Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data

Area of Science:

• Genetics
• Genomic Association Studies
• Bioinformatics

Background:

• Whole-genome association studies (WGS) using tagSNPs are effective for common variants but underpowered for rare variants.
• Common diseases can arise from functional variants across a spectrum of allele frequencies, including rare ones.
• Direct sequencing is crucial for identifying rare variants, with cost-effective technologies enabling broader application.

Purpose of the Study:

• To evaluate and develop robust statistical methods for analyzing rare variants in sequence data.
• To compare the performance of existing methods with novel approaches for genetic association studies.
• To introduce a unified method that leverages the strengths of both collapsing and multivariate analyses.

Main Methods:

• Comparison of individual variant analysis, collapsing methods, and multivariate analysis for rare variant detection.
• Development and application of the Combined Multivariate and Collapsing (CMC) method.
• Testing methods on candidate-gene and whole-genome sequence data.

Main Results:

• Collapsing methods are powerful for analyzing rare variants.
• Multivariate analysis demonstrates robustness against non-causal variants.
• Both collapsing and multivariate methods outperform individual variant analysis.
• The developed CMC method is shown to be both powerful and robust.

Conclusions:

• The CMC method effectively integrates the advantages of collapsing and multiple-marker tests.
• CMC provides a powerful and robust approach for rare variant association studies.
• The CMC method is applicable to both candidate-gene and whole-genome sequence data.