Multiple testing in the context of haplotype analysis revisited: application to case-control data

Area of Science:

• Genetics
• Statistical genetics
• Bioinformatics

Background:

• Haplotype-based association analysis often involves extensive marker combinations, leading to multiple testing problems.
• Existing methods require simulations for P-value calculation, complicating analysis.
• Case-control data is prevalent in genetic association studies.

Purpose of the Study:

• To implement and evaluate a novel multiple testing correction procedure for haplotype association analysis in case-control family data.
• To compare the performance of different statistical tests and strategies for handling phase ambiguity.
• To enhance statistical power in detecting genetic associations while accounting for complex marker interactions.

Main Methods:

• Implementation of a global P-value approach to correct for multiple testing in haplotype association analysis.
• Inclusion of two statistical tests: chi-square based and haplotype trend regression.
• Evaluation of different methods for handling phase ambiguities, including weighted haplotype explanations.
• A large-scale simulation study to assess the performance of the implemented methods.

Main Results:

• The new global P-value approach significantly increases power compared to Bonferroni correction across tested statistics.
• Both haplotype trend regression and chi-square based tests demonstrate good power.
• Assigning weighted haplotype explanations is superior to using the most likely explanation for handling phase ambiguities.
• The approach was validated using a real-world data example.

Conclusions:

• The developed method provides a powerful and efficient way to perform haplotype association studies on case-control family data.
• The global P-value correction effectively addresses multiple testing issues without compromising statistical power.
• Handling phase ambiguities using weighted explanations improves analytical accuracy.