Informatics and data analytics to support exposome-based discovery

Perspectives from a NIEHS workshop

Chirag J Patel International Society of Exposure Science

Henderson, NV (by way of Boston, MA) 10/20/15

chirag@hms.harvard.edu @chiragjp

www.chiragjpgroup.org

Arjun Manrai (Harvard)* Yuxia Cui (NIEHS)

Pierre Bushel (NIEHS) Molly Hall (Penn State, now U Penn)*

Spyros Karakitsios (Aristotle U, Greece) Carolyn Mattingly (NCSU)

Marylyn Ritchie (Geisinger Health/Penn State) Charles Schmitt (NIEHS)

Denis Sarigiannis (Aristotle U, Greece) Duncan Thomas (USC)

David Wishart (U Alberta, Canada) David Balshaw (NIEHS)

The workgroup discussed informatics capability for high-throughput exposome research

(late 2014 to early 2015)

We are now in the era of high-throughput biology and biomedicine.

(now possible to assay thousands to millions of datapoints today)

We are now in the era of high-throughput biology and biomedicine: examples of genomic advances

genetic arrays gene expression

common genetic variants epigenome (methylation)

whole genome sequencing (WGS) full genome sequencing

mRNA-seq epigenome (3D, histone)

3 x 109 nucleotide bases

3-4 x 104 genes106 to 107 variants

Informatics has enabled discovery in genomics investigations.

1. infrastructure/standards,

2. analytics,

3. databases

Information infrastructure has enabled discovery in genomics (example: UCSC genome browser)

Analytic methods have enabled discovery in genomics (example: genome-wide association [GWAS])

A search engine for genetic influence in phenotypes Genome-wide association studies (GWASs)

580 VOLUME 42 | NUMBER 7 | JULY 2010 NATURE GENETICS

A RT I C L E S

13 autosomal loci exceeded the threshold for genome-wide significance (P ranging from 2.8 × 10−8 to 1.4 × 10−22) with allele-specific odds ratios (ORs) between 1.06 and 1.14 (Table 1 and Fig. 2). All signals remained close to or beyond genome-wide significance thresholds (the least significant P value was 5.2 × 10−8) when we repeated analyses after implementing a second (post meta-analysis) round of genomic control adjustment within stage 1 data (Supplementary Note).

We extended our search for susceptibility signals to the X chromo-some, identifying one further signal in the stage 1 discovery samples meeting our criteria for follow-up (represented by rs5945326, near DUSP9, P = 2.3 × 10−6). This SNP showed strong evidence for replication in 8,535 cases and 12,326 controls (OR (allowing for X-inactivation) 1.32 (95% CI 1.16–1.49), P = 2.3 × 10−5), for a combined association P value of 3.0 × 10−10 (OR 1.27 (95% CI 1.18–1.37)) (Table 1 and Fig. 2).

Fourteen signals reaching genome-wide significanceTwo of the 14 signals reaching genome-wide significance on joint analysis (those near MTNR1B and IRS1) represent loci for which T2D associations have been recently reported in samples which partially overlap with those studied here10,14–16 (Table 1).

A third signal (rs231362) on 11p15 overlaps both intron 11 of KCNQ1 and the KCNQ1OT1 transcript that controls regional imprint-ing17 and influences expression of nearby genes including CDKN1C, a known regulator of beta-cell development18. This signal maps ~150 kb from T2D-associated SNPs in the 3 end of KCNQ1 first identified in East Asian GWA scans8,9. SNPs within the 3 signal were also detected in the current DIAGRAM+ meta-analysis (for example, rs163184, P = 6.8 × 10−5), but they failed to meet the threshold for initiating replication. A SNP in the 3 region (rs2237895) that was reported to reach genome-wide significance in Danish samples9 was neither typed nor imputed in the DIAGRAM+ studies. In our European-descent samples, rs231362 and SNPs in the 3 signal were not correlated

(r2 < 0.05), and conditional analyses (see below) establish these SNPs as independent (Fig. 2 and Supplementary Table 4). Further analysis in Icelandic samples has shown that both associations are restricted to the maternally transmitted allele11. Both T2D loci are independent of the common variant associations with electrocardiographic QT intervals that map at the 5 end of KCNQ1 (r2 < 0.02, D < 0.35 in HapMap European CEU data)19,20 (Supplementary Table 5).

