Coupled mixed model for joint genetic analysis of complex disorders with two independently collected data sets

Area of Science:

• Genetics
• Bioinformatics
• Computational Biology

Background:

• Genome-wide Association Studies (GWAS) identify genetic variations linked to diseases.
• Joint analysis of multiple GWAS datasets is crucial for follow-up investigations.
• Analyzing individual-level GWAS data with confounding factors presents computational challenges.

Purpose of the Study:

• To develop a novel computational method for joint analysis of independent GWAS datasets.
• To enable joint analysis even when datasets have different phenotypes.
• To address confounding variables in genetic studies.

Main Methods:

• Propose the Coupled Mixed Model (CMM) for joint GWAS analysis.
• Utilize multivariate sparse mixed models to infer unknown phenotypes.
• Incorporate methods to handle population stratification, family structures, cryptic relatedness, and batch effects.

Main Results:

• CMM enables joint GWAS analysis on two independently collected datasets with different phenotypes.
• The method effectively infers unknown phenotypes and accounts for various confounding factors.
• Simulations and real-data analysis demonstrate CMM's utility in identifying common genetic associations for complex diseases like Alzheimer's and substance use disorder.

Conclusions:

• CMM provides a robust approach for joint GWAS analysis on diverse datasets.
• The method offers new insights into the genetic underpinnings of complex human disorders.
• The developed software is publicly available for researchers.