The impact of genetics and genomics on public health. Eur J Hum Genet

German Center for Public Health Genomics, University of Applied Sciences, Bielefeld, Germany.
European Journal of HumanGenetics (Impact Factor: 4.35). 10/2007; 16(1):5-13. DOI: 10.1038/sj.ejhg.5201942


Public health practice has to date concerned itself with environmental or social determinants of health and disease and has paid scant attention to genomic variations within the population. The advances brought about by genomics are changing these perceptions. In the long run, this knowledge will enable health promotion messages and disease prevention programmes to be specifically directed at susceptible individuals and families, or at subgroups of the population, based on their genomic risk profile. As the controversial discourse in science and health politics shows, the integration of genomics into public health research, policy and practice is one of the major challenges that our health-care system is currently facing.Keywords: public health genomics, genetics, genomics and population health, prevention, health policy, inequalities in health and social exclusion, public health ethics

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    • "Inclusion of these SNPs and combinations in these analyses may strengthen the predictive abilities of new prediction models. Taken together, this will most likely lead to optimizing personalized public health programs and identification of high risk groups [37]. Complementary, health protection, fueled by genetic risk profiles will be a highly effective and efficient public health task [38]. "
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    ABSTRACT: Sensorineural hearing loss is the most common sequela in survivors of bacterial meningitis (BM). In the past we developed a validated prediction model to identify children at risk for post-meningitis hearing loss. It is known that host genetic variations, besides clinical factors, contribute to severity and outcome of BM. In this study it was determined whether host genetic risk factors improve the predictive abilities of an existing model regarding hearing loss after childhood BM. Four hundred and seventy-one Dutch Caucasian childhood BM were genotyped for 11 single nucleotide polymorphisms (SNPs) in seven different genes involved in pathogen recognition. Genetic data were added to the original clinical prediction model and performance of new models was compared to the original model by likelihood ratio tests and the area under the curve (AUC) of the receiver operating characteristic curves. Addition of TLR9-1237 SNPs and the combination of TLR2 + 2477 and TLR4 + 896 SNPs improved the clinical prediction model, but not significantly (increase of AUC's from 0.856 to 0.861 and from 0.856 to 0.875 (p = 0.570 and 0.335, respectively). Other SNPs analysed were not linked to hearing loss. Although addition of genetic risk factors did not significantly improve the clinical prediction model for post-meningitis hearing loss, AUC's of the pre-existing model remain high after addition of genetic factors. Future studies should evaluate whether more combinations of SNPs in larger cohorts has an additional value to the existing prediction model for post meningitis hearing loss.
    BMC Infectious Diseases 07/2013; 13(1):340. DOI:10.1186/1471-2334-13-340 · 2.61 Impact Factor
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    • "As genetics moves into the direction of genomics, and as a genetic test moves into the direction of genome-based health information, it becomes obvious that the ACCE framework is of limited use for evaluation. Instead, the Health Technology Assessment (HTA) approach is already used and has been established as an evaluation tool within the European Member States in the last ten years [5]. This means that the end result of such research is a systematic review and synthesis of pieces of evidence that will support the development of evidence-based policy and practice guidelines [8,14]. "
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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 concept of personalized medicine assumes that prediction of phenotypes based on genome information can enable better prognosis, prevention and medical care which can be tailored individually (Brand et al. 2008; Janssens and van Duijn 2008). However, practical application of genome-based information to medicine requires the disease risk to be predicted with high accuracy, while knowledge on genetics of common complex diseases is still insufficient to allow their accurate prediction solely from DNA data (Alaerts and Del-Favero 2009; Chung et al. 2010; Ku et al. 2010; McCarthy and Zeggini 2009). "
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    ABSTRACT: Predicting complex human phenotypes from genotypes is the central concept of widely advocated personalized medicine, but so far has rarely led to high accuracies limiting practical applications. One notable exception, although less relevant for medical but important for forensic purposes, is human eye color, for which it has been recently demonstrated that highly accurate prediction is feasible from a small number of DNA variants. Here, we demonstrate that human hair color is predictable from DNA variants with similarly high accuracies. We analyzed in Polish Europeans with single-observer hair color grading 45 single nucleotide polymorphisms (SNPs) from 12 genes previously associated with human hair color variation. We found that a model based on a subset of 13 single or compound genetic markers from 11 genes predicted red hair color with over 0.9, black hair color with almost 0.9, as well as blond, and brown hair color with over 0.8 prevalence-adjusted accuracy expressed by the area under the receiver characteristic operating curves (AUC). The identified genetic predictors also differentiate reasonably well between similar hair colors, such as between red and blond-red, as well as between blond and dark-blond, highlighting the value of the identified DNA variants for accurate hair color prediction. Electronic supplementary material The online version of this article (doi:10.1007/s00439-010-0939-8) contains supplementary material, which is available to authorized users.
    Human Genetics 04/2011; 129(4):443-54. DOI:10.1007/s00439-010-0939-8 · 4.82 Impact Factor
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