Of the remaining loci, two (near BCL11A and HNF1A) have been highlighted in previous studies7,21–23 but are now shown to reach genome-wide significance. Rare mutations in HNF1A account for a substantial proportion of cases of maturity onset diabetes of the young, and a population-specific variant (G319S) influences T2D risk in Oji-Cree Indians24. Confirmation of a common variant association at HNF1A brings to five the number of loci known to harbor both rare mutations causal for monogenic forms of diabetes and common vari-ants predisposing to multifactorial diabetes, the others being PPARG, KCNJ11, WFS1 and HNF1B. A T2D association in the BCL11A region was suggested by the earlier DIAGRAM meta-analysis (rs10490072, P = 3 × 10−5), but replication was inconclusive7; there is only modest linkage disequilibrium (LD) between rs10490072 and the lead SNP from the present analysis (rs243021, r2 = 0.22, D = 0.73 in HapMap CEU).

The remaining nine signals map near the genes HMGA2, CENTD2, KLF14, PRC1, TP53INP1, ZBED3, ZFAND6, CHCHD9 and DUSP9 (Table 1 and Figs. 1 and 2) and represent new T2D risk loci uncovered by the DIAGRAM+ meta-analysis.

Understanding the genetic architecture of type 2 diabetesCombining newly identified and previously reported loci and assuming a multiplicative model, the sibling relative risk attributable to the 32 T2D susceptibility variants described in this paper is ~1.14. With addition of the five T2D loci recently identified by the Meta-Analysis of Glucose and Insulin-related traits Consortium (MAGIC) investigators12,13 and

50 Locus established previouslyLocus identified by current studyLocus not confirmed by current study

BCL11A

THADANOTCH2

ADAMTS9

IRS1

IGF2BP2

WFS1ZBED3

CDKAL1

HHEX/IDEKCNQ1 (2 signals*: )

TCF7L2

KCNJ11CENTD2MTNR1B

HMGA2 ZFAND6PRC1

FTOHNF1B DUSP9

Conditional analysis

Unconditional analysis

TSPAN8/LGR5HNF1A

CDC123/CAMK1DCHCHD9

CDKN2A/2BSLC30A8

TP53INP1

JAZF1KLF14

PPAR

40

30

–log

10(P

)–l

og10

(P)

20

10

10

1 2 3 4 5 6 7 8Chromosome

9 10 11 12 13 14 15 16 17 18 19 20 21 22 X0

0

Suggestive statistical association (P < 1 10–5)

Association in identified or established region (P < 1 10–4)

Figure 1 Genome-wide Manhattan plots for the DIAGRAM+ stage 1 meta-analysis. Top panel summarizes the results of the unconditional meta-analysis. Previously established loci are denoted in red and loci identified by the current study are denoted in green. The ten signals in blue are those taken forward but not confirmed in stage 2 analyses. The genes used to name signals have been chosen on the basis of proximity to the index SNP and should not be presumed to indicate causality. The lower panel summarizes the results of equivalent meta-analysis after conditioning on 30 previously established and newly identified autosomal T2D-associated SNPs (denoted by the dotted lines below these loci in the upper panel). Newly discovered conditional signals (outside established loci) are denoted with an orange dot if they show suggestive levels of significance (P < 10−5), whereas secondary signals close to already confirmed T2D loci are shown in purple (P < 10−4).

Voight et al, Nature Genetics 2012 N=8K T2D, 39K Controls

GWAS in Type 2 Diabetes

758,000 individuals >400 studies

>>1B datapoints (genotypes and phenotypes) >950 manuscripts (Paltoo et al., Nature Genetics 2014)

Accessible data repositories have enabled discovery in genomics investigation:

(ex: Databases of Genotypes and Phenotypes)

We claim that there is need for informatics analytic methods, databases, and standards for the exposome-driven discovery.

