Make it HuGE: human genome epidemiology reviews, population health, and the IJE

Sharing results from complex disease genetics studies: a community based participatory research approach

OBJECTIVES

Dissemination of research results to communities builds capacity of the community to understand and utilize the results. The objective of this manuscript was to propose a culturally appropriate approach to disseminate complex disease genetics research findings in small Alaska Native communities.

STUDY DESIGN

The Center for Alaska Native Health Research is a community-based participatory research project (CBPR) directed at understanding the interactions between genetic, nutritional and psychosocial risk factors for obesity, diabetes, and cardiovascular disease in Yup'ik Eskimos.

METHODS

We have consulted with regional healthcare providers, tribal leaders, and university-, local-, and national-institutional review boards to identify potential mechanisms for sharing population-based genetics research results or progress.

RESULTS

We propose a six step CBPR-approach to conducting genetics research in isolated identifiable communities. This CPBR-approach includes generating a common research question, determining community interest, recruitment, capacity building, sharing power and control, avoiding group harm, and development of culturally appropriate dissemination procedures.

CONCLUSIONS

Research scientists and community members should both benefit from population-based genetics research. Although we are just beginning our discussions with regard to sharing genetics research progress and findings, we believe that it is essential move forward as co-researchers in the CBPR enterprise.

Public health in the genomic era: will Public Health Genomics contribute to major changes in the prevention of common diseases?

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.

Proximal, distal, and the politics of causation: what's level got to do with it?

Causal thinking in public health, and especially in the growing literature on social determinants of health, routinely employs the terminology of proximal (or downstream) and distal (or upstream). I argue that the use of these terms is problematic and adversely affects public health research, practice, and causal accountability. At issue are distortions created by conflating measures of space, time, level, and causal strength. To make this case, I draw on an ecosocial perspective to show how public health got caught in the middle of the problematic proximal-distal divide--surprisingly embraced by both biomedical and social determinist frameworks--and propose replacing the terms proximal and distal with explicit language about levels, pathways, and power.

Methods for meta-analysis in genetic association studies: a review of their potential and pitfalls

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.

An application of the patient rule-induction method for evaluating the contribution of the Apolipoprotein E and Lipoprotein Lipase genes to predicting ischemic heart disease

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.

[Importance of genetics for health inequalities]

In Germany it has rarely been assessed in a systematic way, if and how genetic disposition and genetic testing are linked to health inequality. The paper aims to be a contribution towards closing this gap. In a first step, it is pointed out that the discussion about potential links between genetic causes of social inequalities has concentrated on issues such as body height and intelligence. It is stressed that, of course, social status is mainly determined socially and not genetically. In the second step, medical benefits of genetic testing are discussed. It can be assumed that low status groups are using these tests less often than high status groups, and that they are less capable of interpreting the results. Tests that can have a positive effect on health could thus lead to an increase of health inequalities. However, empirical studies for testing these hypotheses are hardly available. In the third step, the question is raised whether genetic information could lead to social discrimination (e.g. concerning health insurance, life insurance or employer). According to the current empirical literature, to date, this risk is (still) rather small. Thus, it is stressed that more research is needed, and that already today there is some need for intervention (e.g. concerning equal access to genetic testing, better information of low status groups).