Inference of the haplotype effect in a matched case-control study using unphased genotype data

Area of Science:

• Genetics
• Biostatistics
• Epidemiology

Background:

• Genotype data often lacks haplotype phase information, complicating disease association studies.
• Studying disease-haplotype associations with unphased data presents challenges similar to missing covariate analysis.

Purpose of the Study:

• To propose a novel conditional likelihood approach for disease-haplotype association inference.
• To address the challenge of analyzing unphased genotype data in matched case-control studies.

Main Methods:

• A conditional likelihood approach based on a logistic disease risk model.
• Utilizing Hardy-Weinberg equilibrium (HWE) in the control population.
• Development of an expectation and conditional maximization (ECM) algorithm for joint estimation.

Main Results:

• The proposed method enables inference on disease-haplotype associations from unphased genotype data.
• The ECM algorithm effectively estimates haplotype frequencies and association parameters.
• Successful application to real-world case-control studies, including cancer prevention and breast cancer data.

Conclusions:

• The conditional likelihood and ECM approach provide a robust framework for disease-haplotype association studies.
• This method enhances the analysis of genetic data by accounting for missing haplotype phase information.
• The approach is validated through simulations and real-world data applications, offering a valuable tool for genetic epidemiology.