EWAS akin to GWAS?

Why?courtesy: colabria.com

P = G + E

σ2P = σ2G + σ2E

σ2G

σ2P H2 =

Heritability (H2) is the range of phenotypic variability attributed to genetic variability in a population

Eye colorHair curliness

Type-1 diabetesHeight

SchizophreniaEpilepsy

Graves' diseaseCeliac disease

Polycystic ovary syndromeAttention deficit hyperactivity disorder

Bipolar disorderObesity

Alzheimer's diseaseAnorexia nervosa

PsoriasisBone mineral density

Menarche, age atNicotine dependence

Sexual orientationAlcoholism

LupusRheumatoid arthritis

Crohn's diseaseMigraine

Thyroid cancerAutism

Blood pressure, diastolicBody mass index

DepressionCoronary artery disease

InsomniaMenopause, age at

Heart diseaseProstate cancer

QT intervalBreast cancer

Ovarian cancerHangoverStrokeAsthma

Blood pressure, systolicHypertensionOsteoarthritis

Parkinson's diseaseLongevity

Type-2 diabetesGallstone diseaseTesticular cancer

Cervical cancerSciatica

Bladder cancerColon cancerLung cancerLeukemia

Stomach cancer

0 25 50 75 100Heritability: Var(G)/Var(Phenotype) Source: SNPedia.com

H2 estimates for complex traits are low and variable: massive opportunity for high-throughput E research

Type 2 Diabetes (25%)

Heart Disease (25-30%)

Autism (50%???)

Gaugler et al, Nature Genetics (2014)

Eye colorHair curliness

Type-1 diabetesHeight

SchizophreniaEpilepsy

Graves' diseaseCeliac disease

Polycystic ovary syndromeAttention deficit hyperactivity disorder

Bipolar disorderObesity

Alzheimer's diseaseAnorexia nervosa

PsoriasisBone mineral density

Menarche, age atNicotine dependence

Sexual orientationAlcoholism

LupusRheumatoid arthritis

Crohn's diseaseMigraine

Thyroid cancerAutism

Blood pressure, diastolicBody mass index

DepressionCoronary artery disease

InsomniaMenopause, age at

Heart diseaseProstate cancer

QT intervalBreast cancer

Ovarian cancerHangoverStrokeAsthma

Blood pressure, systolicHypertensionOsteoarthritis

Parkinson's diseaseLongevity

Type-2 diabetesGallstone diseaseTesticular cancer

Cervical cancerSciatica

Bladder cancerColon cancerLung cancerLeukemia

Stomach cancer

0 25 50 75 100Heritability: Var(G)/Var(Phenotype) Source: SNPedia.com

H2 estimates for complex traits are low and variable: massive opportunity for high-throughput E research

H2 < 50%

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NATURE GENETICS ADVANCE ONLINE PUBLICATION 1

A N A LY S I S

Despite a century of research on complex traits in humans, the relative importance and specific nature of the influences of genes and environment on human traits remain controversial. We report a meta-analysis of twin correlations and reported variance components for 17,804 traits from 2,748 publications including 14,558,903 partly dependent twin pairs, virtually all published twin studies of complex traits. Estimates of heritability cluster strongly within functional domains, and across all traits the reported heritability is 49%. For a majority (69%) of traits, the observed twin correlations are consistent with a simple and parsimonious model where twin resemblance is solely due to additive genetic variation. The data are inconsistent with substantial influences from shared environment or non-additive genetic variation. This study provides the most comprehensive analysis of the causes of individual differences in human traits thus far and will guide future gene-mapping efforts. All the results can be visualized using the MaTCH webtool.

Specifically, the partitioning of observed variability into underlying genetic and environmental sources and the relative importance of additive and non-additive genetic variation are continually debated1–5. Recent results from large-scale genome-wide association studies (GWAS) show that many genetic variants contribute to the variation in complex traits and that effect sizes are typically small6,7. However, the sum of the variance explained by the detected variants is much smaller than the reported heritability of the trait4,6–10. This ‘missing heritability’ has led some investigators to conclude that non-additive variation must be important4,11. Although the presence of gene-gene interaction has been demonstrated empirically5,12–17, little is known about its relative contribution to observed variation18.

