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Transcript
Prioritizing GWA data
1
Four independent cohorts
Exploratory Stage
Validation Stage
How to prioritize?
2
✓
?
Genome-wide significance
Candidate SNPs from each cohort
3
Map SNPs to candidate genes
Candidate SNP
Genes
SNP Region
4
Contextual information
Known target genes
Literature, curation supported by text-mining
Matching expression data
5
Correlating expression information
6
Literature
information
No need to really on existing annotation
Still requires existing understanding of
gene function
7
Cell biology assay
siRNA knockdown
8
Re-ranking by
context
9
Contextual information
Known networks: PPI, Coexpression, ..
Not specifically matched to GWAS
10
Map candidates on interaction networks
Identify highly connected regions
11
Identify subnetworks of interest
12
Find additional targets
Guilt-by-association
13
Ranking the final list
Ranked GWAS-based genes
Cellular assay hits
Literature curation
How to combine?
Core networks PPI/Expression
Guilt-by-association
14
Ranking the final list
Ranked GWAS-based genes
Cellular assay hits
Literature curation
Empirically...
Core networks PPI/Expression
Guilt-by-association
15
Bayesian approach
16