A Hierarchical Generalized Linear Model in Combination with Dispersion Modeling to Improve Sib-Pair Linkage Analysis

초록

Objective: We explored the hierarchical generalized linear model (HGLM) in combination with dispersion modeling to improve the sib-pair linkage analysis based on the revised Haseman-Elston regression model for a quantitative trait. Methods: A dispersion modeling technique was investigated for sib-pair linkage analysis using simulation studies and real data applications. We considered 4 heterogeneous dispersion settings according to a signal-to-noise (SNR) ratio in the various statistical models based on the Haseman-Elston regression model. Results: Our numerical studies demonstrated that susceptibility loci could be detected well by modeling the dispersion parameter appropriately. In particular, the HGLM had better performance than the linear regression model and the ordinary linear mixed model when the SNR ratio is low, that is, when substantial noise was present in data. Conclusion: The study showed that the HGLM in combination with dispersion modeling can be utilized to identify multiple markers showing linkage to familial complex traits accurately. Appropriate dispersion modeling might be more powerful to identify markers closest to the major genes which determine a quantitative trait.