In this study, our aim is twofold. First, we analyze empirical esti-mates of the relative contributions of genes and environment for virtually all human traits investigated in the past 50 years. Second, we assess empirical evidence for the presence and relative importance of non-additive genetic influences on all human traits studied. We rely on classical twin studies, as the twin design has been used widely to disentangle the relative contributions of genes and environment, across a variety of human traits. The classical twin design is based on contrasting the trait resemblance of monozygotic and dizygotic twin pairs. Monozygotic twins are genetically identical, and dizygotic twins are genetically full siblings. We show that, for a majority of traits (69%), the observed statistics are consistent with a simple and parsi-monious model where the observed variation is solely due to additive genetic variation. The data are inconsistent with a substantial influence from shared environment or non-additive genetic variation. We also show that estimates of heritability cluster strongly within functional domains, and across all traits the reported heritability is 49%. Our results are based on a meta-analysis of twin correlations and reported variance components for 17,804 traits from 2,748 publications includ-ing 14,558,903 partly dependent twin pairs, virtually all twin studies of complex traits published between 1958 and 2012. This study provides the most comprehensive analysis of the causes of individual differences in human traits thus far and will guide future gene-mapping efforts. All results can be visualized with the accompanying MaTCH webtool.

RESULTSThe distribution of studied traits is nonrandomWe systematically retrieved published classical twin studies in which observed variation in human traits was partitioned into genetic and environmental influences. For each study, we collected reported

Meta-analysis of the heritability of human traits based on fifty years of twin studiesTinca J C Polderman1,10, Beben Benyamin2,10, Christiaan A de Leeuw1,3, Patrick F Sullivan4–6, Arjen van Bochoven7, Peter M Visscher2,8,11 & Danielle Posthuma1,9,11

1Department of Complex Trait Genetics, VU University, Center for Neurogenomics and Cognitive Research, Amsterdam, the Netherlands. 2Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia. 3Institute for Computing and Information Sciences, Radboud University Nijmegen, Nijmegen, the Netherlands. 4Center for Psychiatric Genomics, Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA. 5Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina, USA. 6Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. 7Faculty of Sciences, VU University, Amsterdam, the Netherlands. 8University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Queensland, Australia. 9Department of Clinical Genetics, VU University Medical Center, Neuroscience Campus Amsterdam, Amsterdam, the Netherlands. 10These authors contributed equally to this work. 11These authors jointly supervised this work. Correspondence should be addressed to D.P. (d.posthuma@vu.nl).

Received 13 February; accepted 1 April; published online 18 May 2015; doi:10.1038/ng.3285

Insight into the nature of observed variation in human traits is impor-tant in medicine, psychology, social sciences and evolutionary biology. It has gained new relevance with both the ability to map genes for human traits and the availability of large, collaborative data sets to do so on an extensive and comprehensive scale. Individual differences in human traits have been studied for more than a century, yet the causes of variation in human traits remain uncertain and controversial.

Nature Genetics, 2015

17,804 traits of the phenome 2,748 publications

14,558,903 twin pairs

Average H2 (genome): 0.49

Exposome plays an equal role.

What is the potential chemical (external and internal) space of the exposome?: perhaps on the order of thousands.

>84,000TSCA and EPA Inventory

(2014)

>13,000Davis et al

Comparative Tox DB (2015)

3,600 + 1,634Toxic Exposome Database

Wishart et al (2015)

toxicants drugs

100-1,000? uBiome

What will the exposome data structure look like?: a high-dimensioned 3D matrix of (1) exposure measurements

on (2) individuals as a function of (3) time

time

exposome phenome

pollutants

diet

metabolites . . .

gut flora

height

weightCVD

BPT2D

cancer

xenobiotics . . .

indi

vidu

als

GWAS, RVAS, pathway analysis..etc.

