Variance components genetic association test for zero-inflated count outcomes.

Area of Science:

• Biostatistics
• Genetics
• Neuroscience

Background:

• Biomedical research frequently encounters outcome data with excess zeros, complicating standard statistical analyses.
• Informative excess zeros, such as the absence of neuritic plaques, are crucial for understanding neurodegenerative diseases.

Purpose of the Study:

• To propose a novel variance components score test for genetic association with zero-inflated count outcomes from a mixture distribution.
• To enhance statistical power in genetic association studies with complex outcome data.

Main Methods:

• Developed a variance components score test for genetic association with zero-inflated count data.
• The test utilizes a mixture distribution with structural zeros and a Poisson component.
• Leveraged single-nucleotide polymorphism (SNP)-set test principles for improved performance.

Main Results:

• The proposed method demonstrates superior statistical power compared to existing approaches.
• Achieved higher power particularly with correlated markers and SNPs influencing both mixing proportion and Poisson rate.
• Identified significant associations with the APOE gene in Alzheimer's data for both zero and count parameters.

Conclusions:

• The novel score test is effective for analyzing genetic associations with zero-inflated outcomes in biomedical research.
• The method provides a powerful tool for exploring genetic contributions to neurodegenerative diseases using complex count data.
• The findings highlight the importance of the APOE gene in Alzheimer's disease pathology, impacting both absence and presence of neuritic plaques.