A novel method for testing association of multiple genetic markers with a multinomial trait

Area of Science:

• Genetics
• Computational Biology
• Metabolic Diseases

Background:

• Prediabetes is a significant risk factor for type 2 diabetes.
• Identifying genetic determinants of prediabetes subtypes is crucial for understanding disease mechanisms.
• Current methods for analyzing genetic associations with complex diseases can be limited in scale.

Purpose of the Study:

• To develop and validate a statistical model for simultaneously testing a large number of single nucleotide polymorphisms (SNPs) for association with prediabetes.
• To identify specific genetic associations with two prediabetes phenotypes: impaired glucose tolerance (IGT) and impaired fasting glucose (IFG).

Main Methods:

• Development of a multinomial probit model incorporating singular value decomposition.
• Utilizing maximum likelihood estimation and permutation for statistical inference.
• Simulation studies to validate the model's performance in identifying associated SNPs.

Main Results:

• The developed method successfully identified 8 out of 9 simulated disease-associated SNPs.
• Application to Mexican-American samples revealed 3 genes associated with both IGT and IFG.
• Additional genes were uniquely associated with either IGT (5 genes) or IFG (6 genes).

Conclusions:

• The findings suggest that impaired glucose tolerance and impaired fasting glucose represent distinct physiological mechanisms contributing to prediabetes.
• Different genetic determinants underlie these distinct pathways.
• The developed statistical model is effective for large-scale genetic association studies in complex diseases.