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IRBW2009 workshop presentation Brookes
Posted Mon, 08/06/2009 - 11:43 by
Gudmundur A Thorisson
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Anthony Brookes, University of Leicester - Access to aggregate data from genome-wide association studies
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Access to aggregate data from genome-wide association studies
Anthony Brookes University of Leicester, UK
GEN2PHEN: Work Packages
WP9 (Use & Sustainability) WP8 (Knowledge Centre)
KNOWLEDGE OUT
WP7 (Data Flows)
DATA IN
WP6 (Integration and Searching) WP4 (Genetics DBs) WP5 (Genomics DBs)
DATA IN
WP3 (Standards Development) WP2 (Domain Analysis)
LEADERSHIP
WP10 (Project Management) WP1 (Scientific Coordination)
Human Genome Variation Database: Genotype-toPhenotype
(HGVbaseG2P, http://www.hgvbaseg2p.org)
-
genetic association database aiming to integrate many datasets summary level data only links to data sources for primary data
HGVbaseG2P launch content Aug 2008
Summary level genotype and association data:
• dbSNP b.129 : • Broad DGI: • Finland/USA • NIH NINDS: • NIH NEI: ~107,000 • NCI CGEMS: markers) • Wellcome Trust: Case Control Consortium (14,000 cases, 3,000 controls, ~500,000 markers) Coronary heart disease Type 1 diabetes Type 2 diabetes Rheumatoid arthritis Crohn's disease Bipolar disorder Markers and Allele frequency data (>19 million Markers) Type 2 Diabetes (3,000 Scandinavians, ~386,000 Markers) Type 2 Diabetes (1161 cases, 1174 controls, ~315,000 Markers) Parkinson’s Disease (267 PD cases, 270 controls, ~400,000 Markers) Age-Related Eye Disease Study (AREDS) (350 cases, 171 controls, Markers) Breast Cancer (1145 cases, 1142 controls, ~ 550,000 markers) Prostate Cancer & Follow-up (1177 cases, 1105 controls, ~300,000
Homer et al
HGVbaseG2P launch content Aug 2008
Summary level genotype and association data:
• dbSNP b.129 : • Broad DGI: • Finland/USA • NIH NINDS: • NIH NEI: ~107,000 • NCI CGEMS: markers) • Wellcome Trust: Case Control Consortium (14,000 cases, 3,000 controls, ~500,000 markers) Coronary heart disease Type 1 diabetes Type 2 diabetes Rheumatoid arthritis Crohn's disease Bipolar disorder Markers and Allele frequency data (>19 million Markers) Type 2 Diabetes (3,000 Scandinavians, ~386,000 Markers) Type 2 Diabetes (1161 cases, 1174 controls, ~315,000 Markers) Parkinson’s Disease (267 PD cases, 270 controls, ~400,000 Markers) Age-Related Eye Disease Study (AREDS) (350 cases, 171 controls, Markers) Breast Cancer (1145 cases, 1142 controls, ~ 550,000 markers) Prostate Cancer & Follow-up (1177 cases, 1105 controls, ~300,000
What the Homer at al method can and cannot achieve...
Given: - extensive genotyping profile of individual X, or a close relative - frequency data for many SNPs in two well matched groups (eg. GWAS cases & controls) Can then => Predict reliably whether individual X was in the cases or controls No (or minimal?) ability to: - determine whether individual X was in a GWAS study or not - determine which study an individual was in, within a metaanalysis - determine case/control allocation given a ‘few’ marker frequencies - determine case/control allocation given a ‘few’ p-values
European Genotype Archive (EGA)
Database for Genotype and Phenotypes (dbGaP)
Summary-level GWAS data, and Homer et al...
AGGREGATED DATA
Safe Elements: Slightly Unsafe Elements: unhindered access ‘light touch’ access control
Which Sources?: Which Sources?: Which Methods?: immediate distributionone central archive, or DACs, or user IDs & to/via many databases & many data providers‘web 2.0’ technologies & integration sites Integration sites
HGVbaseG2P Reaction to Homer et al paper
• closed down all access to GWAS datasets (as requested by WT & NCBI) • gathered many new datasets, from public sources other than WT & NCBI, where data has not been withdrawn • constructed a more-powerful, two level data presentation system; - ‘safe data’ (tested SNP lists, phenotypes, p-values graphically, freq on <50 markers) - ‘mildly risky data’ (all freqs, p-values, and odds ratio data, HGVmart downloads)
Recognised ID digital ID provider
“OK, access allowed” “please approve me...” “please give me access…” “is he OK?” RAW DATA ARCHIVE
“OK!”
Data Provider (1)
Central Accreditation ID Body
“Yes, approved”
‘DatabaseJournal’ Concept
Journals
× × Primary publication route Experimental summaries Peer reviewed Publication credit Freetext data Not free access (mostly) × × × ×
Databases
Secondary publication route Experimental details Not peer reviewed No publication credit Structured data Free access
HGVbase G2P
Structured data Free access SELFSUSTAINABLE
Primary publication route Experimental summaries (Not) peer reviewed Publication credit
Identifying Researchers on the Biomedical Web
Acknowledgements
Gudmundur Thorisson, Owen Lancaster, Johan Klinberg Marianne Siegfried, Rob Free, Rob Hastings
Collaborators
Samir Brahmachari, Debasis Dash, et al (IGIB, India) Heikki Lehväslaiho (SANBI, South Africa) PaGE-OM Consortium, esp. Kimitoshi Naito et al (Japan) Hideaki Sugawara et al (Japan) Juha Muilu (Finland) Martin Senger (Philippines) Funding: EC FP6 NoE INFOBIOMED EC FP7 IP GEN2PHEN GlaxoSmithKline, UK
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© 2009
GEN2PHEN Project
.
The
GEN2PHEN Knowledge Centre
has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013)
under grant agreement number 200754 - the GEN2PHEN project.
