title: A Hierarchical Generalized Linear Model in Combination with Dispersion Modeling to Improve Sib-Pair Linkage Analysis.
abstract: OBJECTIVE
  We explored a 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 ratio (SNR) 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 is low, i.e., when substantial noise was present in the data.
  
  
  CONCLUSIONS
  The study shows 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.
authors.affiliation: Department of Statistics, Inha University, Incheon, Republic of Korea.
authors.forename: Woojoo
authors.initials: W
authors.lastname: Lee
authors.forename: Jeonghwan
authors.initials: J
authors.lastname: Kim
authors.forename: Youngjo
authors.initials: Y
authors.lastname: Lee
authors.forename: Taesung
authors.initials: T
authors.lastname: Park
authors.forename: Young Ju
authors.initials: YJ
authors.lastname: Suh
mesh_terms: D030541:Databases, Genetic; D005260:Female; D008040:Genetic Linkage; D006801:Humans; D008297:Male; D008957:Models, Genetic; D040641:Quantitative Trait Loci