EWAS, PheWAS..etc.

geno

me

(sta

tic)

mixtures of exposures

time

drugs

integrative

mixtures of phenotypes

(A) (C)

(B)

exposome factors

nutrient value for individual i

individual i

What will the exposome data structure look like?: a high-dimensioned 3D matrix of (1) exposure measurements

on (2) individuals as a function of (3) time

time

exposome phenome

pollutants

diet

metabolites . . .

gut flora

height

weightCVD

BPT2D

cancer

xenobiotics . . .

indi

vidu

als

GWAS, RVAS, pathway analysis..etc.

EWAS, PheWAS..etc.

geno

me

(sta

tic)

mixtures of exposures

time

drugs

integrative

mixtures of phenotypes

(A) (C)

(B)

longitudinal

system

genome

Data-driven investigation for novel exposome factors in the phenome: Exposome-wide, phenome-wide, and genome-exposome-wide discovery

time

exposome phenome

pollutants

diet

metabolites . . .

gut flora

height

weightCVD

BPT2D

cancer

xenobiotics . . .

indi

vidu

als

GWAS, RVAS, pathway analysis..etc.

EWAS, PheWAS..etc.

geno

me

(sta

tic)

mixtures of exposures

time

drugs

integrative

mixtures of phenotypes

(A) (C)

(B)

Informatics methods to integrate heterogeneous data (E, G, and P) and to conduct EWAS, GxEWAS, and PheWAS

EWAS

PheWAS

Integration challenges in conducting data-driven investigation for novel exposome factors in the phenome:

The exposome is heterogenous and G does not equal E.

platform scale

time-dependent type

correlation

mass-spec: targeted vs. untargeted external vs. internal

sampling and life trajectories continuous vs. categorical

dense!

Interdependencies of the exposome: Correlation globes paint a dense and complex view of exposure

JAMA 2015 Pac Symp Biocomput. 2015

σ2P = σ2G + σ2E

σ2E ???

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Albumin, urineUric acidPhosphorusOsmolalitySodiumPotassiumCreatinineChlorideTotal calciumBicarbonateBlood urea nitrogenTotal proteinTotal bilirubinLactate dehydrogenase LDHGamma glutamyl transferaseGlobulinAlanine aminotransferase ALTAspartate aminotransferase ASTAlkaline phosphotaseAlbuminMethylmalonic acidPSA. totalProstate specific antigen ratioTIBC, Frozen SerumRed cell distribution widthRed blood cell countPlatelet count SISegmented neutrophils percentMean platelet volumeMean cell volumeMean cell hemoglobinMCHCHemoglobinHematocritFerritinProtoporphyrinTransferrin saturationWhite blood cell countMonocyte percentLymphocyte percentEosinophils percentC-reactive proteinSegmented neutrophils numberMonocyte numberLymphocyte numberEosinophils numberBasophils numbermean systolicmean diastolic60 sec. pulse:60 sec HRTotal CholesterolTriglyceridesGlucose, serumInsulinHomocysteineGlucose, plasmaGlycohemoglobinC-peptide: SILDL-cholesterolDirect HDL-CholesterolBone alkaline phosphotaseTrunk FatLumber Pelvis BMDLumber Spine BMDHead BMDTrunk Lean excl BMCTotal Lean excl BMCTotal FatTotal BMDWeightWaist CircumferenceTriceps SkinfoldThigh CircumferenceSubscapular SkinfoldRecumbent LengthUpper Leg LengthStanding HeightHead CircumferenceMaximal Calf CircumferenceBody Mass Index

-0.4 -0.2 0 0.2 0.4

Value

050

100

150

Color Keyand Histogram

Count

http://bit.ly.com/pemap

phen

otyp

es

exposures

+- EWAS-derived phenotype-exposure association map: A 2-D view of 86 phenotype by 252 exposure associations

