A two-stage mixed-effects model approach for gene-set analyses in candidate gene studies

Area of Science:

• Genetics
• Biostatistics
• Human Disease Research

Background:

• Gene-set analysis enhances genetic association studies by examining joint effects of multiple single-nucleotide polymorphisms (SNPs) within functionally related genes.
• Existing methods often overlook crucial within-gene and between-gene correlations, limiting their power and scope.
• Understanding the genetic architecture of common human diseases requires sophisticated analytical approaches.

Purpose of the Study:

• To develop a robust two-stage gene-set analysis approach that accounts for correlations among genetic variants.
• To provide a flexible framework applicable to diverse phenotypic outcomes and genetic models.
• To improve the power and interpretability of gene-set analyses in human disease research.

Main Methods:

• A two-stage approach was implemented: Stage 1 utilized a mixed-effects model with a general random-effects structure to capture SNP correlations.
• Stage 2 employed empirical Bayes estimates from Stage 1 as covariates in a longitudinal phenotype model.
• The method was designed for broad applicability and implementation in standard statistical software.

Main Results:

• The proposed method effectively models within-gene and between-gene correlations, enhancing analytical power.
• Empirical Bayes estimates provide robust covariates for testing gene-set effects.
• The approach demonstrated broad applicability across various phenotypic outcomes and genetic models.

Conclusions:

• This novel two-stage method offers a powerful and flexible tool for gene-set analysis in genetic association studies.
• It addresses limitations of existing approaches by incorporating complex correlation structures.
• The method facilitates deeper insights into the genetic underpinnings of common human diseases.