News Article | October 26, 2016
The bellygenes initiative, coordinated by researchers at Karolinska Institutet and the University Medical Centre Groningen, will study the genetic makeup of 800,000 Europeans in relation to irritable bowel syndrome and associated symptoms. Irritable bowel syndrome (IBS) is the most common gastrointestinal disorder, reducing quality of life in 10-15% of people worldwide (women more than men). Although some contributing factors have been identified, IBS pathophysiology remains largely unknown. Human genetic studies are contributing to the development of personalized treatment options, but in IBS these have been few and underpowered to deal with its complex and heterogeneous nature. A large-scale project, the bellygenes initiative, is now aiming to overcome this issues by studying IBS in relation to the genetic makeup of some 800,000 Europeans from well-established cohorts, biobanks and patients' collections. "This represents an unmatched opportunity to tackle IBS genetics for the first time with adequate statistical power" says bellygenes coordinator Mauro D'Amato from the Unit of Clinical Epidemiology, Department of Medicine, Karolinska Institutet. "We hope to reveal pathophysiological pathways that can help explain the etiology of IBS, inform a molecular reclassification of patients, and ultimately provide novel biological targets for increased therapeutic precision". The bellygenes initiative received support from the BioMolecular resources Research Infrastructure - large prospective cohorts, for accessing data from UK Biobank, LifeLines, Estonian Biobank EGCUT and HUNT. Several other population-based and case-control cohorts are studied as part of a large established IBS collaborative network of international scientists and clinicians.
Haller T.,Massachusetts Institute of Technology |
Kals M.,EGCUT |
Esko T.,EGCUT |
Esko T.,The Broad Institute of MIT and Harvard |
And 5 more authors.
Briefings in Bioinformatics | Year: 2013
Genome-wide association studies are becoming computationally more demanding with the growing amounts of data. Combinatorial traits can increase the data dimensions beyond the computational capabilities of the current tools. We addressed this issue by creating an application for quick association analysis that is ten to hundreds of times faster than the leading fast methods. Our tool (RegScan) is designed for performing basic linear regression analysis with continuous traits maximally fast on large data sets. RegScan specifically targets association analysis of combinatorial traits in metabolomics. It can both generate and analyze the combinatorial traits efficiently. RegScan is capable of analyzing any number of traits together without the need to specify each trait individually. The main goal of the article is to show that RegScan can be the preferred analytical tool when large amounts of data need to be analyzed quickly using the allele frequency test. © The Author 2013. Published by Oxford University Press.