OVA: Integrating molecular and physical phenotype data from multiple biomedical domain ontologies with variant filtering for enhanced variant prioritization

Motivation: Exome sequencing has become a de facto standard method for Mendelian disease gene discovery in recent years, yet identifying disease-causing mutations among thousands of candidate variants remains a non-trivial task. Results: Here we describe a new variant prioritization tool, OVA (ontology variant analysis), in which user-provided phenotypic information is exploited to infer deeper biological context. OVA combines a knowledge-based approach with a variant-filtering framework. It reduces the number of candidate variants by considering genotype and predicted effect on protein sequence, and scores the remainder on biological relevance to the query phenotype. We take advantage of several ontologies in order to bridge knowledge across multiple biomedical domains and facilitate computational analysis of annotations pertaining to genes, diseases, phenotypes, tissues and pathways. In this way, OVA combines information regarding molecular and physical phenotypes and integrates both human and model organism data to effectively prioritize variants. By assessing performance on both known and novel disease mutations, we show that OVA performs biologically meaningful candidate variant prioritization and can be more accurate than another recently published candidate variant prioritization tool

High-density multi-population consensus genetic linkage map for peach

Highly saturated genetic linkage maps are extremely helpful to breeders and are an essential prerequisite for many biological applications such as the identification of marker-trait associations, mapping quantitative trait loci (QTL), candidate gene identification, development of molecular markers for marker-assisted selection (MAS) and comparative genetic studies. Several high-density genetic maps, constructed using the 9K SNP peach array, are available for peach. However, each of these maps is based on a single mapping population and has limited use for QTL discovery and comparative studies. A consensus genetic linkage map developed from multiple populations provides not only a higher marker density and a greater genome coverage when compared to the individual maps, but also serves as a valuable tool for estimating genetic positions of unmapped markers. In this study, a previously developed linkage map from the cross between two peach cultivars 'Zin Dai' and 'Crimson Lady' (ZC2) was improved by genotyping additional progenies. In addition, a peach consensus map was developed based on the combination of the improved ZC2 genetic linkage map with three existing high-density genetic maps of peach and a reference map of Prunus. A total of 1,476 SNPs representing 351 unique marker positions were mapped across eight linkage groups on the ZC2 genetic map. The ZC2 linkage map spans 483.3 cM with an average distance between markers of 1.38 cM/marker. The MergeMap and LPmerge tools were used for the construction of a consensus map based on markers shared across five genetic linkage maps. The consensus linkage map contains a total of 3,092 molecular markers, consisting of 2,975 SNPs, 116 SSRs and 1 morphological marker associated with slow ripening in peach (SR). The consensus map provides valuable information on marker order and genetic position for QTL identification in peach and other genetic studies within Prunus and Rosaceae

Heterozygous <em>COL17A1 </em>variants are a frequent cause of amelogenesis imperfecta

\ua9 Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.Background: Collagen XVII is most typically associated with human disease when biallelic COL17A1 variants (&gt;230) cause junctional epidermolysis bullosa (JEB), a rare, genetically heterogeneous, mucocutaneous blistering disease with amelogenesis imperfecta (AI), a developmental enamel defect. Despite recognition that heterozygous carriers in JEB families can have AI, and that heterozygous COL17A1 variants also cause dominant corneal epithelial recurrent erosion dystrophy (ERED), the importance of heterozygous COL17A1 variants causing dominant non-syndromic AI is not widely recognised. Methods: Probands from an AI cohort were screened by single molecule molecular inversion probes or targeted hybridisation capture (both a custom panel and whole exome sequencing) for COL17A1 variants. Patient phenotypes were assessed by clinical examination and analyses of affected teeth. Results: Nineteen unrelated probands with isolated AI (no co-segregating features) had 17 heterozygous, potentially pathogenic COL17A1 variants, including missense, premature termination codons, frameshift and splice site variants in both the endo-domains and the ecto-domains of the protein. The AI phenotype was consistent with enamel of near normal thickness and variable focal hypoplasia with surface irregularities including pitting. Conclusion: These results indicate that COL17A1 variants are a frequent cause of dominantly inherited non-syndromic AI. Comparison of variants implicated in AI and JEB identifies similarities in type and distribution, with five identified in both conditions, one of which may also cause ERED. Increased availability of genetic testing means that more individuals will receive reports of heterozygous COL17A1 variants. We propose that patients with isolated AI or ERED, due to COL17A1 variants, should be considered as potential carriers for JEB and counselled accordingly, reflecting the importance of multidisciplinary care

Heterozygous COL17A1 variants are a frequent cause of amelogenesis imperfecta

Background Collagen XVII is most typically associated with human disease when biallelic COL17A1 variants (>230) cause junctional epidermolysis bullosa (JEB), a rare, genetically heterogeneous, mucocutaneous blistering disease with amelogenesis imperfecta (AI), a developmental enamel defect. Despite recognition that heterozygous carriers in JEB families can have AI, and that heterozygous COL17A1 variants also cause dominant corneal epithelial recurrent erosion dystrophy (ERED), the importance of heterozygous COL17A1 variants causing dominant non-syndromic AI is not widely recognised. Methods Probands from an AI cohort were screened by single molecule molecular inversion probes or targeted hybridisation capture (both a custom panel and whole exome sequencing) for COL17A1 variants. Patient phenotypes were assessed by clinical examination and analyses of affected teeth. Results Nineteen unrelated probands with isolated AI (no co-segregating features) had 17 heterozygous, potentially pathogenic COL17A1 variants, including missense, premature termination codons, frameshift and splice site variants in both the endo-domains and the ecto-domains of the protein. The AI phenotype was consistent with enamel of near normal thickness and variable focal hypoplasia with surface irregularities including pitting. Conclusion These results indicate that COL17A1 variants are a frequent cause of dominantly inherited non-syndromic AI. Comparison of variants implicated in AI and JEB identifies similarities in type and distribution, with five identified in both conditions, one of which may also cause ERED. Increased availability of genetic testing means that more individuals will receive reports of heterozygous COL17A1 variants. We propose that patients with isolated AI or ERED, due to COL17A1 variants, should be considered as potential carriers for JEB and counselled accordingly, reflecting the importance of multidisciplinary care

