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A comparison of methods sensitive to interactions with small main effects.

Robert C Culverhouse¹

¹Department of Internal Medicine, Washington University in St. Louis School of Medicine, St. Louis, Missouri 63110, USA. rculverh@wustl.edu

Genetic Epidemiology

|March 31, 2012

Summary

The Restricted Partition Method (RPM) effectively identifies complex genetic interactions for diseases, outperforming Multifactor Dimensionality Reduction (MDR) and Support Vector Machines (SVMs) in simulated data analysis.

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Area of Science:

Genetics and Genomics
Statistical Genetics
Computational Biology

Background:

Genetic variants explain only a small part of complex trait heritability, indicating a need to explore gene-gene and gene-environment interactions.
Existing methods for analyzing joint genetic effects are numerous but lack systematic comparison.
Simulated data is crucial for evaluating the performance of different statistical approaches in genetic analysis.

Purpose of the Study:

To systematically compare the performance of three methods—Multifactor Dimensionality Reduction (MDR), Support Vector Machines (SVMs), and Restricted Partition Method (RPM)—in detecting joint genetic effects.
To evaluate these methods using simulated genetic data that incorporates various complexities like noise, missing data, and genetic heterogeneity.

Main Methods:

Development of 96 two-locus genetic models with varying minor allele frequencies and risk levels for a dichotomous phenotype.
Introduction of 'noise' factors including missing data, genotyping error, genetic heterogeneity, and phenocopies into the simulated datasets.
Comparative analysis of MDR, SVMs, and RPM performance on 100 data replicates for each simulated model.

Main Results:

The Restricted Partition Method (RPM) consistently outperformed both MDR and SVMs across all six classes of genetic models.
MDR showed better performance than SVMs when the underlying genetic model had few, distinct risk classes.
SVMs outperformed MDR on more complex genetic models, indicating varying strengths and weaknesses among the tested methods.

Conclusions:

The RPM is a highly effective method for detecting joint genetic effects in complex traits, demonstrating superior performance in simulated scenarios.
The choice of method (MDR or SVM) depends on the complexity of the genetic architecture, with RPM offering a robust solution.
While MDR offers a user-friendly interface, RPM's superior performance highlights its potential for advancing genetic association studies.