Background

The prevalence of obesity and overweight is highest among ethnic minority groups in Western countries. The objective of this study is to examine the contribution of ethnicity and beliefs of parents about overweight preventive behaviours to their child’s outdoor play and snack intake, and to the parents’ intention to monitor these behaviours.

Methods

A cross-sectional survey was conducted among parents of native Dutch children and children from a large minority population (Turks) at primary schools, sampled from Youth Health Care registers.

Results

Native Dutch parents observed more outdoor play and lower snack intake in their child and had stronger intentions to monitor these behaviours than parents of Turkish descent. In the multivariate analyses, the parents’ attitude and social norm were the main contributing factors to the parental intention to monitor the child’s outdoor play and snack intake. Parental perceived behavioural control contributed to the child’s outdoor play and, in parents who perceived their child to be overweight, to snacking behaviour. The associations between parents’ behavioural cognitions and overweight related preventive behaviours were not modified by ethnicity, except for perceived social norm. The relationship between social norm and intention to monitor outdoor play was stronger in Dutch parents than in Turkish parents.

Conclusions

As the overweight related preventive behaviours of both children and parents did differ between the native and ethnic minority populations of this study, it is advised that interventions pay attention to cultural aspects of the targeted population. Further research is recommended into parental behavioural cognitions regarding overweight prevention and management for different ethnicities.

Genetic prediction of common diseases is based on testing multiple genetic variants with weak effect sizes. Standard logistic regression and Cox Proportional Hazard models that assess the combined effect of multiple variants on disease risk assume multiplicative joint effects of the variants, but this assumption may not be correct. The risk model chosen may affect the predictive accuracy of genomic profiling. We investigated the discriminative accuracy of genomic profiling by comparing additive and multiplicative risk models. We examined genomic profiles of 40 variants with genotype frequencies varying from 0.1 to 0.4 and relative risks varying from 1.1 to 1.5 in separate scenarios assuming a disease risk of 10%. The discriminative accuracy was evaluated by the area under the receiver operating characteristic curve. Predicted risks were more extreme at the lower and higher risks for the multiplicative risk model compared with the additive model. The discriminative accuracy was consistently higher for multiplicative risk models than for additive risk models. The differences in discriminative accuracy were negligible when the effect sizes were small (<1.2), but were substantial when risk genotypes were common or when they had stronger effects. Unraveling the exact mode of biological interaction is important when effect sizes of genetic variants are moderate at the least, to prevent the incorrect estimation of risks.

The rapid and continuing progress in gene discovery for complex diseases is fueling interest in the potential application of genetic risk models for clinical and public health practice. The number of studies assessing the predictive ability is steadily increasing, but the quality and completeness of reporting varies. A multidisciplinary workshop sponsored by the Human Genome Epidemiology Network developed a checklist of 25 items recommended for strengthening the reporting of Genetic RIsk Prediction Studies, building on the principles established by previous reporting guidelines. These recommendations aim to enhance the transparency of study reporting, and thereby to improve the synthesis and application of information from multiple studies that might differ in design, conduct, or analysis. A detailed Explanation and Elaboration document is published on the EJHG website.

Migraine is a common neurological disorder with a genetically complex background. This paper describes a meta-analysis of genome-wide association (GWA) studies on migraine, performed by the Dutch–Icelandic migraine genetics (DICE) consortium, which brings together six population-based European migraine cohorts with a total sample size of 10 980 individuals (2446 cases and 8534 controls). A total of 32 SNPs showed marginal evidence for association at a P-value<10−5. The best result was obtained for SNP rs9908234, which had a P-value of 8.00 × 10−8. This top SNP is located in the nerve growth factor receptor (NGFR) gene. However, this SNP did not replicate in three cohorts from the Netherlands and Australia. Of the other 31 SNPs, 18 SNPs were tested in two replication cohorts, but none replicated. In addition, we explored previously identified candidate genes in the meta-analysis data set. This revealed a modest gene-based significant association between migraine and the metadherin (MTDH) gene, previously identified in the first clinic-based GWA study (GWAS) for migraine (Bonferroni-corrected gene-based P-value=0.026). This finding is consistent with the involvement of the glutamate pathway in migraine. Additional research is necessary to further confirm the involvement of glutamate.

Background

Genetic risk models could potentially be useful in identifying high-risk groups for the prevention of complex diseases. We investigated the performance of this risk stratification strategy by examining epidemiological parameters that impact the predictive ability of risk models.

