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Modeling interactions with known risk loci-a Bayesian model averaging approach.

Teresa Ferreira¹, Jonathan Marchini

¹Department of Statistics, University of Oxford, UK.

Annals of Human Genetics

|December 2, 2010

Summary

Genome-wide association studies (GWAS) can now detect disease-related genetic loci, even when considering interactions between multiple loci. This new method improves the power of GWAS for complex diseases by modeling combined genetic effects.

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Area of Science:

Genetics
Biostatistics
Computational Biology

Background:

Genome-wide association studies (GWAS) are effective for identifying genetic loci associated with complex diseases.
Current GWAS primarily use single-locus scans, often overlooking gene-gene interactions.
Replicated loci are typically found through marginal effect analyses.

Purpose of the Study:

To develop and evaluate a novel statistical method for GWAS that incorporates interactions between known genetic loci.
To enhance the power of detecting associated loci, considering both marginal and interactive effects.
To provide an implementation of the proposed method within the SNPTEST software.

Main Methods:

Utilizing a Bayesian model averaging approach to combine evidence from various plausible interaction models.
Assessing SNP association by modeling the combined effect of a locus with other known loci.
Evaluating the method's performance in simulations for both marginal and interactive genetic effects.

Main Results:

The proposed method demonstrates good statistical power in detecting associated loci.
The method is effective whether the association arises from marginal effects or interactions with known loci.
The approach is integrated as an option in the SNPTEST program.

Conclusions:

The developed Bayesian model averaging method enhances GWAS by accounting for locus interactions.
This approach improves the detection of genetic associations for complex diseases.
The SNPTEST implementation facilitates the application of this advanced GWAS method.