Building a search engine to find and robustly identify environmental factors with phenotype and disease

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1 Building a search engine to find and robustly identify environmental factors with phenotype and disease Chirag J Patel Unite for Sight 4/14/2018

2 Phenotype Genome Environment P = G + E Type 2 Diabetes Cancer Alzheimer s Gene expression Variants Infectious agents Diet + Nutrients Pollutants Drugs

3 G We are great at G investigation! >4000 (as of 1/1/18) 36,066 G-P associations Genome-wide Association Studies (GWAS)

4 E:??? Nothing comparable to elucidate E influence! We lack high-throughput methods and data to discover new E in P

5 A similar paradigm for discovery should exist for E! Why?

6 σ 2 P = σ 2 G + σ 2 E

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

8 Height is an example of a heritable trait: Francis Galton shows how its done (1887) mid-height of 205 parents described 60% of variability of 928 offspring What else describes height?

9 Heritability estimates for burdensome diseases are low and variable cancer? Type 2 Diabetes (25%) Heart Disease (25-30%) Source: SNPedia.com

10 G estimates for complex disease (P) are low and variable: massive opportunity for high-throughput E discovery σ 2 E Source: SNPedia.com

11 What describes this variation NOT explained by genetics?

12 Physical activity?

13 Is it coffee? HR: 0.9 in N=500K

14 HR: 0.9 in N=50K

15 Chemicals? EPA Chemical Substances List (~80K)

16 we just don t know via tylervigen.com

17 we just don t know xkcd.com

18 We just don t know: Is everything we are exposed to associated with cancer? 50 random ingredients from Boston Cooking School Cookbook Any associated with cancer? Of 50, 40 studied in cancer risk Weak statistical evidence: non-replicated inconsistent effects non-standardized Schoenfeld and Ioannidis, AJCN 2012

19 we just don t know

20 The problem remains: (1) what explains the missing variation in phenotype σ 2 E

21 So the problem remains: (2) and how do we find the stuff that matters? E:??? Diet Infection Pollution Drugs

22 How did genetics-based investigations advance? (And advance so quickly?) G We are great at G investigation! >4000 (as of 1/1/18) 36,066 G-P associations Genome-wide Association Studies (GWAS)

23 A new paradigm of GWAS for discovery of G in P: Human Genome Project to GWAS Sequencing of the genome Characterize common variation Measurement tools 2001 HapMap project: current day High-throughput variant assay < $99 for ~1M variants ~2003 (ongoing) Comprehensive, high-throughput analyses GWAS Nature 2008

24 How can we do better in both discovery and translation?: Leverage data-driven exposomic techniques! Data-driven discovery search through all the possibilities gauge the totality of the evidence New ways to measure the exposome (E)! scalable ways to measure diet, infection, pollution

25 Explaining the missing variation: A data-driven paradigm for robust discovery of E in disease via systematic study of the exposome what to measure? how to measure? how to analyze in relation to health? A more comprehensive view of environmental exposure is needed... to discover major causes of diseases... Wild, 2005, 2012 Rappaport and Smith, 2010, 2011 Buck-Louis and Sundaram 2012 Miller and Jones, 2014 Patel CJ and Ioannidis JPAI, 2014

26 Possible to use existing technologies for E Exposure (and P) Assessment however, heterogeneous measures that require different study designs and analytic approaches. CEBP 2017

27 Promises and Challenges in creating a search engine for identifying E in P JAMA 2014 ARPH 2016 JECH 2014 Curr Epidemiol Rep 2017

28 Examples of data-driven discovery for E associations

>250 exposures (serum + urine) GWAS chip >200 quantitative clinical traits (e.g.

29 Gold standard for breadth of human exposure information: National Health and Nutrition Examination Survey 1 since the 1960s now biannual: 1999 onwards 10,000 participants per survey >250 exposures (serum + urine) GWAS chip >200 quantitative clinical traits (e.g., serum glucose, lipids, body mass index) Death index linkage (cause of death) 1

30 Gold standard for breadth of exposure & behavior data: National Health and Nutrition Examination Survey Nutrients and Vitamins vitamin D, carotenes Drugs statins; aspirin Infectious Agents hepatitis, HIV, Staph. aureus Plastics and consumables phthalates, bisphenol A Physical Activity Pesticides and pollutants e.g., steps atrazine; cadmium; hydrocarbons

31 What E are associated with aging: all-cause mortality, heart disease, and telomere length? Int J Epidem 2013 Int J Epidem 2016

income (quintile 2) serum and urine cadmium [1 SD] male income (quintile 1) past smoker? serum lycopene current smoker?

