Genome-wide pathway association studies of multiple correlated quantitative phenotypes using principle component...

Area of Science:

• Genetics
• Bioinformatics
• Computational Biology

Background:

• Complex diseases involve multiple phenotypes, often inadequately captured by single-phenotype association studies.
• Current genome-wide pathway association studies typically focus on a single disease phenotype, limiting biological insight.
• Understanding the genetic underpinnings of complex diseases requires methods that can integrate multiple phenotypic traits.

Purpose of the Study:

• To develop and evaluate a novel multi-phenotypes pathway association study (MPPAS) approach.
• To leverage principle component analysis (PCA) for integrating multiple correlated quantitative phenotypes.
• To enhance the identification of biological pathways associated with complex diseases.

Main Methods:

• Principle component analysis (PCA) was applied to multiple correlated quantitative phenotypes to extract orthogonal phenotypic components.
• These extracted components were used for pathway association analysis, replacing individual phenotypes.
• Four statistical methods were proposed for the PCA-based MPPAS, with performance evaluated through simulations using HapMap data.

Main Results:

• Simulation studies demonstrated the power and type I error rates of PCA-based MPPAS across various genetic effect scenarios.
• Genome-wide MPPAS analysis of bone mineral density (BMD) identified significant associations with KENNY_CTNNB1_TARGETS_UP and LONGEVITYPATHWAY.
• The approach effectively identified causal pathways underlying complex diseases in simulation and real-world data.

Conclusions:

• The developed PCA-based MPPAS approach offers a robust method for analyzing complex diseases with multiple phenotypes.
• This method provides deeper insights into the biological mechanisms driving complex diseases by considering multiple traits.
• MPPAS enhances our understanding of disease associations and potential therapeutic targets.