Panel at AMIA 2013 Conference on big data - The Exposome and the quantified self fjms
exposome-wide association studies for discovering ... · The exposome – promoting discovery of...
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Exposome-wide association studies (EWAS) for discovering environmental
causes of disease
S.M. Rappaport University of California, Berkeley
Research support from NIEHS and the American Chemistry Council
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Some background • About three fourths of all people die from chronic
diseases, mainly CVD and cancer • These diseases result from a combination of genetic (G)
and environmental (E) factors o Known risk factors account for less than half of chronic-
disease risks • Elaboration of the G matrix with modern GWAS has
been stunningly comprehensive o but has explained relatively little CVD and cancer risks
• Elaboration of the E matrix relies on questionnaires, geographic information and targeted measurements o much as it did a century ago
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The exposome – promoting discovery of environmental causes of disease
Christopher Wild defined the ‘exposome’, representing all environmental exposures (including diet, lifestyle, and infections) from conception onwards, as a complement to the genome in studies of disease etiology
Wild, C.P., Cancer Epidemiol Biomarkers Prev 14 (8), 1847-1850 (2005).
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2004 2006 2008 2010 2012
Num
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Year
Scientific citations to ‘exposome’ (Google Scholar)
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Wild CEBP (2005)
1st NAS workshop; Science Perspective
2nd NAS workshop
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Functionalizing the exposome
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The exposome includes all chemicals in the internal chemical environment
S.M. Rappaport and M.T. Smith, Science, 2010: 330, 460-461
Measurements of biomarkers can provide information about both exogenous and endogenous exposures
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Exposome-wide association studies (EWAS)
By applying EWAS to biospecimens from healthy and diseased subjects, we can discover causal environmental exposures.
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http://www.flickr.com/photos/paulieparker/246707763/
But which ‘omes’ offer the most promise for EWAS and follow-up studies?
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The molecular basis of life (and disease)
Genome
(G = DNA) Transcriptome
(R = RNA) Proteome (P = large
molecules)
Metabolome (M = small molecules)
INTERNAL CHEMICAL ENVIRONMENT
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G Rc Disease
phenotype Rr E
G = genome E = environment R = transcriptome (gene expression)
Causal pathway (c) Reactive pathway (r)
Secondary phenotype
S. Rappaport, Biomarkers, 2012, 17(6), 48: 3-9 Based on: E. Shadt et al., Nat Gen, 2005, 37: 710-717
Disease pathways
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G = genome E = environment R = transcriptome (gene expression) P = proteome (protein expression) M = metabolome (all small molecules and metals)
G Rc Pc Disease
traits Rr Pr Secondary
traits
Mr
Causal pathway (c) Reactive pathway (r)
Adding omes
S. Rappaport, Biomarkers, 2012, 17(6), 48: 3-9
E Mc
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G Rc Pc Disease
traits Rr Pr Secondary
traits
E
Mr
Mc
Epigenetic modifications Post-translational modifications Genetic modifications (mutations)
More omic connections
S. Rappaport, Biomarkers, 2012, 17(6), 48: 3-9
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Which omes for EWAS?
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If causal exposures operate primarily through small
molecules (Mc) and proteins (Pc), then EWAS require metabolomics and/or proteomics.
G Rc Pc Disease
traits
E Mc
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Reactive biomarkers obscure causal pathways. For validation of exposure biomarkers, biospecimens should be obtained prior to disease (prospective cohorts)
Biospecimens for EWAS? Reactive biomarkers
(disease) Causal biomarkers
(exposure)
G Rc Pc Disease
traits Rr Pr
E
Mr
Mc
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Metabolome: Lipids Sugars
Nucleotides Amino acids Metabolites Xenobiotics
Reactive electrophiles: Reactive O, N & Cl species
Aldehydes Epoxides Quinones
Metals Micronutrients Receptor-binding agents:
Hormones Xenoestrogens
Endocrine disruptors
Microbiome products
Bioactive molecules
Drugs
Inflammation markers: Cytokines
Chemokines Eicosanoids
Vasoactive amines Growth factors
SERUM EXPOSOME
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Biomarkers of exposure
Candidate biomarkers
Diseased vs. healthy (case-control studies) Untargeted designs
Diseased vs. healthy (prospective cohorts) Targeted designs
DATA-DRIVEN DISCOVERY (EWAS)
Discriminating features Chemical identification
Biomarkers of disease
Serum exposome
S. Rappaport, Biomarkers, 2012, 17(6), 48: 3-9
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DATA-DRIVEN DISCOVERY (EWAS)
Molecular epidemiology
Systems biology
Genomics , epigenomics, transcriptomics & experiments
Identify sources & measure exposures
Exposure biology
KNOWLEDGE-DRIVEN APPLICATIONS
Causality and prevention
Drug development
Diagnosis, prognosis and treatment
Disease stage and response to therapy
Biomarkers of exposure
Candidate biomarkers
Diseased vs. healthy (case-control studies) Untargeted designs
Diseased vs. healthy (prospective cohorts) Targeted designs
Discriminating features Chemical identification
Biomarkers of disease
Serum exposome
S. Rappaport, Biomarkers, 2012, 17(6), 48: 3-9
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An EWAS of cardiovascular disease
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Wang et al. Nature (2011) 472: 57-63.
From untargeted serum metabolomics (2,000 features), 18 unknown features were associated with cardiovascular disease. Of these, 3 were highly correlated, suggesting a common pathway.
Trimethylamine oxide (TMAO)
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Choline metabolism: a major cause of CVD?
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FMO = flavin monooxygenase
Targeted LC-MS/MS analyses of 1870 subjects from an independent cohort of CVD patients and controls
Dietary choline: eggs, milk, red meat, poultry, seafood and lecithin (food additive)
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Two biomarker-research agendas EWAS
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For disease etiology Data-driven, untargeted designs Focus on small molecules and proteins To identify biomarkers Proof of concept has been established
Follow-up studies o
o
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For etiology, diagnosis and prognosis Knowledge-driven, targeted designs For causative or suspicious factors Use biomarkers to confirm causality, etc. Provide feedback for public health and treatment 19
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Best wishes from Berkeley
Major support from NIEHS through grants U54ES016115 and P42ES04705 and from the ACC Long-range Research Initiative