Premium
A bivariate genetic analysis of HDL‐ and LDL‐cholesterol incorporating measured covariates: A gibbs sampling application
Author(s) -
Mack Wendy J.,
Gauderman W. James,
Thomas Duncan C.
Publication year - 1993
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.1370100649
Subject(s) - polygene , bivariate analysis , covariate , major gene , gibbs sampling , biology , genetics , sampling (signal processing) , gene , phenotype , statistics , mathematics , quantitative trait locus , computer science , bayesian probability , filter (signal processing) , computer vision
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 analyses illustrate the potential applicability of Gibbs sampling to such complex problems as the multivariate analysis of continuous phenotypes. © 1993 Wiley‐Liss, Inc.