Evaluating Genetic Testing for Disease Risk
Author Information
Author(s): Jakobsdottir Johanna, Gorin Michael B., Conley Yvette P., Ferrell Robert E., Weeks Daniel E.
Primary Institution: University of Pittsburgh
Hypothesis
How useful are highly associated SNPs for individual-level risk estimation and prediction?
Conclusion
Strong genetic associations do not guarantee effective discrimination between cases and controls in personalized medicine.
Supporting Evidence
- Strong associations do not guarantee effective discrimination between cases and controls.
- The AUC for the three-factor model of AMD was 0.79, but only 30% of those classified as high risk were actual cases.
- Logistic regression analysis showed odds ratios of around 3 for significant SNPs.
- Genetic testing results are often poorly understood by individuals and physicians.
Takeaway
Scientists are trying to figure out if genetic tests can really help us know if we will get sick, but just because a gene is linked to a disease doesn't mean it can accurately tell who will get it.
Methodology
The study used logistic regression and ROC curve analysis on genetic data related to age-related macular degeneration and other diseases.
Potential Biases
Potential overinterpretation of genetic findings in personalized medicine.
Limitations
The study highlights that strong associations do not ensure good classification ability and that the effectiveness of genetic markers needs to be formally established.
Participant Demographics
The study discusses various age groups, particularly focusing on those 40 years and older for AMD.
Statistical Information
P-Value
10−13
Confidence Interval
0.74–0.83
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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