Identifying pleiotropic genes for complex phenotypes with summary statistics from a perspective of composite null...

Area of Science:

• Genetics
• Statistical Genetics
• Computational Biology

Background:

• Pleiotropy, where one gene influences multiple traits, is crucial for understanding complex phenotypes and disease etiology.
• Genome-wide association studies (GWAS) offer potential for pleiotropy detection, but efficient statistical methods are limited.
• Existing methods struggle with the composite null hypothesis inherent in pleiotropy testing and overlapping study subjects.

Purpose of the Study:

• To develop a novel, powerful, and efficient gene-based statistical method for identifying pleiotropic associations.
• To address the methodological challenges of composite null hypothesis testing and overlapping subjects in pleiotropy detection.
• To improve the accuracy and power of detecting shared genetic influences across complex phenotypes.

Main Methods:

• Proposed MAIUP (Method for Association Inference Under Pleiotropy), a gene-based method utilizing intersection-union test principles.
• Incorporated a three-component mixture null distribution to account for the composite null hypothesis in pleiotropy testing.
• Validated MAIUP through extensive simulation studies and applied it to GWAS summary statistics for 14 psychiatric disorders.

Main Results:

• MAIUP demonstrated superior performance, maintaining correct type I error control and higher statistical power compared to existing methods across various scenarios.
• The method effectively handled overlapping subjects, a common issue in association studies.
• Application to psychiatric disorders identified numerous novel pleiotropic genes not detected by other approaches, supported by functional and enrichment analyses.

Conclusions:

• MAIUP is an efficient and robust method for pleiotropy identification, offering well-calibrated P-values and effective control of error rates.
• The method enhances the discovery of shared genetic factors underlying complex traits, particularly in psychiatric disorders.
• MAIUP provides a valuable tool for advancing genetic research into disease etiology and complex phenotypes.