Ovarian cancer risk in Polish BRCA1 mutation carriers is not associated with the prohibitin 3' untranslated region polymorphism

<p>Abstract</p> <p>Background</p> <p>The variable penetrance of ovarian cancer in <it>BRCA1 </it>mutation carriers suggests that other genetic or environmental factors modify disease risk. The C to T transition in the 3' untranslated region of the prohibitin (<it>PHB</it>) gene alters mRNA function and has recently been shown to be associated with hereditary breast cancer risk in Polish women harbouring <it>BRCA1 </it>mutations.</p> <p>Methods</p> <p>To investigate whether the <it>PHB </it>3'UTR polymorphism also modifies hereditary ovarian cancer risk, we performed a case-control study among Polish women carrying one of the three common founder mutations (5382insC, 300 T > G, 4154delA) including 127 ovarian cases and 127 unaffected controls who had both breasts and ovaries intact. Controls were matched to cases by year of birth and <it>BRCA1 </it>mutation. Genotyping analysis was performed using PCR-based restriction fragment length polymorphism analysis. Odds ratios (OR) were calculated using conditional and penalized univariable and multivariable logistic regression.</p> <p>Results</p> <p>A comparison of the genotype frequencies between cases and controls revealed no association of the <it>PHB </it>3'UTR _CT+TT genotypes with ovarian cancer risk (OR_adj1.34; 95% CI, 0.59–3.11).</p> <p>Conclusion</p> <p>Our data suggest that the <it>PHB </it>3'UTR polymorphism does not modify ovarian cancer risk in women carrying one of the three Polish <it>BRCA1 </it>founder mutations.</p

Accelerated cloning of a potato late blight–resistance gene using RenSeq and SMRT sequencing

Global yields of potato and tomato crops are reduced owing to potato late blight disease, which is caused by Phytophthora infestans. Although most commercial potato varieties are susceptible to blight, wild potato relatives are not and are therefore a potential source of Resistance to P. infestans (Rpi) genes. Resistance breeding has exploited Rpi genes from closely related tuber-bearing potato relatives, but is laborious and slow 1–3. Here we report that the wild, diploid non-tuber-bearing Solanum americanum harbors multiple Rpi genes. We combine R gene sequence capture (RenSeq4) with single-molecule real-time SMRT sequencing (SMRT RenSeq) to clone Rpi-amr3i . This technology should enable de novo assembly of complete nucleotide-binding, leucine-rich repeat receptor (NLR) genes, their regulatory elements and complex multi-NLR loci from uncharacterized germplasm. SMRT RenSEQ can be applied to rapidly clone multiple R genes for engineering pathogen-resistant crops

The Reactome pathway knowledgebase

Reactome (http://www.reactome.org) is a manually curated open-source open-data resource of human pathways and reactions. The current version 46 describes 7088 human proteins (34% of the predicted human proteome), participating in 6744 reactions based on data extracted from 15 107 research publications with PubMed links. The Reactome Web site and analysis tool set have been completely redesigned to increase speed, flexibility and user friendliness. The data model has been extended to support annotation of disease processes due to infectious agents and to mutation

The Reactome pathway Knowledgebase

The Reactome Knowledgebase (www.reactome.org) provides molecular details of signal transduction, transport, DNA replication, metabolism and other cellular processes as an ordered network of molecular transformations-an extended version of a classic metabolic map, in a single consistent data model. Reactome functions both as an archive of biological processes and as a tool for discovering unexpected functional relationships in data such as gene expression pattern surveys or somatic mutation catalogues from tumour cells. Over the last two years we redeveloped major components of the Reactome web interface to improve usability, responsiveness and data visualization. A new pathway diagram viewer provides a faster, clearer interface and smooth zooming from the entire reaction network to the details of individual reactions. Tool performance for analysis of user datasets has been substantially improved, now generating detailed results for genome-wide expression datasets within seconds. The analysis module can now be accessed through a RESTFul interface, facilitating its inclusion in third party applications. A new overview module allows the visualization of analysis results on a genome-wide Reactome pathway hierarchy using a single screen page. The search interface now provides auto-completion as well as a faceted search to narrow result lists efficiently

