scholarly journals Fitting Proportional Odds Model to Case-Control data with Incorporating Hardy-Weinberg Equilibrium

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Wei Zhang ◽  
Zehui Zhang ◽  
Xinmin Li ◽  
Qizhai Li
Keyword(s):  
Case Control ◽  
Proportional Odds Model ◽  
Proportional Odds ◽  
Control Data ◽  
Weinberg Equilibrium ◽  
Hardy Weinberg Equilibrium

Significance of Hardy-Weinberg Equilibrium in Case Control

2004 ◽  
Vol 73 (1) ◽  
pp. 95-95
Author(s):  
Hemant Kumar Bid ◽  
Rama D. Mittal
Keyword(s):  
Case Control ◽  
Weinberg Equilibrium ◽  
Hardy Weinberg Equilibrium

A Simple Loglinear Model for Haplotype Effects in a Case-Control Study Involving Two Unphased Genotypes

2005 ◽  
Vol 4 (1) ◽  
Author(s):  
Stuart G. Baker
Keyword(s):  
Interactive Effect ◽  
Case Control ◽  
Nuisance Parameters ◽  
Design Matrix ◽  
Case Control Studies ◽  
Weinberg Equilibrium ◽  
Hardy Weinberg Equilibrium ◽  
Unphased Genotype ◽  
Unphased Genotype Data ◽  
Control Study

Because haplotypes may parsimoniously summarize the effect of genes on disease, there is great interest in using haplotypes in case-control studies of unphased genotype data. Previous methods for investigating haplotypes effects in case-control studies have not allowed for both of the following two scenarios that could have a large impact on results (i) departures from Hardy-Weinberg equilibrium in controls as well as cases, and (ii) an interactive effect of haplotypes and environmental covariates on the probability of disease. A new method is proposed that generalizes the model of Epstein and Satten to incorporate both (i) and (ii). Computations are relatively simple involving a single loglinear design matrix for parameters modeling the distribution of haplotype frequencies in controls, parameters modeling the effect of haplotypes and covariate-haplotype interactions on disease, and nuisance parameters required for correct inference. Based on simulations with realistic sample sizes, the method is recommended with data from two genotypes, a recessive or dominant model linking haplotypes to disease, and estimates of haplotype effects among haplotypes with a frequency greater than 10%. The methodology is most useful with candidate genotype pairs or for searching through pairs of genotypes when scenarios (i) and (ii) are likely. An example without a covariate illustrates the importance of modeling a departure from Hardy-Weinberg equilibrium in controls.

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