Targeted development and mapping of functional molecular markers in an apple rootstock (Malus pumila) mapping progeny

The cultivated apple (Malus pumila Mill.) is an economically important crop, which is widely grown throughout the world. The identification of genes involved in traits of agronomic importance and the development of molecular markers for these genes is the key to the development of marker-assisted selection in breeding programs. Several genetic maps have been reported for apple, but the focus of these maps has been mainly on scion (fruit variety) crosses. In this investigation we aim to use information from the published apple genome sequence to develop intron-spanning primer pairs from the exons of Malus genes identified from within genetic regions of low marker density on a pre-existing SSR-based linkage map of an apple rootstock cross M.27 x M.116 (M432). Eighteen ‘gaps’ – regions larger than 10 cM containing no genetic markers - were identified for gene-specific primer design. BLAST analysis produced 2536 possible contig matches. The most significant 319 matches were selected and identified on 249 scaffolds. A total number of 165 gene-specific primers have been designed around the introns of genes located in these scaffolds. A set of 78 primers amplified single products in the parental genotypes whereas 33 amplified two products. Polymorphic markers developed within these scaffolds will increase marker density within regions. This communication details targeted development of markers for the improved saturation of the M432 apple rootstock linkage map

An inexpensive and rapid genomic DNA extraction protocol for rosaceous species

The development of high-throughput DNA genotyping, as part of marker-assisted selection and other DNA-based applications, has led to the need for a cheap, rapid and simple method for extracting genomic DNA (gDNA) from plant material. Commonly available gDNA extraction protocols do not provide consistently good quality gDNA from a wide range of plant species, particularly those in families such as the Rosaceae, which contain variable concentrations of polysaccharides and phenolic compounds. Furthermore, although commercial DNA extraction kits are widely available, they are expensive for the preparation of large numbers of DNA samples. Here, we present an improved gDNA extraction method which we have tested on 12 different species from the family Rosaceae: apple, apricot, blackberry, strawberry, peach, sweet cherry, sour cherry, pear, plum, red raspberry, quince, and almond. The method employs a novel lysis buffer which is a crucial step in obtaining high quality gDNA. It does not require expensive or toxic reagents and can be performed in the open laboratory, rather than in an air-flow hood. The method is reliable and produces good quality DNA that can be used in a variety of DNA-based studies

Genetics of resistance to Amphorophora idaei in red raspberry

Raspberry breeding aims to develop improved cultivars to satisfy market demands whilst allowing financially- and environmentally-sustainable production. The large raspberry aphid, Amphorophora idaei, transmits four viruses and thus its presence in raspberry plantings has a detrimental effect on fruit quality, yield and plantation longevity. Resistance to this vector has been central to the red raspberry breeding programme at East Malling for over 50 years and resistance genes (A1, A10, AK4a, AL518 and Acor) from different genetic backgrounds have been identified and introduced into our breeding lines. Selection pressure on the aphid, imposed by the widespread cultivation of resistant cultivars, has led to aphid populations overcoming A1, and more recently, A10 resistance. As part of efforts to produce a more durable resistance, breeders have made crosses to combine several of these genes. However, most of them produce identical responses from the predominant aphid biotype so it is currently impossible to determine which resistance genes, and how many, are carried by breeding lines. Further, the genetic basis of the resistance itself have yet to be unravelled. A series of crosses designed to identify molecular markers linked to the various aphid resistance genes have been carried out and thus far, A1 and A10 have been allocated to linkage groups 3 and 7 respectively. This paper briefly summarizes current knowledge of Amphorophora idaei, introduces our research strategy on aphid resistance genetics in red raspberry, presents progress to date and outlines future work

Correlation analysis and QTL mapping of fruit quality and plant architecture traits in cultivated strawberry (Fragaria × ananassa)

Understanding the association between phenotype and genotype is essential for plant breeders when selecting for optimal cultivars. However, it is not well known how different traits are correlated at both phenotypic and genotypic level. In this study, correlation analysis and QTL identification of 31 plant characteristics and fruit quality traits was investigated in cultivated strawberry mapping population ('Redgauntlet' × 'Hapil'). Phenotypic data was collected for two consecutive years (2013 and 2014) among six replications using field-grown strawberry plants. The key traits recorded included the attributes of plant growth and development, yield, firmness, sugar level and fruit shape. Phenotypic variations and correlation coefficients were calculated for each trait for both years using R statistical package. The consensus SNP-based genetic linkage map constructed by genotyping IStraw90 Axiom array was used to identify quantitative trait loci (QTL) for the traits recorded. At least a single QTL was identified for all traits recorded. A total of 72 QTLs were identified in 2013 and 90 QTLs were identified in 2014, however majority of them were year-dependent. Only 19 QTLs representing 13 traits were detected for two years. This suggests QTLs are likely to have been affected by environmental factors, and phenotyping traits for the third year are crucial to investigate the consistency of QTLs and to reduce the environmental factors