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How to Use an Article About Genetic Association:  C: What Are the Results and Will They Help Me in Caring for My Patients?

John Attia, MD, PhD; John P. A. Ioannidis, MD, PhD; Ammarin Thakkinstian, PhD; Mark McEvoy, MMedSc; Rodney J. Scott, PhD; Cosetta Minelli, PhD; John Thompson, PhD; Claire Infante-Rivard, MD, PhD; Gordon Guyatt, MD, MSc
JAMA. 2009;301(3):304-308. doi:10.1001/jama.2008.993.
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In the first 2 articles of this series, we reviewed the basic genetics concepts necessary to understand genetic association studies, and we enumerated the major issues in judging the validity of these studies. In this third article, we review the issues relating to the applicability of the results in the clinical situation. How large and precise are the associations? Many genetic effects are expected to be smaller in magnitude than traditional risk factors. Does the genetic association improve predictive power beyond easily measured clinical variables? In some cases, the additional genetic information adds only a small increment in the predictive ability of a diagnostic or prognostic test. What are the absolute vs relative effects? Even if the genetic risk is high in relative terms, the baseline risk may be very low in absolute terms. Is the risk-associated allele likely to be present in my patient? A risk allele may have a strong effect but be rare in a particular ethnic group. Is the patient likely better off knowing the genetic information? Given that genes cannot be modified, one must weigh whether the genetic information is likely to be helpful in planning other health interventions or initiating behavior change.

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Figure. Example of a Receiver Operating Characteristic (ROC) Curve for Cardiovascular Risk Related to APOE
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A, Example of an ROC curve for a test that performs no better than chance. B, Example of an ROC curve for a test with perfect predictive ability (sensitivity = 100%; specificity = 100%). C, ROC curves for cardiovascular disease calculated using PROCAM (Prospective Cardiovascular Munster study) risk score plus APOE genotype. Based on 2451 men (of 3012 eligible) who had complete data for PROCAM and APOE genotyping. APOE genotype was fitted as a class variable with 3 categories 33, 22/23, and 34/44. Factors included age, body mass index, total cholesterol, triglycerides, systolic blood pressure, and family history. Other factors in PROCAM were not measured in all men. For the PROCAM score, the ROC value (95% confidence interval) was 0.65 (0.61-0.70), with a detection rate of 11.7% for a false-positive rate of 5.0%. In univariate analysis, APOE genotype was significant at = .01. In multivariate analysis, the area under the curve increased to 0.67 (0.63-0.71) (detection rate,14.0%), but this improvement was not significant (= .11). Panel C data based on Humphries et al.12

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