Testing for association between ordinal traits and genetic variants in pedigree-structured samples by collapsing and...

Area of Science:

• Genetics
• Statistical Genetics
• Bioinformatics

Background:

• Genome-wide association studies (GWAS) commonly use logistic regression for binary traits.
• Existing GWAS methods are often ill-suited for ordinal traits, leading to statistical errors and reduced power.
• Analyzing ordinal traits in GWAS is challenging due to a lack of appropriate statistical methods.

Purpose of the Study:

• To develop a general framework for identifying genetic variants associated with ordinal traits in pedigree-structured samples.
• To address the limitations of current GWAS methods when applied to ordered categorical data.
• To provide a robust statistical approach for genetic association studies involving ordinal traits.

Main Methods:

• Development of a general framework using collapsing and kernel methods.
• Application of local odds ratios generalized estimating equations (GEE) to handle complex correlations within families.
• Utilizing a retrospective approach to model genetic markers as random variables for calculating genetic correlations.
• Incorporation of covariate adjustment capabilities within the proposed method.

Main Results:

• The proposed method accommodates ordinal traits and allows for covariate adjustment.
• Simulation studies demonstrated the effectiveness of the new tests compared to existing models.
• The method was successfully applied to analyze both family and cross-sectional data from the Genetic Analysis Workshop 19 (GAW19).

Conclusions:

• The developed framework provides a statistically sound approach for GWAS of ordinal traits.
• This method improves upon existing techniques by accurately handling ordered categorical data and complex family structures.
• The findings offer a valuable tool for genetic research involving traits with multiple ordered categories.