32 Identifying E associated with all-cause mortality: Data-driven searching through 253 associations physical activity [low, moderate, high activity]* age (10 years) any one smoke in home? income (quintile 2) serum and urine cadmium [1 SD] male income (quintile 1) past smoker? serum lycopene current smoker? [1SD] black income (quintile 3) Multivariate cox (age, sex, income, education, race/ethnicity, occupation [in red]) *derived from METs per activity and categorized by Health.gov guidelines R 2 ~ 14% (2%)

33 R 2 < 10-20% Required

34 What about other factors related to aging?: 452 associations in Telomere Length! Cadmium Trunk Fat CRP Alk. Phos cigs per day Cadmium (urine) retinyl stearate pulse rate PCBs VO2 Max FDR<5% shorter telomeres longer telomeres 2-8 years R 2 ~ 1% adjusted by age, age 2, race, poverty, education, occupation median N=3000; N range: Int J Epidem 2016

35 Interdependencies of the exposome: Correlation globes paint a complex view of exposure for each pair of E: Spearman ρ (575 factors: 81,937 correlations) permuted data to produce null ρ sought replication in > 1 cohort Red: positive ρ Blue: negative ρ thickness: ρ Pac Symp Biocomput JECH. 2015

36 Interdependencies of the exposome: Correlation globes paint a complex view of exposure: average correlation of < 0.3 for each pair of E: Spearman ρ (575 factors: 81,937 correlations) permuted data to produce null ρ sought replication in > 1 cohort Red: positive ρ Blue: negative ρ thickness: ρ Effective number of variables: 500 (10% decrease) Pac Symp Biocomput JECH. 2015

37 How can we do better in both discovery and translation?: Leverage data-driven exposomic techniques! Data-driven discovery search through all the possibilities gauge the totality of the evidence New ways to measure the exposome (E)! scalable ways to measure diet, infection, pollution

38 Data-driven discovery to identifying factors that matter! 1.) Find elusive E in P and explain variation of disease risk 2.) Consideration of totality of evidence: Does my correlation matter? 3.) Machine learning methods to detecting signals in observational and large data

39 Data-driven discovery to identifying factors that matter! 1.) Find elusive E in P and explain variation of disease risk 2.) Consideration of totality of evidence: Does my correlation matter? 3.) Machine learning methods to detecting signals in observational and large data ARPH 2016 JAMA 2014 JECH 2015

40 Data-driven discovery to identifying factors that matter! 1.) Find elusive E in P and explain variation of disease risk 2.) Consideration of totality of evidence: Does my correlation matter? 3.) Machine learning methods to detecting signals in observational and large data ARPH 2016 JAMA 2014 JECH 2015

41 How can we do better in both discovery and translation?: Leverage data-driven exposomic techniques! Data-driven discovery search through all the possibilities gauge the totality of the evidence New ways to measure the exposome (E)! scalable ways to measure diet, infection, pollution

Explaining the missing variation: A data-driven paradigm for robust discovery of E in disease via systematic study of the exposome what to measure? how to measure?

42 Explaining the missing variation: A data-driven paradigm for robust discovery of E in disease via systematic study of the exposome what to measure? how to measure? how to analyze in relation to health? A more comprehensive view of environmental exposure is needed... to discover major causes of diseases... Wild, 2005, 2012 Rappaport and Smith, 2010, 2011 Buck-Louis and Sundaram 2012 Miller and Jones, 2014 Patel CJ and Ioannidis JPAI, 2014

43 Need to assess the exposome globally: (e.g., India and China) c/o Getty Images c/o AFP

44 and Sub-Saharan Africa! Can we predict HIV as a function of the exposome? AIDS 2018

RagGroup Arjun Manrai Nam Pho Jake Chung Kajal Claypool Chirag

Acknowledgements Harvard DBMI Susanne Churchill Nathan Palmer

Kohane John Ioannidis Dennis Bier Hugo Aschard NIH Common Fund Big

45 RagGroup Arjun Manrai Nam Pho Jake Chung Kajal Claypool Chirag Lakhani Danielle Rasooly Alan LeGoallec Sivateja Tangirala Acknowledgements Harvard DBMI Susanne Churchill Nathan Palmer Sophia Mamousette Sunny Alvear Mentioned Collaborators Isaac Kohane John Ioannidis Dennis Bier Hugo Aschard NIH Common Fund Big Data to Knowledge Chirag J Patel

Building a search engine to find environmental factors associated with disease and health

Building a search engine to find environmental factors associated with disease and health Chirag J Patel IEA-WCE 2017 Symposium Saitama, Japan 8/20/17 chirag@hms.harvard.edu @chiragjp www.chiragjpgroup.org