapid background selection of parents for the successive generation. A diverse set of 274 accessions was genotyped using random-tag genotyping-by-sequencing, and phenotyped for eight fruit quality traits. The relationship between 'own phenotypes' of 274 accessions and their general combining ability (GCA) was also examined. Trait heritability influenced the strength of correspondence between own phenotype and the GCA. The average (across eight traits) accuracy of predicting own phenotype was 0.70, and the correlations between genomic-predicted own phenotype and GCA were similar to the observed correlations. Our results suggest that genome-assisted parental selection (GAPS) is a credible alternative to phenotypic parental selection, so could help reduce the generation interval to allow faster accumulation of favourable alleles from donor and recipient parents.

Genome-Enabled Estimates of Additive and Nonadditive Genetic Variances and Prediction of Apple Phenotypes Across Environments.

The nonadditive genetic effects may have an important contribution to total genetic variation of phenotypes, so estimates of both the additive and nonadditive effects are desirable for breeding and selection purposes. Our main objectives were to: estimate additive, dominance and epistatic variances of apple (Malus × domestica Borkh.) phenotypes using relationship matrices constructed from genome-wide dense single nucleotide polymorphism (SNP) markers; and compare the accuracy of genomic predictions using genomic best linear unbiased prediction models with or without including nonadditive genetic effects. A set of 247 clonally replicated individuals was assessed for six fruit quality traits at two sites, and also genotyped using an Illumina 8K SNP array. Across several fruit quality traits, the additive, dominance, and epistatic effects contributed about 30%, 16%, and 19%, respectively, to the total phenotypic variance. Models ignoring nonadditive components yielded upwardly biased estimates of additive variance (heritability) for all traits in this study. The accuracy of genomic predicted genetic values (GEGV) varied from about 0.15 to 0.35 for various traits, and these were almost identical for models with or without including nonadditive effects. However, models including nonadditive genetic effects further reduced the bias of GEGV. Between-site genotypic correlations were high (>0.85) for all traits, and genotype-site interaction accounted for <10% of the phenotypic variability. The accuracy of prediction, when the validation set was present only at one site, was generally similar for both sites, and varied from about 0.50 to 0.85. The prediction accuracies were strongly influenced by trait heritability, and genetic relatedness between the training and validation families.

This is how we do it.

The purpose of this article is to explore students' experiences of variations in practice and how this impacts on their learning. This is particularly pertinent in terms of reconfiguration of services instigated by the Department of Health's (DH) Making it better agenda (DH 2007), meaning that many qualified staff are moving hospitals and also experiencing changes in practice. Students are thus being mentored by a range of mentors in a variety of settings, which can often mean that practice can be perceived as inconsistent and even idiosyncratic (Jones et al 2005). This writing is unique in that the midwifery lecturer facilitated the students' development of the work in a truly collaborative piece, developed through a writing and journal club. All details of the practice placements and the student comments have been anonymised.