Metabolomics for Characterizing the Human Exposome: The need for a unified and high-throughput way to ascertain environmental exposures

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Metabolomics for Characterizing the Human Exposome: The need for a unified and high-throughput way to ascertain environmental exposures Chirag J Patel 5/28/2015 Center for Biomedical Informatics Harvard Medical School Center for Assessment Technology and Continuous Health (CATCH) Massachusetts General Hospital chirag@hms.harvard.edu @chiragjp www.chiragjpgroup.org

Steven Rappaport David Balshaw Benjamin Blount Roel Vermeulen Toby Athersuch Erin Baker Thank you Elaine Cohen Hubal Oliver Fiehn Pieter Dorrestein Dean Jones Anthony Macharone Andrew Patterson Susan Sumner

We claimed:

We claimed: Metabolomics technologies can enable the comprehensive and accessible assessment of the high-throughput human exposome,

We claimed: Metabolomics technologies can enable the comprehensive and accessible assessment of the high-throughput human exposome, accelerate data-driven discovery in health and disease,

We claimed: Metabolomics technologies can enable the comprehensive and accessible assessment of the high-throughput human exposome, accelerate data-driven discovery in health and disease, and have wide-reaching implications in health policy and decision making.

Motivation

P = G + E

P = G + E

Phenome P = G + E Height Eye color Type 2 Diabetes Cancer

Phenome Genome P = G + E Height ~10M SNPs Eye color Type 2 Diabetes Cancer

Phenome Genome Environment P = G + E Height Eye color Type 2 Diabetes Cancer ~10M SNPs Infectious agents Nutrients Pollutants Pharmaceuticals

Phenotypes (P) emerge due to genes (G) and environments (E) P = G + E

Phenotypes (P) emerge due to genes (G) and environments (E) P = G + E... and we re exposed to many environmental factors of the exposome...

Phenotypes (P) emerge due to genes (G) and environments (E) P = G + E... and we re exposed to many environmental factors of the exposome... but, we lack methods to ascertain and assess highthroughput E.

G: O($100)... a unified, accessible, cost-effective platform has allowed for high-throughput discovery of G in disease! image from illumina, inc

G: O($100)... a unified, accessible, cost-effective platform has allowed for high-throughput discovery of G in disease! >1,400 Genome-wide Association Studies (GWAS) NHGRI GWAS Catalog https://www.genome.gov/ image from illumina, inc

E?

σp = σg + σe

Heritability (H 2 ) is the range of phenotypic variability attributed to genetic variability in a population H 2 = σ G σ P Indicator of the proportion of phenotypic differences attributed to the genomic differences.

H 2 estimates for complex traits are low and variable: massive opportunity for exposome research Stomach cancer Leukemia Lung cancer Colon cancer Bladder cancer Sciatica Cervical cancer Testicular cancer Gallstone disease Type-2 diabetes Longevity Parkinson's disease Osteoarthritis Hypertension Blood pressure, systolic Asthma Stroke Hangover Ovarian cancer Breast cancer QT interval Prostate cancer Heart disease Menopause, age at Insomnia Coronary artery disease Depression Body mass index Blood pressure, diastolic Autism Thyroid cancer Migraine Crohn's disease Rheumatoid arthritis Lupus Alcoholism Sexual orientation Nicotine dependence Menarche, age at Bone mineral density Psoriasis Anorexia nervosa Alzheimer's disease Obesity Bipolar disorder Attention deficit hyperactivity disorder Polycystic ovary syndrome Celiac disease Graves' disease Epilepsy Schizophrenia Height Type-1 diabetes Hair curliness Eye color 0 25 50 75 100 Heritability: Var(G)/Var(Phenotype) Source: SNPedia.com

