Joint analysis of multiple phenotypes for extremely unbalanced case-control association studies

Area of Science:

• Genetics
• Statistical Genetics
• Bioinformatics

Background:

• Genome-wide association studies (GWAS) often involve numerous phenotypes with imbalanced case-control ratios.
• Existing methods for joint analysis of multiple unbalanced phenotypes can lead to inflated type I error rates.

Purpose of the Study:

• To develop a robust statistical method for the joint analysis of multiple unbalanced case-control phenotypes in GWAS.
• To address the issue of inflated type I error rates in association testing with sparse case data.

Main Methods:

• Phenotypes are clustered using hierarchical clustering and merged within clusters.
• Saddlepoint approximation is employed for association testing of merged phenotypes with single nucleotide polymorphisms (SNPs).
• Cauchy combination method integrates p-values across clusters for a comprehensive association test.

Main Results:

• Extensive simulations demonstrate that the proposed method effectively controls type I error rates.
• The approach shows superior statistical power compared to existing methods.
• Application to UK Biobank data identified more significant SNPs for circulatory system diseases.

Conclusions:

• The developed method provides a reliable and powerful tool for joint analysis of multiple unbalanced phenotypes in GWAS.
• This approach improves the identification of genetic associations in large-scale biobank studies.
• The method is particularly valuable for phenotypes with rare or sparse case data.