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Human Genome Epidemiology: A Scientific Foundation for Using Genetic Information to Improve Health and Prevent Disease

 

 

Part II:
Methods and Approaches 1: Assessing Disease Associations and Interactions

 

Chapter 11 Tables


Epidemiologic Approach to Genetic Tests: Population-Based Data for Preventive Medicine

Marta Gwinn, Muin J. Khoury


Table 11-1
Analytic validity, clinical validity, and clinical utility of a genetic test

Characteristic
How is it defined?
How is it determined?
How is it used?
Analytic validity
sensitivity, specificity, and predictive value in relation to genotype
laboratory analyses comparing test result with gold standard
validating test before clinical or research use
Clinical validity
sensitivity, specificity, and predictive value in relation to genotype
population-based, epidemiologic studies (cohort, case-control)
predicting risk, screening, making diagnosis
Clinical utility
benefits and risks accruing from both positive and negative tests
clinical trials; synthesis of observational data
assessing added value of testing in preventing disease outcomes


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Table 11-2
Analytic validity, clinical validity, and clinical utility of a genetic test
Average
Moderate
High
Risk Factor
No 1°relativea
Any 1°relativea
HNPCC
FAPb
Prevalence
9/10
1/10
1/3,000c
1/8,000
Absolute Risk
0.04
0.055
0.80d
~1
Relative Risk
1.7
~20
~30
Attributable fraction
0.07
unk
~0.004

a Fuchs CS, Giovannucci EL, Colditz GA, et al. A prospective study of family history and the risk of colorectal cancer. New Engl J Med 1994;331:1669-1674.

b Bodmer W. Familial adenomatous polyposis (FAP) and its gene, APC. Cytogenet Cell Genet 1999;86:99-104.

c Dunlop MG, Farrington SM, Nicholl I, et al. Population carrier frequency of hMSH2 and hMLH1 mutations. Brit J Cancer 2000;83:1643-1645.

d Lynch HT, Lynch JF. Hereditary nonpolyposis colorectal cancer. Semin Surg Oncol 2000;18:305-313.

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Appendix 11
Joint probability distribution of susceptibility genotype and
disease outcome in a hypothetical population.

Disease
Yes
No
Total
Genotype Present
P(GD)
P(icon)
P(G)
  Absent
p(icon)
P(icon)
P(icon)
  Total
P(D)
P(icon)
1

probability of genotype = P(G)
probability of disease = P(D)
population odds of disease = P(D) / P(icon)
joint probability of G and D = P(GD)
sensitivity = P(GD) / P(D) = P(G|D)
specificity = P(icon|icon), 1 – specificity = P(G|icon)
positive predictive value (ppv) of genotype = P(D|G)
negative predictive value (npv) of genotype = P(icon|icon)

The improved ability to predict disease using genotype can be summarized:

P(GD)/P(icon) = [P(G|D) / P(G|icon)] [P(D)/P(icon)]

or,odds of disease in presence of genotype = [sensitivity/(1 – specificity)](population disease odds)

The factor by which disease prediction can be improved in persons with the genotype,
relative to the whole population, is [sensitivity/(1 – specificity)], known as the likelihood
ratio.a
Likewise, the relative risk of disease in persons with the genotype can be expressed:
[P(GD)/P(G)]/[P(iconD)/P(icon)] = P(D|G) / P(D|icon) = ppv / (1-npv)

aReference 31.

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