Ordered-subset analysis (OSA) for family-based association mapping of complex traits

Area of Science:

• Genetics
• Statistical Genetics
• Bioinformatics

Background:

• Genetic heterogeneity can reduce the power of standard association analyses for complex diseases.
• Family-based association tests (FBAT) are valuable but can be limited by heterogeneity.
• Ordered-subset analysis (OSA) is a linkage method effective in heterogeneous datasets.

Purpose of the Study:

• To introduce a novel statistical method, Association in the Presence of Linkage-Ordered Subset Analysis (APL-OSA).
• To enhance the power of genetic association studies in the presence of genetic heterogeneity.
• To adapt the OSA strategy for family-based association testing.

Main Methods:

• APL-OSA integrates the Association in the Presence of Linkage (APL) statistic with the OSA framework.
• A permutation procedure is employed to determine the statistical significance of the APL-OSA statistic.
• Comprehensive simulations were conducted to evaluate performance under the null hypothesis and in scenarios with heterogeneity.

Main Results:

• APL-OSA demonstrated a correct type I error rate in simulations.
• The proposed method significantly increased power compared to APL, FBAT, and covariate-adjusted FBAT in heterogeneous datasets.
• Simulations confirmed the utility of APL-OSA for increasing power in the presence of genetic heterogeneity.

Conclusions:

• APL-OSA is a powerful extension of family-based association testing for complex diseases.
• The method effectively addresses challenges posed by genetic heterogeneity.
• APL-OSA shows promise for improving gene discovery in complex genetic studies, as demonstrated in an age-related macular degeneration example.