Methods

We assessed sensitivity, specificity, and positive and negative predictive value for all possible risk thresholds that can define high-risk groups and investigated how these measures depend on the frequency of disease in the population, the frequency of the high-risk group, and the discriminative accuracy of the risk model, as assessed by the area under the receiver-operating characteristic curve (AUC). In a simulation study, we modeled genetic risk scores of 50 genes with equal odds ratios and genotype frequencies, and varied the odds ratios and the disease frequency across scenarios. We also performed a simulation of age-related macular degeneration risk prediction based on published odds ratios and frequencies for six genetic risk variants.

Results

We show that when the frequency of the high-risk group was lower than the disease frequency, positive predictive value increased with the AUC but sensitivity remained low. When the frequency of the high-risk group was higher than the disease frequency, sensitivity was high but positive predictive value remained low. When both frequencies were equal, both positive predictive value and sensitivity increased with increasing AUC, but higher AUC was needed to maximize both measures.

Conclusions

The performance of risk stratification is strongly determined by the frequency of the high-risk group relative to the frequency of disease in the population. The identification of high-risk groups with appreciable combinations of sensitivity and positive predictive value requires higher AUC.

Background

As genetics technology proceeds, practices of genetic testing have become more heterogeneous: many different types of tests are finding their way to the public in different settings and for a variety of purposes. This diversification is relevant to the discourse on ethical, legal and societal issues (ELSI) surrounding genetic testing, which must evolve to encompass these differences. One important development is the rise of personal genome testing on the basis of genetic profiling: the testing of multiple genetic variants simultaneously for the prediction of common multifactorial diseases. Currently, an increasing number of companies are offering personal genome tests directly to consumers and are spurring ELSI-discussions, which stand in need of clarification. This paper presents a systematic approach to the ELSI-evaluation of personal genome testing for multifactorial diseases along the lines of its test characteristics.

Discussion

This paper addresses four test characteristics of personal genome testing: its being a non-targeted type of testing, its high analytical validity, low clinical validity and problematic clinical utility. These characteristics raise their own specific ELSI, for example: non-targeted genetic profiling poses serious problems for information provision and informed consent. Questions about the quantity and quality of the necessary information, as well as about moral responsibilities with regard to the provision of information are therefore becoming central themes within ELSI-discussions of personal genome testing. Further, the current low level of clinical validity of genetic profiles raises questions concerning societal risks and regulatory requirements, whereas simultaneously it causes traditional ELSI-issues of clinical genetics, such as psychological and health risks, discrimination, and stigmatization, to lose part of their relevance. Also, classic notions of clinical utility are challenged by the newer notion of 'personal utility.'

Summary

Consideration of test characteristics is essential to any valuable discourse on the ELSI of personal genome testing for multifactorial diseases. Four key characteristics of the test - targeted/non-targeted testing, analytical validity, clinical validity and clinical utility - together determine the applicability and the relevance of ELSI to specific tests. The paper identifies and discusses four areas of interest for the ELSI-debate on personal genome testing: informational problems, risks, regulatory issues, and the notion of personal utility.

The rapid and continuing progress in gene discovery for complex diseases is fueling interest in the potential application of genetic risk models for clinical and public health practice. The number of studies assessing the predictive ability is steadily increasing, but they vary widely in completeness of reporting and apparent quality. Transparent reporting of the strengths and weaknesses of these studies is important to facilitate the accumulation of evidence on genetic risk prediction. A multidisciplinary workshop sponsored by the Human Genome Epidemiology Network developed a checklist of 25 items recommended for strengthening the reporting of Genetic RIsk Prediction Studies (GRIPS), building on the principles established by previous reporting guidelines. These recommendations aim to enhance the transparency, quality and completeness of study reporting, and thereby to improve the synthesis and application of information from multiple studies that might differ in design, conduct or analysis.

The rapid and continuing progress in gene discovery for complex diseases is fueling interest in the potential application of genetic risk models for clinical and public health practice. The number of studies assessing the predictive ability is steadily increasing, but the quality and completeness of reporting varies. A multidisciplinary workshop sponsored by the Human Genome Epidemiology Network developed a checklist of 25 items recommended for strengthening the reporting of genetic risk prediction studies (the GRIPS statement), building on the principles established by prior reporting guidelines. These recommendations aim to enhance the transparency of study reporting, and thereby to improve the synthesis and application of information from multiple studies that might differ in design, conduct, or analysis. A detailed Explanation and Elaboration document is published at http://www.plosmedicine.org.

