Joint analyses of disease and correlated quantitative phenotypes using next-generation sequencing data

Area of Science:

• Genomics
• Statistical Genetics
• Bioinformatics

Background:

• Joint analysis of multiple disease phenotypes increases statistical power for identifying pleiotropic genes in chronic diseases.
• Next-generation sequencing data necessitates methods to detect rare variants effectively.
• Genetic Analysis Workshop 17 (GAW17) provided exome sequence data with quantitative phenotypes for simulated replicates.

Purpose of the Study:

• To explore statistical methods for identifying causal variants using next-generation sequencing data.
• To investigate the utility of analyzing multiple disease phenotypes for complex disease biology.
• To assess the impact of using family versus unrelated individuals in genetic analyses.

Main Methods:

• Utilizing data reduction or joint methods for multiple phenotypes.
• Applying various statistical approaches to analyze exome sequence data.
• Investigating strategies to reduce data dimensionality and address multiple testing issues.

Main Results:

• Family and unrelated case-control samples are optimal for detecting different types of genetic variants.
• Collapsing phenotypes or genotypes reduces data dimensionality and mitigates multiple testing burdens.
• Joint analysis of disease status and quantitative traits demonstrated improved statistical power in specific cases.

Conclusions:

• Multiple phenotypes enhance analytical power and clarify complex disease biology.
• Data reduction techniques like collapsing are effective for dimensionality reduction and multiple testing.
• Joint analysis strategies are crucial for maximizing insights from genomic data in complex disease research.