Bochud, Murielle

Malats, Núria

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3 Mardis ER, Wilson RK. Cancer genomesequencing: a review. Hum Mol Genet2009;18:163–8.4 Deisboeck TS. Personalizing medicine: asystems biology perspective. Mol Syst Biol2009;5:249.5 Hood L. A doctor’s vision of the future ofmedicine. Newsweek 13 July 2009.Tobias Schulte in den Ba¨umenInstitute for Public Health Genomics(IPHG), School for Public Health andPrimary Care (CAPHRI), MaastrichtUniversity, The Netherlands,Muin J Khoury, Director, Office of PublicHealth Genomics, CDC, Atlanta, USACorrespondence: Tobias Schulte inden Ba¨umen,e-mail: t.sidb@maastrichtuniversity.nldoi:10.1093/eurpub/ckq201Public health genomics and the challenges for epidemiologyWhile chronic diseases (e.g. obesity,diabetes, coronary heart disease,cancer, etc), also named ‘complex dis-eases’, are caused by the combinedeffects of multiple environmental andgenetic factors, it is estimated that theenvironmental component plays a majorrole. Nevertheless, the smaller role ofinherited genetic factors may partly beexplained by the difficulties in exploringthis component until recently. The hugeamount of genomics data produced bythe fast-developing biotechnologies atan unprecedented speed will probablyhelp in dissecting the genetics under-lying these diseases.During the past 5 years, genome-wideassociation studies (GWAS) identifiedhundreds of genomic loci robustly asso-ciated with common chronic diseases.1While this strategy has provided keynovel insight into disease biology withina very short time scale, it will take manyyears before the impact of GWASfindings can be precisely estimated.GWAS, as currently conducted, is notable to identify rare variants, structuralvariants (e.g. copy number variation),and loci displaying high level of allelicheterogeneity across populations be-cause current standards require associ-ations to be replicated in independentpopulations. Hence, there is still a largefraction of the trait heritability to beexplained, the so-called ‘missing herit-ability’. The current application of mas-sive parallel sequencing (MPS) isidentifying rare variants associated withcommon chronic diseases. The hugeheterogeneity, almost at an individuallevel, of the genetic alterations foundthrough these new technologies empha-sizes the notion of individualized dis-eases. Epidemiologists are now movingthe focus of their analyses from singlegenetic variants to entire genes and/orpathways.Thinking on the biological meaningof the genetic variants, there is probablya continuum between rare monogenicdiseases with few loci exerting largeeffects and common complex traitswith a large number of loci with tinyeffect sizes. Thus, most of the identifiedgenetic variants have very small effectsizes and probably interact with othergenetic variants and with environmentalfactors, though examples of robust andvalidated interactions are still rare.1Hence, most genetic variants identifiedto date, when taken individually, areneither necessary nor sufficient to causedisease but they may have a direct effecton intermediate outcomes such as geneexpression and/or protein function.Accordingly, epidemiologists need todeal with the design of large studiesaiming at integrating genetics, genomics,transcriptomics and proteomics dataat the individual level, together withextensive information on environmentalfactors and behaviours.Regarding the latter, epidemiologistsface the important challenge of assessingthe complexity of highly correlated en-vironmental exposures. We do not haveplatforms able to assess environmentalexposures with the same low measure-ment error as ‘omics’ (in particulargenomics) platforms do. Rather, wecontinue asking individuals about theirlifespan exposures through question-naires, which represent soft data. Yet,selected biomarkers represent excellenttools to measure environmental expos-ures with higher accuracy. A changein paradigm is needed, moving froma candidate to an agnostic/exploratoryexposure analysis. Incorporating epige-nomics (i.e. modifications in DNAmethylation of CpG islands, histoneacetylation, etc) and metabolomicsmarkers should help in better dissectingthe still ‘missing exposurome’ for mostchronic diseases. Tools for standardizedcollection across centres and acrosscountries will be needed to this end.Furthermore, the notion that nothingis static during an individual’s lifespan isbecoming more and more important.The changes along the time apply toenvironmental exposures and to ‘omics’data. Taking such lifelong modificationsinto account in epidemiological studiesis going to be a very difficult task thatwill necessitate to closely monitor indi-viduals. Once the data is available, itsmodelling will represent anotherchallenge.Following the current concept ofepidemiological study design and statis-tical power requirements, very largesample sizes are needed to explore theunderlying biological complexity in ameaningful manner. Being provoca-tive, we could argue that instead ofconducting large scale epidemiologicalstudies, epidemiologist should focus onfewer extremely very well characterizedand bio-monitored individuals. In anycase, the integration of several typesof data, from environmental exposuresto epigenetics, metabolomics and gen-omics, requires the development ofinnovative bioinformatics and data re-duction techniques. There is still a longway to go.The extraordinary development ofhypothesis-free (agnostic) approachesshould not discourage researchers fromconducting targeted candidate genestudies. Both approaches should beviewed as complementary and synergis-tic. Similarly, although most GWAShave included unrelated people, family-based studies may bring valuable infor-mation on transgenerational effects,shared environmental factors andparent-of-origin effects. An exampleillustrating future challenges in termsof study design can be found in the fieldof pharmacogenomics that aims at iden-tifying genetic variants involved in drugresponse in order to improve drug safetyand efficacy, thus minimizing side ef-fects. There are large inter-individualvariations in the activity of enzymesinvolved in drug metabolism and trans-port that are in large part geneticallydetermined. In contrast to the smalleffect of the identified variantsassociated with the risk of