H 2 estimates for complex traits are low and variable: massive opportunity for exposome research Stomach cancer Leukemia Lung cancer Colon cancer Bladder cancer Sciatica Cervical cancer Testicular cancer Gallstone disease Type-2 diabetes Longevity Parkinson's disease Osteoarthritis Hypertension Blood pressure, systolic Asthma Stroke Hangover Ovarian cancer Breast cancer QT interval Prostate cancer Heart disease Menopause, age at Insomnia Coronary artery disease Depression Body mass index Blood pressure, diastolic Autism Thyroid cancer Migraine Crohn's disease Rheumatoid arthritis Lupus Alcoholism Sexual orientation Nicotine dependence Menarche, age at Bone mineral density Psoriasis Anorexia nervosa Alzheimer's disease Obesity Bipolar disorder Attention deficit hyperactivity disorder Polycystic ovary syndrome Celiac disease Graves' disease Epilepsy Schizophrenia Height Type-1 diabetes Hair curliness Eye color 0 25 50 75 100 Heritability: Var(G)/Var(Phenotype) exposome Source: SNPedia.com

Meta-analysis of the heritability of human traits based on fifty years of twin studies Tinca J C Polderman 1,10, Beben Benyamin 2,10, Christiaan A de Leeuw 1,3, Patrick F Sullivan 4 6, Arjen van Bochoven 7, Peter M Visscher 2,8,11 & Danielle Posthuma 1,9,11 Nature Genetics, 2015 17,804 traits of the phenome 2,748 publications 14,558,903 twin pairs Average H 2 (genome): 0.49 Exposome plays an equal role.

Explaining the other 51%: A new data-driven and cost-effective paradigm for discovery of E A data-driven and accessible and view of the environment is required to discover the cause of burdensome diseases today. Wild, 2005 Rappaport and Smith, 2010, 2011 Buck-Louis and Sundaram 2012 Miller and Jones, 2014 Patel CJ and Ioannidis JPAI, 2014

NIEHS Exposome workshop (January, 2015): Towards a high-throughput definition of comprehensive environmental exposures Internal exposome David Balshaw

NIEHS Exposome workshop (January, 2015): Towards a high-throughput definition of comprehensive environmental exposures Internal exposome External exposome David Balshaw

NIEHS Exposome workshop (January, 2015): Towards a high-throughput definition of comprehensive environmental exposures Internal exposome External exposome Exposome and biological responses (phenomes) David Balshaw

NIEHS Exposome workshop (January, 2015): Towards a high-throughput definition of comprehensive environmental exposures Internal exposome External exposome Exposome and biological responses (phenomes) Exposome and epidemiology David Balshaw

NIEHS Exposome workshop (January, 2015): Towards a high-throughput definition of comprehensive environmental exposures Internal exposome External exposome Exposome and biological responses (phenomes) Exposome and epidemiology Exposome data analytics and informatics David Balshaw

Can metabolomics provide the analogous, unified, and costeffective modality for ascertainment of the exposome? E:??? image from illumina, inc

Many challenges in using metabolomics technologies to ascertain exposome P = G + E

Many challenges in using metabolomics technologies to ascertain exposome Metabolome is both P and E (and G) P = G + E

Many challenges in using metabolomics technologies to ascertain exposome Metabolome is both P and E (and G) P = G + E endogenous vs. exogenous untargeted and targeted technologies temporality and study design e.g., Athersuch, 2012

but possibilities for impactful discovery: big data exposome research examples exposome phenome EWAS, PheWAS..etc. individuals genome (static) mixtures of exposures integrative GWAS, RVAS, pathway analysis..etc. mixtures of phenotypes pollutants diet metabolites xenobiotics drugs gut flora... time CVD BP cancer weight height T2D Figure 1: The exposome is a unified, multi-modal, temporally dependent, and comprehensive digital representation of external and internal environmental exposures linked to humans. Data mining with the exposome can be used to systematically discover relationships between mixtures of exposures, the genome, and mixtures of traits and diseases. In the example above, diet and gut flora are linked with genomic markers to type 2 diabetes and blood pressure.... time 2015 NIEHS Exposome Workshop (January 2015) Manrai et al (in prep)

Thank you chirag@hms.harvard.edu @chiragjp www.chiragjpgroup.org

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