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Related Experiment Video

Updated: Jun 13, 2026

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

Stakeholder perspectives on a risk-benefit framework for genetic testing.

Joshua A Roth¹, Louis P Garrison, Wylie Burke

¹Department of Pharmacy, University of Washington, Seattle, WA 98195, USA.

Public Health Genomics

|April 22, 2010

Summary

Developing a quantitative risk-benefit framework is crucial for integrating genetic tests into healthcare. Stakeholders support this approach for evaluating clinical utility and guiding research, emphasizing transparency and iterative processes.

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

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Published on: June 21, 2018

Area of Science:

Genomic Medicine
Health Services Research
Decision Analysis

Background:

Integrating genomic applications into healthcare requires timely assessment of clinical benefits versus risks.
Current limitations include lack of direct evidence, quantitative frameworks, and methods to assess uncertainty.
Genetic tests need robust evaluation to ensure appropriate clinical integration.

Purpose of the Study:

To propose and explore a quantitative risk-benefit framework for evaluating genetic tests.
To discuss the application of such a framework using warfarin pharmacogenomics as a case study.
To identify stakeholder perspectives on genetic test risk-benefit analysis.

Main Methods:

A qualitative analysis of responses from a stakeholder meeting held in December 2008.
Focused discussions on using risk-benefit analysis for decision-making, test categorization, and research prioritization.
Case study approach using warfarin pharmacogenomics.

Main Results:

Stakeholders expressed support for risk-benefit modeling to structure discussions on genetic test clinical utility.
A consensus emerged for an iterative, transparent, and parsimonious modeling process.
Concerns were raised regarding the use of quality-adjusted life-years in the evaluation.

Conclusions:

Risk-benefit analysis holds potential utility for evaluating genetic tests.
Further research and stakeholder consensus-building are needed in key areas.
A structured framework can aid in communicating benefits, harms, and uncertainty of genetic tests.