commoncomplex traits, the effect ofViewpoints 5pharmacogenomic-related variants maybe larger and clinically relevant. Therecently launched Clarification ofOptimal Anticoagulation throughGenetics (COAG) double-blind, rando-mized controlled trial2 will ascertainwhether adapting the dose of warfarintherapy based on genetic variantslocated within the CYP2C9 andVKORC1 genes may improve patientcare as compared with aclinically-guided dosing algorithm.Designing such a trial is particularlychallenging because the power to detecta pre-specified between-group differ-ence will depend on the geneticmakeup of the participants. The chal-lenge comes from the fact that allelefrequencies may vary substantiallyacross ethnic groups. To recommendgenetic testing, investigators will need todemonstrate that drug dosing based ongenetic information significantly re-duces costs and morbidity.3Focusing on a single disease or on asingle trait does not allow understandingthe full range of phenotypes associatedwith many genes, for which pleiotropiceffects have been described (i.e. onegene may be involved in both cancerand cardiovascular disease). Hence, anadditional challenge for epidemiologistsis collecting extensive phenotypic data,not only at a single point in time, butlongitudinally, again. The collection ofhigh quality phenotypes and more com-prehensive phenomes are therefore ofutmost importance and will be key tobetter account for the underlying bio-logical complexity of human organismsliving in selected environmental condi-tions.4,5 The digitalization of patient’srecords and imaging technologies, aswell as web-based testing, should allowaccumulating and linking massiveamounts of information for eachperson. The availability of entire gen-omes and phenomes may revolutionizethe way we classify diseases. There is littledoubt that data-gathering technologyhas dramatically changed and willcontinue to largely influence the wayepidemiologists conduct research.Making best use of all the availableinformation, without harming studyparticipants (i.e. discrimination by in-surance companies or employers, undueaccess to the data by third parties, etc),will be a challenging task in the years tocome.In conclusion, recent advances ingenomics have highlighted the polygen-ic nature of most common disorders.The effects of these genetic variants alsoneed to be studied taking into accounttime-dependent environmental and be-havioural factors.5 As a consequence,any single genetic variant has littleimpact in terms of disease risk predic-tion. Yet, polygenic risk scores in rela-tion to continuous traits (i.e. BMI,blood lipid, blood pressure, etc) shouldstimulate public health researchers tochange paradigms and consider integra-tion of multilayer biological data, dy-namic designs, agnostic approaches, aswell as using quantitative measures inassessing both exposures and outcomes(i.e. continuum of affectedness). Suchquantitative thinking leads to a publichealth model that focuses on preventionon a continuous scale rather than justtreating cases. Looking at multivariatecontinuous dimensions rather than clin-ical diagnoses using arbitrary cut-offsrepresent more powerful approaches todecipher the complex etiological mech-anisms leading to human diseases.Large inter-disciplinary teams areneeded to properly design studiesand collect, store and analyse high-throughput data. Whereas the prices of‘omics’ data production have dramatic-ally come down, the costs of data storageand analysis are very high and often tendto be underestimated. Unless studiesare not well funded, epidemiology willnot be able to assume the challengesmentioned above. High-quality andcontinuously updated education pro-grammes are needed to ensure thatresearchers and health-careprofessionals are able to critically ap-praise research findings in the ‘omics’fields, including ‘epi-omics’.FundingMurielle Bochud is supported by theSwiss School of Public Health Plus,Nu´ria Malats by EU-FP7-PHGEN II#2008302.References1 Manolio TA. Genomewide association studiesand assessment of the risk of disease. N Engl JMed 2010;363:166–76.2 French B, Joo J, Geller NL, et al.Anticoagulation Through Genetics InvestigatorsCC. Statistical design of personalized medicineinterventions: the Clarification of OptimalAnticoagulation through Genetics (COAG)trial. Trials 2010;11:108.3 Hall WD, Mathews R, Morley KI. Beingmore realistic about the public health impactof genomic medicine. PLoS Med2010;7:e1000347.4 Houle D, Govindaraju DR, Omholt S.Phenomics: the next challenge. Nat Rev Genet2010;11:855–66.5 Ioannidis JP, Loy EY, Poulton R, et al.Researching genetic versus nongenetic deter-minants of disease: a comparison and proposedunification. Sci Transl Med 2009;1:7ps8.Murielle Bochud1, Nu´ria Malats21Institute of Social and Preventive Medicine,Centre Hospitalier Universitaire Vaudoisand University of Lausanne, Lausanne,Switzerland, 2Genetic and MolecularEpidemiology Group, Spanish National CancerResearch Centre (CNIO), Madrid, SpainCorrespondence: Nu´ria Malats, Grupo deEpidemiologı´a Gene´tica y Molecular,Programa de Gene´tica del Ca´ncer Humano,Centro Nacional de InvestigacionesOncolo´gicas (CNIO), Melchor Ferna´ndezAlmagro, 3, 28029-Madrid, Espan˜a.e-mail: nuria@cnio.esdoi:10.1093/eurpub/ckq203A European view on the future of personalised medicine in the EUAt the European Health Forum Gasteinin October 2010, personalized medicinewas presented as a new paradigm forhealth care in Europe. I had the oppor-tunity to participate in this session andto listen to the views of my esteemedco-participants on what can be done atthe European level to achieve imple-mentation of personalized medicineapproaches and other emergingtechnologies.Personalized medicine is becoming anever more important area for the appli-cation of genome-based informationand technologies identified underpublic health genomics. The scope andvision of public health genomics,according to the 2005 BellagioConference1 is ‘the responsible andeffective translation of genome-basedknowledge and technologies for thebenefit of population health’.This concept encompasses bothpopulation and individual health. Itcovers prevention, treatment and care.Genomics is clearly an area of in-novative research that seeks to close the6 Viewpoints

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