Harmonising and linking biomedical and clinical data across disparate data archives to enable integrative cross-biobank research

A wealth of biospecimen samples are stored in modern globally distributed biobanks. Biomedical researchers worldwide need to be able to combine the available resources to improve the power of large-scale studies. A prerequisite for this effort is to be able to search and access phenotypic, clinical and other information about samples that are currently stored at biobanks in an integrated manner. However, privacy issues together with heterogeneous information systems and the lack of agreed-upon vocabularies have made specimen searching across multiple biobanks extremely challenging. We describe three case studies where we have linked samples and sample descriptions in order to facilitate global searching of available samples for research. The use cases include the ENGAGE (European Network for Genetic and Genomic Epidemiology) consortium comprising at least 39 cohorts, the SUMMIT (surrogate markers for micro- and macro-vascular hard endpoints for innovative diabetes tools) consortium and a pilot for data integration between a Swedish clinical health registry and a biobank. We used the Sample avAILability (SAIL) method for data linking: first, created harmonised variables and then annotated and made searchable information on the number of specimens available in individual biobanks for various phenotypic categories. By operating on this categorised availability data we sidestep many obstacles related to privacy that arise when handling real values and show that harmonised and annotated records about data availability across disparate biomedical archives provide a key methodological advance in pre-analysis exchange of information between biobanks, that is, during the project planning phase

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

A Genome-Wide Metabolic QTL Analysis in Europeans Implicates Two Loci Shaped by Recent Positive Selection

We have performed a metabolite quantitative trait locus (mQTL) study of the 1H nuclear magnetic resonance spectroscopy (1H NMR) metabolome in humans, building on recent targeted knowledge of genetic drivers of metabolic regulation. Urine and plasma samples were collected from two cohorts of individuals of European descent, with one cohort comprised of female twins donating samples longitudinally. Sample metabolite concentrations were quantified by 1H NMR and tested for association with genome-wide single-nucleotide polymorphisms (SNPs). Four metabolites' concentrations exhibited significant, replicable association with SNP variation (8.6×10−11<p<2.8×10−23). Three of these—trimethylamine, 3-amino-isobutyrate, and an N-acetylated compound—were measured in urine. The other—dimethylamine—was measured in plasma. Trimethylamine and dimethylamine mapped to a single genetic region (hence we report a total of three implicated genomic regions). Two of the three hit regions lie within haplotype blocks (at 2p13.1 and 10q24.2) that carry the genetic signature of strong, recent, positive selection in European populations. Genes NAT8 and PYROXD2, both with relatively uncharacterized functional roles, are good candidates for mediating the corresponding mQTL associations. The study's longitudinal twin design allowed detailed variance-components analysis of the sources of population variation in metabolite levels. The mQTLs explained 40%–64% of biological population variation in the corresponding metabolites' concentrations. These effect sizes are stronger than those reported in a recent, targeted mQTL study of metabolites in serum using the targeted-metabolomics Biocrates platform. By re-analysing our plasma samples using the Biocrates platform, we replicated the mQTL findings of the previous study and discovered a previously uncharacterized yet substantial familial component of variation in metabolite levels in addition to the heritability contribution from the corresponding mQTL effects

Совершенствование технологий для осуществления рентабельного процесса добычи нефти на малодебитном фонде скважин

Материалы XII Междунар. науч. конф. студентов, магистрантов, аспирантов и молодых ученых, Гомель, 16–17 мая 2019 г

Services Design in a Collaborative Network for Multidisciplinary Research Projects

Part 10: Services DesignInternational audienceProviding information management services for multi-disciplinary research projects presents scientific, technological and communication challenges: constant change of themes and objects of scientific studies, entangled privacy and ownership requirements along with rapidly evolving methods of analysis and ways to look at data call for a highly dynamic communication and data service. We will present a case study of a collaborative network (simbioms.org). SIMBioMS, founded in 2005, develops open source software and provides data management services in biomedical research through four academic organisations and one company

Biobank Metaportal to Enhance Collaborative Research: sail.simbioms.org

In order to identify new ways to prevent, diagnose and treat diseases, biobanks systematically collect samples of human tissues and population-wide data on health and lifestyle. Efficient access to population biobank data and to biomaterial is crucial for development and marketing of new pharmaceutical products, especially in the area of personalised medicine. However, such access is hindered by legal and ethical constraints, and by the huge semantic diversity across different biobanks. To address these challenges, we have developed SAIL, a sophisticated metaportal for biobank data annotation across different collections and repositories, harmonised to allow cross-biobank searchability, while preserving the anonymity and privacy of the underlying data such that legal and ethical requirements are met. We describe the technological architecture and design of SAIL that allows us to meet these pressing challenges, and give an overview of the current functionality of the application. SAIL is available online at sail.simbioms.org, and it currently contains around 200 000 samples from 14 collections

SAIL-a software system for sample and phenotype availability across biobanks and cohorts

SUMMARY: The Sample avAILability system-SAIL-is a web based application for searching, browsing and annotating biological sample collections or biobank entries. By providing individual-level information on the availability of specific data types (phenotypes, genetic or genomic data) and samples within a collection, rather than the actual measurement data, resource integration can be facilitated. A flexible data structure enables the collection owners to provide descriptive information on their samples using existing or custom vocabularies. Users can query for the available samples by various parameters combining them via logical expressions. The system can be scaled to hold data from millions of samples with thousands of variables. AVAILABILITY: SAIL is available under Aferro-GPL open source license: https://github.com/sail