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Commingling analysis of the distribution of a phenotype conditioned on two marker genotypes: Application to plasma angiotensin‐converting enzyme levels
Author(s) -
Barrett Jennifer H.,
Foy Carole A.,
Grant Peter J.
Publication year - 1996
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/(sici)1098-2272(1996)13:6<615::aid-gepi7>3.0.co;2-z
Subject(s) - biology , genotype , genetics , gene , genetic marker , angiotensin converting enzyme , microbiology and biotechnology , endocrinology , blood pressure
Commingling analysis is a statistical method for distinguishing between one (usually normal) distribution and a mixture of two or more distributions. It is used in genetic studies, since one mechanism giving rise to a mixture of distributions is the effect of a major gene. Plasma levels of angiotensin‐converting enzyme (ACE) have been shown to be related to an insertion/deletion (I/D) polymorphism in the ACE gene. Recently, Cambien et al. [(1994) Circulation 90:669–676] used commingling analysis in a study of ACE, extending the method to condition on information about the I/D polymorphism in the gene. A further common polymorphism has been discovered recently by our group [Foy et al. (1995) Blood Coagulation Fibrinolysis 6:590] in the promoter region of the gene. In this paper we extend the method of commingling analysis to condition on two marker loci. The method is illustrated by application to plasma ACE levels in subjects for whom genetic information at both the I/D and promoter loci has been recorded. The results confirm strong evidence for a mixture of three normal distributions in preference to one or two, and also show that neither polymorphism is identical to a putative functional polymorphism. To assess the performance of the method, a simulation study was carried out. Parameters may be estimated more efficiently conditioning on two marker loci than on one or none, but this is not always the case if the underlying distributions are well separated. Conditioning on marker loci can increase the power to distinguish between alternative hypotheses of interest. © 1996 Wiley‐Liss, Inc.