Reactome: a database of reactions, pathways and biological processes

Reactome (http://www.reactome.org) is a collaboration among groups at the Ontario Institute for Cancer Research, Cold Spring Harbor Laboratory, New York University School of Medicine and The European Bioinformatics Institute, to develop an open source curated bioinformatics database of human pathways and reactions. Recently, we developed a new web site with improved tools for pathway browsing and data analysis. The Pathway Browser is an Systems Biology Graphical Notation (SBGN)-based visualization system that supports zooming, scrolling and event highlighting. It exploits PSIQUIC web services to overlay our curated pathways with molecular interaction data from the Reactome Functional Interaction Network and external interaction databases such as IntAct, BioGRID, ChEMBL, iRefIndex, MINT and STRING. Our Pathway and Expression Analysis tools enable ID mapping, pathway assignment and overrepresentation analysis of user-supplied data sets. To support pathway annotation and analysis in other species, we continue to make orthology-based inferences of pathways in non-human species, applying Ensembl Compara to identify orthologs of curated human proteins in each of 20 other species. The resulting inferred pathway sets can be browsed and analyzed with our Species Comparison tool. Collaborations are also underway to create manually curated data sets on the Reactome framework for chicken, Drosophila and rice

QTL mapping in autotetraploids using SNP dosage information

Dense linkage maps derived by analysing SNP dosage in autotetraploids provide detailed information about the location of, and genetic model at, quantitative trait loci. Recent developments in sequencing and genotyping technologies enable researchers to generate high-density single nucleotide polymorphism (SNP) genotype data for mapping studies. For polyploid species, the SNP genotypes are informative about allele dosage, and Hackett et al. (PLoS ONE 8:e63939, 2013) presented theory about how dosage information can be used in linkage map construction and quantitative trait locus (QTL) mapping for an F1 population in an autotetraploid species. Here, QTL mapping using dosage information is explored for simulated phenotypic traits of moderate heritability and possibly non-additive effects. Different mapping strategies are compared, looking at additive and more complicated models, and model fitting as a single step or by iteratively re-weighted modelling. We recommend fitting an additive model without iterative re-weighting, and then exploring non-additive models for the genotype means estimated at the most likely position. We apply this strategy to re-analyse traits of high heritability from a potato population of 190 F1 individuals: flower colour, maturity, height and resistance to late blight (Phytophthora infestans (Mont.) de Bary) and potato cyst nematode (Globodera pallida), using a map of 3839 SNPs. The approximate confidence intervals for QTL locations have been improved by the detailed linkage map, and more information about the genetic model at each QTL has been revealed. For several of the reported QTLs, candidate SNPs can be identified, and used to propose candidate trait genes. We conclude that the high marker density is informative about the genetic model at loci of large effects, but that larger populations are needed to detect smaller QTLs

Differentiating Protein-Coding and Noncoding RNA: Challenges and Ambiguities

The assumption that RNA can be readily classified into either protein-coding or non-protein–coding categories has pervaded biology for close to 50 years. Until recently, discrimination between these two categories was relatively straightforward: most transcripts were clearly identifiable as protein-coding messenger RNAs (mRNAs), and readily distinguished from the small number of well-characterized non-protein–coding RNAs (ncRNAs), such as transfer, ribosomal, and spliceosomal RNAs. Recent genome-wide studies have revealed the existence of thousands of noncoding transcripts, whose function and significance are unclear. The discovery of this hidden transcriptome and the implicit challenge it presents to our understanding of the expression and regulation of genetic information has made the need to distinguish between mRNAs and ncRNAs both more pressing and more complicated. In this Review, we consider the diverse strategies employed to discriminate between protein-coding and noncoding transcripts and the fundamental difficulties that are inherent in what may superficially appear to be a simple problem. Misannotations can also run in both directions: some ncRNAs may actually encode peptides, and some of those currently thought to do so may not. Moreover, recent studies have shown that some RNAs can function both as mRNAs and intrinsically as functional ncRNAs, which may be a relatively widespread phenomenon. We conclude that it is difficult to annotate an RNA unequivocally as protein-coding or noncoding, with overlapping protein-coding and noncoding transcripts further confounding this distinction. In addition, the finding that some transcripts can function both intrinsically at the RNA level and to encode proteins suggests a false dichotomy between mRNAs and ncRNAs. Therefore, the functionality of any transcript at the RNA level should not be discounted