Computing power and sample size for case-control association studies with copy number polymorphism: application of...

Area of Science:

• Genetics
• Statistical Genetics
• Bioinformatics

Background:

• Copy number polymorphisms (CNPs) are increasingly recognized for their role in disease susceptibility.
• Current detection methods, primarily microarray technology, often categorize CNPs, potentially losing information.
• Case-control studies conventionally use chi-square tests on categorized CNP data.

Purpose of the Study:

• To develop and specify a likelihood ratio test statistic (LRTS) for case-control studies using continuous CNP measurements.
• To evaluate the power and relative efficiency of the LRTS against the chi-square test.
• To provide sample size and power formulas for both statistical methods.

Main Methods:

• Developed the LRTS for case-control designs utilizing underlying continuous quantitative measurements from microarrays.
• Employed Bayesian classification rules for categorizing CNPs in the chi-square test comparison.
• Derived sample size and power formulas for both LRTS and chi-square tests.

Main Results:

• The LRTS demonstrated superior power compared to the chi-square test, particularly for alternatives with low frequencies of at-risk CNP categories.
• An application comparing CNP distributions across ethnicities showed the LRTS to be more powerful.
• Potential misclassification of common CNP categories may explain the observed power difference.

Conclusions:

• The LRTS is a more powerful statistical approach for analyzing CNPs in case-control studies than the conventional chi-square test.
• Utilizing continuous CNP data with LRTS can improve the detection of disease associations, especially in scenarios with imbalanced category frequencies.
• The LRTS offers a valuable alternative for genetic association studies involving copy number variations.