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Improved statistics for genome-wide interaction analysis.

Masao Ueki¹, Heather J Cordell

¹Faculty of Medicine, Yamagata University, Yamagata, Japan.

|April 13, 2012

Summary

Researchers evaluated novel genome-wide interaction statistics, finding errors in original formulas that inflate type 1 error rates. Adjusted and new joint effects statistics offer improved accuracy and power for genetic association studies.

Area of Science:

Genetics
Statistical Genetics
Bioinformatics

Background:

Novel statistics for genome-wide interaction analysis were recently proposed by Wu et al. using case/control or case-only data.
These statistics showed superior performance in simulations compared to existing methods like PLINK's fast-epistasis and logistic regression.
The theoretical underpinnings and reasons for the superior performance of Wu et al.'s statistics were not fully explored.

Purpose of the Study:

To investigate the theoretical properties and performance of Wu et al.'s proposed genome-wide interaction statistics.
To identify and correct errors in the proposed statistics and existing methods (PLINK).
To develop and evaluate new statistics for robust interaction analysis, particularly focusing on genuine interaction effects.

Main Methods:

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Theoretical analysis of statistical formulae for interaction analysis.
Computer simulations to evaluate type 1 error rates and statistical power.
Comparison of proposed adjusted and new statistics against original Wu et al. and PLINK statistics.

Main Results:

Minor errors were found in Wu et al.'s statistics, leading to inflated type 1 error rates.
Minor errors were also identified in PLINK's fast-epistasis and case-only statistics, with negligible impact on type 1 error.
Adjusted versions of Wu et al.'s statistics and new 'joint effects' statistics demonstrated correct type 1 error control and high power across various scenarios.

Conclusions:

The originally proposed Wu et al. statistics should be used with caution due to inflated error rates.
The newly proposed adjusted Wu and joint effects statistics provide reliable and powerful alternatives for genome-wide interaction analysis.
Some previously proposed statistics can be sensitive to main effects, especially with linkage disequilibrium, unlike the proposed joint effects statistics.