title: A bivariate genetic analysis of HDL- and LDL-cholesterol incorporating measured covariates: a Gibbs sampling application.
abstract: We analyzed HDL- and LDL-cholesterol levels as a bivariate phenotype in 27 families as a function of major genes, polygenes, and measured covariates using a Monte Carlo sampling technique called Gibbs sampling. Major genes and polygenes exhibited strong effects, when considered separately. While a major gene versus polygene model could not be clearly differentiated for HDL-C, polygenes appeared to play a stronger role than a major gene for LDL-C. There was no evidence of linkage between the two major genes for HDL- and LDL-C, and the correlation in polygenes was negative. The analysis illustrate the potential applicability of Gibbs sampling to such complex problems as the multivariate analysis of continuous phenotypes.
authors.affiliation: Department of Preventive Medicine, University of Southern California, Los Angeles 90033.
authors.forename: W J
authors.initials: WJ
authors.lastname: Mack
authors.forename: W J
authors.initials: WJ
authors.lastname: Gauderman
authors.forename: D C
authors.initials: DC
authors.lastname: Thomas
mesh_terms: D008076:Cholesterol, HDL / Q000235:genetics*; D008078:Cholesterol, LDL / Q000235:genetics*; D003327:Coronary Disease / Q000235:genetics; D005260:Female; D008040:Genetic Linkage; D020022:Genetic Predisposition to Disease; D006801:Humans; D008297:Male; D008957:Models, Genetic; D015233:Models, Statistical; D009010:Monte Carlo Method; D015999:Multivariate Analysis; D010641:Phenotype; D012307:Risk Factors
chemical_list: D008076:Cholesterol, HDL; D008078:Cholesterol, LDL