Machine learning methods applied to genotyping data capture interactions between single nucleotide variants in late onset Alzheimer's disease

Introduction Genome-wide association studies (GWAS) in late onset Alzheimer's disease (LOAD) provide lists of individual genetic determinants. However, GWAS do not capture the synergistic effects among multiple genetic variants and lack good specificity. Methods We applied tree-based machine learning algorithms (MLs) to discriminate LOAD (>700 individuals) and age-matched unaffected subjects in UK Biobank with single nucleotide variants (SNVs) from Alzheimer's disease (AD) studies, obtaining specific genomic profiles with the prioritized SNVs. Results MLs prioritized a set of SNVs located in genes PVRL2, TOMM40, APOE, and APOC1, also influencing gene expression and splicing. The genomic profiles in this region showed interaction patterns involving rs405509 and rs1160985, also present in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. rs405509 located in APOE promoter interacts with rs429358 among others, seemingly neutralizing their predisposing effect. Discussion Our approach efficiently discriminates LOAD from controls, capturing genomic profiles defined by interactions among SNVs in a hot-spot region

Twelve quick tips for deploying a Beacon

This study was funded by ELIXIR, the research infrastructure for life-science data. The project leading to these results has also received funding and grant support from the “LaCaixa” Foundation under the Grant 004745/008034. LAF was supported, in part, by La Marató TV3, and has received funding from the European Union's Horizon Europe research and innovation programme under grant agreement No 101057182. AJB and JR received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 825575. MB acknowledges funding under the BioMedIT Network project of Swiss Institute of Bioinformatics (SIB) and Swiss Personalized Health Network (SPHN). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Beacon v2 and Beacon networks: A "lingua franca" for federated data discovery in biomedical genomics, and beyond

Beacon is a basic data discovery protocol issued by the Global Alliance for Genomics and Health (GA4GH). The main goal addressed by version 1 of the Beacon protocol was to test the feasibility of broadly sharing human genomic data, through providing simple "yes" or "no" responses to queries about the presence of a given variant in datasets hosted by Beacon providers. The popularity of this concept has fostered the design of a version 2, that better serves real-world requirements and addresses the needs of clinical genomics research and healthcare, as assessed by several contributing projects and organizations. Particularly, rare disease genetics and cancer research will benefit from new case level and genomic variant level requests and the enabling of richer phenotype and clinical queries as well as support for fuzzy searches. Beacon is designed as a "lingua franca" to bridge data collections hosted in software solutions with different and rich interfaces. Beacon version 2 works alongside popular standards like Phenopackets, OMOP, or FHIR, allowing implementing consortia to return matches in beacon responses and provide a handover to their preferred data exchange format. The protocol is being explored by other research domains and is being tested in several international projects.This study was funded by ELIXIR, the research infrastructure for lifescience data and also by La Caixa Foundation (grant number 004745/ 008034). Tim Beck was supported by a UKRI Innovation Fellowship at Health Data Research UK (MR/S003703/1). Anthony J. Brookes and Jordi Rambla were supported, in part, by the European Union's Horizon 2020 research and innovation program under the EJP RD COFUND‐EJP #825575. Michael Baudis acknowledges funding under the BioMedIT Network project of Swiss Institute of Bioinformatics (SIB) and Swiss Personalized Health Network (SPHN

Consent codes: Maintaining consent in an ever-expanding open science ecosystem

We previously proposed a structure for recording consent-based data use 'categories' and 'requirements' - Consent Codes - with a view to supporting maximum use and integration of genomic research datasets, and reducing uncertainty about permissible re-use of shared data. Here we discuss clarifications and subsequent updates to the Consent Codes (v4) based on new areas of application (e.g., the neurosciences, biobanking, H3Africa), policy developments (e.g., return of research results), and further practical considerations, including developments in automated approaches to consent management.SOMD, SD, ACE and JK were supported by The Neuro Tanenbaum Open Science Institute, the Canadian Open Neuroscience Platform (funded in part by Brain Canada), and McGill Healthy Brains for Healthy Lives. NM and LZ are funded by the NIH under grant number U24HG006941. MM is funded by EUH2020 CINECA grant number 825775. NM, VN and NSM are funded by the NHLBI award number U24HL135600. JDS and GK are funded by the Wellcome Trust grant 360G-Wellcome-201535_Z_16_Z and previously the EU H2020 Corbel grant number 645248

Data use categories and requirements (Consent Codes): definition and abbreviation.

