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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Published on: June 26, 2013

Association tests based on the principal-component analysis.

Sohee Oh¹, Taesung Park

¹Department of Statistics, Seoul National University, 56-1 Shillim-Dong, Kownak-Gu, Seoul 151-747, South Korea. oh.sohee@gmail.com

BMC Proceedings

|May 10, 2008

Summary

This summary is machine-generated.

The principal-component (PC) association test offers a powerful alternative to traditional haplotype-based association studies. This novel method addresses haplotype sparseness, showing improved performance in genetic association analyses.

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Area of Science:

Genetics
Statistical genetics
Bioinformatics

Background:

Haplotypes, combinations of alleles on a chromosome, capture linkage disequilibrium (LD).
Haplotype-based association studies offer greater power than single-marker analyses.
Constructing haplotypes with many markers can lead to a sparseness problem.

Purpose of the Study:

To propose the principal-component (PC) association test as an alternative to haplotype-based association tests.
To evaluate the performance of the PC test against traditional haplotype analysis.

Main Methods:

Define PC scores from linkage disequilibrium (LD) blocks.
Perform association tests using logistic regression.
Apply the PC test to the Genetic Analysis Workshop 15 simulated data set.

Main Results:

The PC test demonstrated a tendency for smaller Akaike Information Criterion (AIC) values compared to haplotype-based tests.
The PC test exhibited slightly greater statistical power than the haplotype-based association test.
Evaluations included AIC, power, and type I error rates.

Conclusions:

The PC association test is a viable and potentially superior alternative for genetic association studies.
This method effectively mitigates the sparseness problem inherent in complex haplotype construction.
The PC test provides a valuable tool for analyzing genetic data with high-density markers.