Data Availability Statement: The raw genotype data cannot be shared publicly because this could allow the identification of obese subjects enrolled in the clinical weight loss trial from the DiOGenes study or the clinical weight loss program from the Ottawa Obesity Clinic. The data are available from the DiOGenes consortium and the Ottawa Obesity Clinic for all researchers who meet the criteria for access to confidential data (point of contact Ruth McPherson, email: rmcpherson@ottawaheart.ca). The MS proteomic data have been deposited on the ProteomeXchange Consortium via the PRIDE analyze jointly thousands of traits and millions of markers. Its applicability goes beyond pQTL studies, opening new perspectives for large-scale genome-wide association and QTL analyses. Diet, Obesity and Genes (DiOGenes) trial registration number: NCT00390637.
The genetic contribution to obesity has been widely studied, yet the functional mechanisms underlying metabolic states remain elusive. This has prompted analysis of endophenotypes via quantitative trait locus studies, which assess how genetic variants affect intermediate gene (eQTL) or protein (pQTL) expression phenotypes. However, most such studies rely on univariate screening, which entails a strong multiplicity burden and does not leverage shared regulatory patterns. We present the first multivariate pQTL analysis with our highly scalable Bayesian framework LOCUS, on plasma protein levels from a dual mass-spectrometry and SomaLogic assay, and show that it is more powerful than a standard univariate procedure on this data.We identify 136 pQTL associations in the Ottawa obesity cohort, of which > 80% replicate in the independent DiOGenes cohort and have significant functional enrichments; 15% of the hits would be missed by univariate analysis. By exploiting clinical data, we reveal the implication of proteins under genetic control in low-grade inflammation, insulin resistance, and dyslipidemia, opening new perspectives for diagnosing and treating metabolic disorders. All results are freely accessible online from our searchable database.
The CernVM File System (CernVM-FS) provides a scalable and reliable software distribution service implemented as a POSIX read-only filesystem in user space (FUSE). It was originally developed at CERN to assist High Energy Physics (HEP) collaborations in deploying software on the worldwide distributed computing infrastructure for data processing applications. Files are stored remotely as content-addressed blocks on standard web servers and are retrieved and cached on-demand through outgoing HTTP connections only. Repository metadata is recorded in SQLite catalogs, which represent implicit Merkle treeencodings of the repository state. For writing, CernVM-FS follows a publish-subscribe pattern with a single source of new content that is propagated to a large number of readers. This paper focuses on the work to move the CernVM-FS architecturein the direction of a responsive data distribution system. A new distributed publication backend allows scaling out large publication tasks across multiple machines, reducing the time to publish. For the faster propagation of new published content, the addition of a notification system allows clients to subscribe to messages about changes in the repository and to request new root catalogs as soon as they become available. These devel-opments make CernVM-FS more responsive and are particularly relevant for use cases where a short propagation delay from repository down to individual clients is important, such as using CernVM-FS as an AFS replacement for distributing software stacks. Additionally, they permit the implementation of more complex workflows, with producer-consumer pipelines, as for example in the ALICE analysis trains system.
Inherited from the flexible architecture of Xrootd, Xcache allows a wide range of customization through configurations and plugin modules. This paper describes several completed and ongoing R&D efforts of using Xcache in the LHC ATLAS distributed computing environment, in particular, using Xcache with the ATLAS data management system Rucio for easy-to-use and to improve cache hit rate, to replace Squid and improve distribution of large files in CVMFS, to adapt the HPC environment and the data lake model for efficient data distribution and access for the HPCs.
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