TriglyceridesTotal CholesterolLDL-cholesterol

Trunk FatAlbumin, urine

InsulinTotal Fat

Head CircumferenceBlood urea nitrogen

AlbuminHomocysteineC-peptide: SI

C-reactive proteinBody Mass Index

FerritinThigh Circumference

Maximal Calf CircumferenceDirect HDL-Cholesterol

Total calciumTotal bilirubin

Red cell distribution widthGamma glutamyl transferase

Mean cell volumeMean cell hemoglobinWhite blood cell count

Uric acidProtoporphyrinHemoglobinTotal protein

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HematocritWeight

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Trunk Lean excl BMCMethylmalonic acid

Triceps SkinfoldLymphocyte number

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Segmented neutrophils numberLactate dehydrogenase LDH

Bone alkaline phosphotaseTIBC, Frozen Serum

Aspartate aminotransferase ASTPhosphorus

Lumber Pelvis BMDGlycohemoglobin

GlobulinChloride

BicarbonateAlanine aminotransferase ALT

60 sec. pulse:Upper Leg Length

Total BMDPotassium

Glucose, serumGlucose, plasma

Red blood cell countLumber Spine BMD

Platelet count SIMCHC

OsmolalityMonocyte number

mean systolicLymphocyte percent

Segmented neutrophils percentRecumbent Length

Eosinophils numberMonocyte percent

Head BMDmean diastolic

Prostate specific antigen ratio60 sec HR

Basophils numberSodium

PSA, freeMean platelet volume

Eosinophils percentPSA. total

Basophils percent

0 10 20 30 40R^2 * 100

1 to 66 exposures identified for 81 phenotypes

Additive effect of E factors: Describe less than 10% of variability in P

(On average: 8%)

Stan Shaw, Hugues Aschard, JP Ioannidis

σ2E?

Exposome may enable realization of

remainder of P (>40%)

Recall: H2 <= 50%

What do we do now? Recommendations from the workgroup

Data workgroup recommendation highlights

Comprehensive catalog of documented environmental associations (e.g., risk, variance explained) to strengthen case for exposome.

Where is evidence robust (e.g., air pollution and CVD)? Where do we see non-replication?

Where is heritability low and ripe for exposome?

Identify technologies that can measure the exposome. Targeted and untargeted metabolomics.

Develop high-throughput data analytic capability. Statistical methodologies for the 3D matrix!

Encourage a shift from 1 E to many Es. Link external and internal exposome measures.

Data workgroup recommendation highlights

time

exposome phenome

pollutants

diet

metabolites . . .

gut flora

height

weightCVD

BPT2D

cancer

xenobiotics . . .

indi

vidu

als

GWAS, RVAS, pathway analysis..etc.

EWAS, PheWAS..etc.

geno

me

(sta

tic)

mixtures of exposures

time

drugs

integrative

mixtures of phenotypes

(A) (C)

(B)

Develop data repositories to house and disseminate individual-level exposome data.

Assess the variability of the exposome in diverse populations

Data workgroup recommendation highlights

Identify data standards for exposome research. Develop data standards to enable the re-use of research to build

large exposome-rich cohorts.

Identify analytics standards for reproducible research. Software libraries and tools to share methods and findings.

Incentivize other parties (e.g., researchers, funders, and industry) to integrate the exposome in their existing programs.

Data workgroup recommendation highlights

Educate.

Identify example datasets (e.g., NHANES, DEMOCOPHES). Hackathons and challenges to recruit data scientists.

Develop big data training support (e.g., K awards) directed at exposome-related research

google: “niehs chear”

Informatics will enable us to decipher the role of the emerging exposome in phenotypes to capture the missing σ2P

σ2P = σ2G + σ2E

Arjun Manrai (Harvard)* Yuxia Cui (NIEHS)

Pierre Bushel (NIEHS) Molly Hall (Penn State, now Penn)*

Spyros Karakitsios (Aristotle U, Greece) Carolyn Mattingly (NCSU)

Marylyn Ritchie (Geisinger/Penn State) Charles Schmitt (NIEHS)

Denis Sarigiannis (Aristotle U, Greece) Duncan Thomas (USC)

David Wishart (U Alberta, Canada) David Balshaw (NIEHS)

Thanks again to the group:

Funded in part by the NIEHS.

chirag@hms.harvard.edu @chiragjp

www.chiragjpgroup.org

Thank you.