A Novel One-Sample Mendelian Randomization Approach for Count-Type Outcomes That Is Robust to Correlated and...

Area of Science:

• Genetics and Epidemiology
• Statistical Genetics
• Biostatistics

Background:

• Mendelian randomization (MR) is a powerful tool for causal inference.
• Count-type health outcomes present unique challenges for MR analysis.
• Validating instrumental variables (IVs), such as single-nucleotide polymorphisms (SNPs), is crucial for MR reliability.

Purpose of the Study:

• To propose novel one-sample MR methods for count-type health outcomes.
• To address and mitigate violations of IV assumptions in MR analyses.
• To evaluate the causal effect of fetal hemoglobin (HbF) on sickle cell disease (SCD) complications.

Main Methods:

• Developed two one-sample MR approaches for equidispersion and overdispersion.
• Incorporated a robust SNP selection process to remove invalid IVs.
• Validated methods through simulations assessing robustness, type-I error, and statistical power.

Main Results:

• Proposed MR methods demonstrated robustness to IV assumption violations.
• Simulations confirmed valid causal estimates, interpretable type-I errors, and statistical power.
• Identified a causal relationship between HbF levels and acute chest syndrome (ACS) events in SCD patients.

Conclusions:

• The novel MR approaches enhance causal inference for count data.
• The methods provide practical tools for analyzing genetic associations with health outcomes.
• A user-friendly Shiny web application is available for broader research application.