Make it HuGE: human genome epidemiology reviews, population health, and the IJE. Int J Epidemiol

University of Bristol, Bristol, England, United Kingdom
International Journal of Epidemiology (Impact Factor: 9.18). 07/2006; 35(3):507-10. DOI: 10.1093/ije/dyl071
Source: PubMed


The International Journal of Epidemiology is concerned with scientific evidence that can ultimately form the basis of strategies for improving population health. Hence, the IJE would be expected to remain cautious about the technological advances heralded by the sequencing of the human genome. The classical epidemiological approaches of examining secular trends in disease risk, changes in risk consequent upon migration, and differences in disease rates between populations indicate that little of the global burden of common disease can be attributed to simple differences in genetically determined risk. It is not surprising that many social epidemiologists and public health practitioners (including, in the past, some of the authors of this editorial) have pointed this out. More surprising, perhaps, is that in the spirit of honest accounting, some geneticists and genetic epidemiologists have also punctured the inflated claims of genetic epidemiology by emphasizing that the population-attributable risk of most common genetic variants will be low and that in any case the influence of genetic factors is not reversible through changing genetic make-up. Thus Terwilliger and Weiss 1 point out that alleles identified as

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Available from: Marta Gwinn, Jan 22, 2014
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    • "This means that, before clinical applications can be considered, all the accumulated knowledge about a set of gene variants must be carefully assessed, including prevalence in different populations, the strength of the association with different disease endpoints, and interactions between gene variants and social and environmental determinants of risk. This results in rigorous systematic reviews and meta-analyses of genetic associations, the HuGE reviews [11]. HuGENet further maintains a continuously updated knowledge base, called the HuGE Navigator which lists all the reported genetic associations with certain traits [12]. "
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    ABSTRACT: The completion of the Human Genome Project triggered a whole new field of genomic research which is likely to lead to new opportunities for the promotion of population health. As a result, the distinction between genetic and environmental diseases has faded. Presently, genomics and knowledge deriving from systems biology, epigenomics, integrative genomics or genome-environmental interactions give a better insight on the pathophysiology of common diseases. However, it is barely used in the prevention and management of diseases. Together with the boost in the amount of genetic association studies, this demands for appropriate public health actions. The field of Public Health Genomics analyses how genome-based knowledge and technologies can responsibly and effectively be integrated into health services and public policy for the benefit of population health. Environmental exposures interact with the genome to produce health information which may help explain inter-individual differences in health, or disease risk. However today, prospects for concrete applications remain distant. In addition, this information has not been translated into health practice yet. Therefore, evidence-based recommendations are few. The lack of population-based research hampers the evaluation of the impact of genomic applications. Public Health Genomics also evaluates the benefits and risks on a larger scale, including normative, legal, economic and social issues. These new developments are likely to affect all domains of public health and require rethinking the role of genomics in every condition of public health interest. This article aims at providing an introduction to the field of and the ideas behind Public Health Genomics.
    12/2011; 69(1):8. DOI:10.1186/0778-7367-69-8
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    • "The development of high throughput techniques has resulted in an explosion of available genetic and genomic information . This creates challenges in analyzing, synthesizing and finally translating this rapidly accumulating evidence in useful clinical and public health applications (Burke et al. 2006; Guttmacher and Collins 2003; Higgins et al. 2007; Smith et al. 2006). Human genome epidemiology addresses associations between genetic variation and risk for complex common diseases. "
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    ABSTRACT: Meta-analysis offers the opportunity to combine evidence from retrospectively accumulated or prospectively generated data. Meta-analyses may provide summary estimates and can help in detecting and addressing potential inconsistency between the combined datasets. Application of meta-analysis in genetic associations presents considerable potential and several pitfalls. In this review, we present basic principles of meta-analytic methods, adapted for human genome epidemiology. We describe issues that arise in the retrospective or the prospective collection of relevant data through various sources, common traps to consider in the appraisal of evidence and potential biases that may interfere. We describe the relative merits and caveats for common methods used to trace inconsistency across studies along with possible reasons for non-replication of proposed associations. Different statistical models may be employed to combine data and some common misconceptions may arise in the process. Several meta-analysis diagnostics are often applied or misapplied in the literature, and we comment on their use and limitations. An alternative to overcome limitations arising from retrospective combination of data from published studies is to create networks of research teams working in the same field and perform collaborative meta-analyses of individual participant data, ideally on a prospective basis. We discuss the advantages and the challenges inherent in such collaborative approaches. Meta-analysis can be a useful tool in dissecting the genetics of complex diseases and traits, provided its methods are properly applied and interpreted.
    Human Genetics 03/2008; 123(1):1-14. DOI:10.1007/s00439-007-0445-9 · 4.82 Impact Factor
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    • "This assumption is seldom likely to be true as environmental context, indexed by variables such as age, gender, and body size, nearly always plays a role in determining the influence of genetic variation on measures of health that have a complex multifactorial etiology. As an alternative to population-based single model risk stratification schemes [Conroy et al., 2003;Anderson et al., 1990] and population-based marginal genetic effects [Smith et al., 2006], the PRIM makes possible a more personalized risk prediction strategy that incorporates both rare and common environmental and genetic risk factors, an objective that has been the goal of medical genetics in particular, and clinical practice in general. "
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    ABSTRACT: Different combinations of genetic and environmental risk factors are known to contribute to the complex etiology of ischemic heart disease (IHD) in different subsets of individuals. We employed the Patient Rule-Induction Method (PRIM) to select the combination of risk factors and risk factor values that identified each of 16 mutually exclusive partitions of individuals having significantly different levels of risk of IHD. PRIM balances two competing objectives: (1) finding partitions where the risk of IHD is high and (2) maximizing the number of IHD cases explained by the partitions. A sequential PRIM analysis was applied to data on the incidence of IHD collected over 8 years for a sample of 5,455 unrelated individuals from the Copenhagen City Heart Study (CCHS) to assess the added value of variation in two candidate susceptibility genes beyond the traditional, lipid and body mass index risk factors for IHD. An independent sample of 362 unrelated individuals also from the city of Copenhagen was used to test the model obtained for each of the hypothesized partitions.
    Genetic Epidemiology 09/2007; 31(6):515-27. DOI:10.1002/gepi.20225 · 2.60 Impact Factor
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