<p>Data use categories and requirements (Consent Codes): definition and abbreviation.</p

Consent codes: upholding standard data use conditions

A systematic way of recording data use conditions that are based on consent permissions as found in the datasets of the main public genome archives (NCBI dbGaP and EMBL-EBI/CRG EGA).SOMD is supported by the Canadian Institutes of Health Research (grants EP1-120608; EP2-120609), the Canada Research Chair in Law and Medicine, and the Public Population Project in Genomics and Society (P3G). DNP and ESL are supported, in part, by the Intramural Research Program of the NIH, Office of the Director, Office of Science Policy. BMK is supported by the Canada Research Chairs Program. MT and MR are sponsored by ODEX4all (NWO 650.002.002) and funding from the European Commission (FP-7 project RD-Connect, grant agreement No. 305444

The European Genome-phenome Archive of human data consented for biomedical research

The European Genome-phenome Archive (EGA) is a permanent archive that promotes the distribution and sharing of genetic and phenotypic data consented for specific approved uses but not fully open, public distribution. The EGA follows strict protocols for information management, data storage, security and dissemination. Authorized access to the data is managed in partnership with the data-providing organizations. The EGA includes major reference data collections for human genetics research.The EGA has received support from the European Molecular Biology Laboratory, the European Union ELIXIR Technical Feasibility Study, the Wellcome Trust (grant WT 085475/C/08/Z), the UK Medical Research Council (grant G0800681), the Spanish Instituto de Salud Carlos III Instituto Nacional de Bioinformática (grant PT13/0001/0026), the Spanish Ministerio de Economía y Competitividad (MINECO) and Centro de Excelencia Severo Ochoa (grant SEV-2012-0208), the Fundació La Caixa and the Barcelona Supercomputing Centre. The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013 under grant agreements 211601–ELIXIR, 200754–GEN2PHEN, 262055–ESGI, 242006–BASIS, 261376–IHMS and 305444–RD-CONNECT)

Registered access: authorizing data access

The Global Alliance for Genomics and Health (GA4GH) proposes a data access policy model-"registered access"-to increase and improve access to data requiring an agreement to basic terms and conditions, such as the use of DNA sequence and health data in research. A registered access policy would enable a range of categories of users to gain access, starting with researchers and clinical care professionals. It would also facilitate general use and reuse of data but within the bounds of consent restrictions and other ethical obligations. In piloting registered access with the Scientific Demonstration data sharing projects of GA4GH, we provide additional ethics, policy and technical guidance to facilitate the implementation of this access model in an international setting

Registered access: authorizing data access

The Global Alliance for Genomics and Health (GA4GH) proposes a data access policy model-"registered access"-to increase and improve access to data requiring an agreement to basic terms and conditions, such as the use of DNA sequence and health data in research. A registered access policy would enable a range of categories of users to gain access, starting with researchers and clinical care professionals. It would also facilitate general use and reuse of data but within the bounds of consent restrictions and other ethical obligations. In piloting registered access with the Scientific Demonstration data sharing projects of GA4GH, we provide additional ethics, policy and technical guidance to facilitate the implementation of this access model in an international setting.SOMD is supported by the Canadian Institutes of Health Research (EP1-120608; EP1-120609; CEE-151618), Genome Quebec, Genome Canada, the Government of Canada, the Ministère de l’Économie, Innovation et Exportation du Québec (Can-SHARE grant 141210), and the Canada Research Chair in Law and Medicine. ML, IL, JT, and TN are supported by the ELIXIR, the research infrastructure for life-science data, and the H2020 ELIXIR-EXCELERATE grant 676559. IL and GK are supported by the European Molecular Biology Laboratory; MS by Research Foundation Flanders (FWO); MH by NIH/NHGRI 5U41HG002371-15; SW by NIH/NHGRI R00HG008175; S Beck by the National Institute for Health Research UCLH Biomedical Research Centre (BRC369/CN/SB/101310); S Brenner by NIH/NHGRI U41 HG007346; BMK by the Canada Research Chair in Law and Medicine; and PF by WT201535/Z/16/Z and the European Molecular Biology Laboratory