Robust quantitative trait association tests in the parent-offspring triad design: conditional likelihood-based...

Area of Science:

• Genetics
• Biostatistics
• Complex Disease Research

Background:

• Family-based association studies, like the transmission/disequilibrium test (TDT), are crucial for mapping genes in complex diseases.
• While TDT is established for binary traits, robust methods for quantitative traits are less explored.
• Population stratification can lead to spurious associations, necessitating robust analytical approaches.

Purpose of the Study:

• To develop and evaluate robust candidate-gene association tests for quantitative traits using parent-offspring triad families.
• To address the limited attention on robust analysis for quantitative traits in complex diseases.
• To demonstrate the feasibility of robust association testing in this context.

Main Methods:

• Utilized parent-offspring triad families for association analysis.
• Introduced score statistics derived from conditional likelihoods under various genetic models.
• Applied existing robust procedures to construct novel association tests for quantitative traits.
• Conducted simulations to assess type I error rates and statistical power.

Main Results:

• The proposed robust association tests demonstrated robustness against misspecification of the underlying genetic model.
• Simulations confirmed the reliability of the empirical type I error rates and powers.
• The developed methods are feasible for establishing robust candidate-gene association tests.

Conclusions:

• Robust association tests can be effectively established for quantitative traits using parent-offspring data.
• These tests offer reliable power even when the genetic model is uncertain.
• The findings contribute to more accurate gene mapping for complex diseases with quantitative traits.