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
[email protected] @chiragjp
www.chiragjpgroup.org
Center for Biomedical Informatics Harvard Medical School Center for Assessment Technology and Continuous Health (CATCH) Massachusetts General Hospital
Thank you
Steven Rappaport
David Balshaw
Toby Athersuch
Erin Baker
Anthony Macharone
Dean JonesAndrew Patterson
Susan Sumner
Oliver Fiehn
Pieter Dorrestein
Elaine Cohen Hubal
Benjamin Blount
Roel Vermeulen
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.
We claimed:
Motivation
P = G + E
P = G + E
P = G + EHeight
Eye colorType 2 Diabetes
Cancer
Phenome
P = G + EHeight
Eye colorType 2 Diabetes
Cancer
Phenome Genome
~10M SNPs
P = G + EHeight
Eye colorType 2 Diabetes
Cancer
Phenome Genome
~10M SNPs
Environment
Infectious agents Nutrients Pollutants
Pharmaceuticals
P = G + EPhenotypes (P) emerge due to genes (G) and environments (E)…
P = G + EPhenotypes (P) emerge due to genes (G) and environments (E)…
... and we’re exposed to many environmental factors of the exposome...
P = G + EPhenotypes (P) emerge due to genes (G) and environments (E)…
but, we lack methods to ascertain and assess high-throughput E.
... and we’re exposed to many environmental factors of the exposome...
... a unified, accessible, cost-effective platform has allowed for high-throughput discovery of G in disease!
G:
image from illumina, inc
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/
G:
image from illumina, inc
O($100)
E?
σP = σG + σE
σG
σP H2 =
Heritability (H2) is the range of phenotypic variability attributed to genetic variability in a population
Indicator of the proportion of phenotypic differences attributed to the genomic differences.
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 exposome research
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 exposome research
exposome
©20
15N
atur
e A
mer
ica,
Inc.
All
righ
ts r
eser
ved.
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. ([email protected]).
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.
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
Explaining the other 51%: A new data-driven and cost-effective paradigm for
discovery of E
NIEHS Exposome workshop (January, 2015): Towards a high-throughput definition of
comprehensive environmental exposures
David Balshaw
Internal exposome
NIEHS Exposome workshop (January, 2015): Towards a high-throughput definition of
comprehensive environmental exposures
David Balshaw
Internal exposome
External exposome
NIEHS Exposome workshop (January, 2015): Towards a high-throughput definition of
comprehensive environmental exposures
David Balshaw
Internal exposome
External exposome
Exposome and biological responses (phenomes)
NIEHS Exposome workshop (January, 2015): Towards a high-throughput definition of
comprehensive environmental exposures
David Balshaw
Internal exposome
External exposome
Exposome and biological responses (phenomes)
Exposome and epidemiology
NIEHS Exposome workshop (January, 2015): Towards a high-throughput definition of
comprehensive environmental exposures
David Balshaw
Internal exposome
External exposome
Exposome and biological responses (phenomes)
Exposome and epidemiology
Exposome data analytics and informatics
E:
Can metabolomics provide the analogous, unified, and cost-effective modality for ascertainment of the exposome?
???
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…
P = G + EMetabolome is both P and E (and G)
Many challenges in using metabolomics technologies to ascertain exposome…
P = G + EMetabolome is both P and E (and G)
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
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)
Data mining of the internal and external exposome
mixtures of exposures
time
drugs
integrative
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 system-atically 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.
mixtures of phenotypes
2015 NIEHS Exposome Workshop (January 2015) Manrai et al (in prep)
