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Genetic Case-Control Association Studies in Neuropsychiatry

Patrick F. Sullivan, MD, FRANZCP; Lindon J. Eaves, PhD, DSc; Kenneth S. Kendler, MD; Michael C. Neale, PhD
Arch Gen Psychiatry. 2001;58(11):1015-1024. doi:10.1001/archpsyc.58.11.1015.
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Case-control association studies use genetic markers as putative etiologic risk factors. The approach is controversial and has tended to produce associations in neuropsychiatry that do not stand the test of time. We studied the processes that can bias the outcomes away from a true representation of the relationship between a genetic marker and a neuropsychiatric disorder. If conducted with care and mindfulness of the potential pitfalls, case-control association studies can be an important tool for psychiatric genetic research.

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Figures

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Figure 1.

Outcomes of a case-control study.

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Figure 2.

Contour plots showing the power of a case-control association study (analytic calculations assuming α = .05, n = 100 per group, and Hardy-Weinberg equilibrium). A, Fully recessive model of gene action. B, Fully dominant model. The relative risk conferred by the "effective prevalence" of the increasing allele is on the x-axis and the increasing allele frequency, on the y-axis. Black indicates power of 0.80 or more; dark gray, power greater than or equal to 0.70 but less than 0.80; and light gray, power less than 0.70.

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Figure 3.

False-positive probability as a function of the number of independent statistical comparisons (or, in this instance, the number of unlinked genetic markers) for α = .05 (solid line; analytic calculations). The dashed line depicts the function for α = .01; the dotted line, for the Bonferroni correction.

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Figure 4.

Contour plot of the impact of different combinations of allele frequencies in 2 groups of different ancestry (analytic results). Assumptions are as follows: independence of the allele and trait; 200 cases and 200 controls; 2 ancestral groups randomly allocated to cases and controls; 80% of the sample from the first group and 20% from the second group; and trait prevalences of 17.9% and 11.9% in the first and second groups, respectively. The contours show P values due to stratification and are in 4 shades: the lightest indicates .5<P≤1.0; second lightest, .25<P≤.5; second darkest, .1<P≤.25; and darkest, P≤.1.

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Figure 5.

Diagram showing how the genotype of an affected individual (A1/A3) combined with parental genotypes (father A1/A2 and mother A3/A4) can be used to create a matched pseudosibling control (A2/A4) to eliminate the potential for a false-positive result due to population stratification.

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Figure 6.

Contour plots demonstrating the power of a family-based association study (assuming α = .05 and 100 triads composed of an affected offspring and both biological parents) for dominant and recessive models of gene action. Power was estimated via simulation with 1000 repetitions per parameter combination. Black indicates power of 0.80 or more; dark gray, power greater than or equal to 0.70 but less than 0.80; and light gray, power less than 0.70.

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Figure 7.

A highly simplified illustration of how a true-positive association might not be a causal path. A portion of the DNA sequence of the human dopamine transporter is depicted at the top of the diagram with 2 fictitious polymorphisms (markers 1 and 2). Intermarker distances are usually considerably greater than shown here. Mutation in marker 2 leads to altered protein expression or function and thence to an observable characteristic (trait B), which in turn causes trait A.

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