Family-based research in genetically isolated populations is an effective approach for identifying loci influencing variation in disease traits. In common with all studies in humans, those in genetically isolated populations need ethical approval; however, existing ethical frameworks may be inadequate to protect participant privacy and confidentiality and to address participants' information needs in such populations. Using the ethical–legal guidelines of the Council for International Organizations of Medical Sciences (CIOMS) as a template, we compared the participant information leaflets and consent forms of studies in five European genetically isolated populations to identify additional information that should be incorporated into information leaflets and consent forms to guarantee satisfactorily informed consent. We highlight the additional information that participants require on the research purpose and the reasons why their population was chosen; on the potential risks and benefits of participation; on the opportunities for benefit sharing; on privacy; on the withdrawal of consent and on the disclosure of genetic data. This research raises some important issues that should be addressed properly and identifies relevant types of information that should be incorporated into information leaflets for this type of study.

Personal genome testing is offered via the internet directly to consumers. Most tests that are currently offered use data from genome-wide scans to predict risks for multiple common diseases and traits. The utility of these tests is limited, predominantly because they lack predictive ability and clear benefits for disease prevention that are specific for genetic risk groups. In the near future, personal genome tests will likely be based on whole genome sequencing, but will these technological advances increase the utility of personal genome testing? Whole genome sequencing theoretically provides information about the risks of both monogenic and complex diseases, but the practical utility remains to be demonstrated. The utility of testing depends on the predictive ability of the test, the likelihood of actionable test results, and the options available for the reduction of risks. For monogenic diseases, the likelihood of known mutations will be extremely low in the general population and it will be a challenge to recognize new causal variants among all rare variants that are found using sequencing. For complex diseases, the predictive ability of genetic tests will be mainly restricted by the heritability of the disease, but also by the genetic complexity of the disease etiology, which determines the extent to which the heritability can be understood. Given that numerous genetic and non-genetic risk factors interact in the causation of complex diseases, the predictive ability of genetic models will likely remain modest. Personal genome testing will have minimal benefits for individual consumers unless major breakthroughs are made in the near future.

Background

A family history, reflecting genetic susceptibility as well as shared environmental and behavioral factors, is an important risk factor for common chronic multifactorial diseases such as cardiovascular diseases, type 2 diabetes and many cancers.

Discussion

The purpose of the present paper is to discuss the evidence for the use of family history as a tool for primary prevention of common chronic diseases, in particular for tailored interventions aimed at promoting healthy lifestyles. The following questions are addressed: (1) What is the value of family history information as a determinant of personal disease risk?; (2)How can family history information be used to motivate at-risk individuals to adopt and maintain healthy lifestyles in order to prevent disease?; and (3) What additional studies are needed to assess the potential value of family history information as a tool to promote a healthy lifestyle?

Summary

In addition to risk assessment, family history information can be used to personalize health messages, which are potentially more effective in promoting healthy lifestyles than standardized health messages. More research is needed on the evidence for the effectiveness of such a tool.

The translation of emerging genomic knowledge into public health and clinical care is one of the major challenges for the coming decades. At the moment, genome-based prediction of common diseases, such as type 2 diabetes, coronary heart disease and cancer, is still not informative. Our understanding of the genetic basis of multifactorial diseases is improving, but the currently identified susceptibility variants contribute only marginally to the development of disease. At the same time, an increasing number of companies are offering personalized lifestyle and health recommendations on the basis of individual genetic profiles. This discrepancy between the limited predictive value and the commercial availability of genetic profiles highlights the need for a critical appraisal of the usefulness of genome-based applications in clinical and public health care. Anticipating the discovery of a large number of genetic variants in the near future, we need to prepare a framework for the design and analysis of studies aiming to evaluate the clinical validity and utility of genetic tests. In this article, we review recent studies on the predictive value of genetic profiling from a methodological perspective and address issues around the choice of the study population, the construction of genetic profiles, the measurement of the predictive value, calibration and validation of prediction models, and assessment of clinical utility. Careful consideration of these issues will contribute to the knowledge base that is needed to identify useful genome-based applications for implementation in clinical and public health practice.

Numerous genetic loci influence systolic blood pressure (SBP) and diastolic blood pressure (DBP) in Europeans 1-3. We now report genome-wide association studies of pulse pressure (PP) and mean arterial pressure (MAP). In discovery (N=74,064) and follow-up studies (N=48,607), we identified at genome-wide significance (P= 2.7×10-8 to P=2.3×10-13) four novel PP loci (at 4q12 near CHIC2/PDGFRAI, 7q22.3 near PIK3CG, 8q24.12 in NOV, 11q24.3 near ADAMTS-8), two novel MAP loci (3p21.31 in MAP4, 10q25.3 near ADRB1) and one locus associated with both traits (2q24.3 near FIGN) which has recently been associated with SBP in east Asians. For three of the novel PP signals, the estimated effect for SBP was opposite to that for DBP, in contrast to the majority of common SBP- and DBP-associated variants which show concordant effects on both traits. These findings indicate novel genetic mechanisms underlying blood pressure variation, including pathways that may differentially influence SBP and DBP.