A General and Robust Framework for Secondary Traits Analysis.

Area of Science:

• Genetics
• Biostatistics
• Epidemiology

Background:

• Case-control studies are common in genetic association research.
• Secondary traits are often collected alongside case-control status.
• Direct regression of secondary traits in case-control samples can yield biased estimates.

Purpose of the Study:

• To introduce a novel weighted estimating equation (WEE) approach for unbiased estimation of genetic associations with secondary traits.
• To evaluate the robustness and performance of the WEE compared to existing methods like inverse probability weighting (IPW) and semiparametric maximum-likelihood (SPML).

Main Methods:

• The proposed WEE approach combines observed and counterfactual outcomes.
• Simulations were conducted to assess WEE performance under diverse models and sampling schemes.
• The WEE was applied to an asthma case-control study investigating gene-trait associations.

Main Results:

• The WEE approach demonstrated robustness across all simulated scenarios.
• WEE exhibited appropriate Type I error rates and comparable or superior statistical power to IPW and SPML.
• In an asthma study, WEE identified novel single nucleotide polymorphism (SNP) associations with overweight status, serum IgE levels, and specific IgE quantiles.

Conclusions:

• The WEE approach provides a general and robust framework for secondary trait analyses in genetic studies.
• This method complements existing approaches and aids in discovering new genetic associations.
• WEE is a valuable tool for researchers analyzing complex genetic data and secondary phenotypes.