A general method for controlling the genome-wide type I error rate in linkage and association mapping experiments in...

Area of Science:

• Genetics
• Statistical Genetics
• Bioinformatics

Background:

• Controlling the genome-wide type I error rate (GWER) is crucial for accurate genetic association and linkage mapping.
• Existing methods like permutation tests fail to account for population structure, leading to false positives in association mapping.
• Bonferroni correction is applicable but often overly conservative as it doesn't leverage test correlations.

Purpose of the Study:

• To propose a novel simulation-based approach for controlling the GWER in both linkage and association mapping.
• To develop a method that effectively accounts for population structure, a common confounder in genetic studies.
• To provide a more powerful alternative to existing error rate control methods.

Main Methods:

• A simulation procedure was developed to estimate and control the GWER.
• The method was designed to be applicable to both linkage and association mapping contexts.
• The approach was tested using parameter settings from three real-world datasets.

Main Results:

• The proposed simulation procedure successfully controlled the genome-wide type I error rate (GWER).
• It also demonstrated control over the generalized genome-wide type I error rate (GWER(k)).
• The method effectively addresses the issue of population structure in association mapping.

Conclusions:

• The new simulation-based method offers robust control of the GWER in genetic mapping studies.
• This approach mitigates false positive associations caused by population structure.
• The method provides a flexible and accurate tool for genetic researchers in both linkage and association studies.