Challenges of translating genetic tests into clinical and public health practice


Research in genetics and genomics has led to an expanding list of molecular genetic tests, which are increasingly entering health care systems. However, the evidence surrounding the benefits and harms of these tests is frequently weak. Here we present the main challenges to the successful translation of new research findings about genotype–phenotype associations into clinical practice. We discuss the means to achieve an accelerated translation research agenda that is conducted in a reasonable, fair and efficient manner.

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We acknowledge the comments from various colleagues, especially M.-A. Bailey, D. Callahan, L. Friedman Ross, J. John, R. Leidl and J. Schmidtke. All errors of omission and commission remain our own. W.R. was partly supported by EuroGentest, an EU-FP6 supported NoE contract number 512148 (EuroGentest Unit 3: Clinical genetics, community genetics and public health, Workpackage 3.2 (J. Schmidtke)). The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the US Centers for Disease Control and Prevention.

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Correspondence to Wolf H. Rogowski.

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Evaluation of Genomic Applications in Practice and Prevention (EGAPP)

National Institute of Health and Clinical Excellence (NICE)


Analytic validity

The ability of a genetic test to accurately and reliably measure the genotype (or analyte) of interest in the clinical laboratory, and in specimens that are representative of the population of interest.

Cascade screening

Active screening among the family members of known carriers of disease-relevant mutations.

Clinical validity

The ability of a genetic test to accurately and reliably predict the clinically defined disorder or phenotype of interest.

Clinical utility

The improved measurable clinical outcomes shown by a genetic test, and the usefulness and added value to patient management resulting from a test compared with current patient management without genetic testing.

Cost–benefit analysis

Estimation of the costs of an intervention and the value of outcomes, which can include both health and non-health outcomes, in monetary terms.

Cost-effectiveness analysis

Estimation of the net cost per unit of health outcome gained, such as cases of disease, number of deaths prevented or life years gained.

Cost–utility analysis

A variant of cost-effectiveness analysis in which a preference-based measure of health, such as the quality-adjusted life year, combines information on mortality and morbidity.

Distributive justice

Normative principles designed to guide the allocation of the benefits and burdens of economic activity.

Molecular genetic test

The application of genetics and genomics research results to the analysis of human DNA, with the aim of detecting disorders that have a genetic basis or specific reactions to medical treatments.

Quality-adjusted life years

A summary health measure that combines information on mortality and morbidity by assessing different dimensions of health with a generic instrument and then evaluating these different health states by weights elicited from representative surveys.

Value of information analysis

Assessment of the potential benefit of further information for a decision maker; value of information depends on the probability that the decision based on existing information is wrong and the consequences of a wrong decision, in terms of resources and health benefit forgone.

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Rogowski, W., Grosse, S. & Khoury, M. Challenges of translating genetic tests into clinical and public health practice. Nat Rev Genet 10, 489–495 (2009) doi:10.1038/nrg2606

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