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An application of a model for a genotype‐dependent relationship between a concomitant (age) and a quantitative trait(LDL cholesterol)in pedigree data
Author(s) -
Moll Patricia P.,
Sing Charles F.,
LussierCacan Suzanne,
Davig Jean,
Rao D. C.
Publication year - 1984
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.1370010403
Subject(s) - trait , genotype , concomitant , quantitative trait locus , biology , cholesterol , ldl cholesterol , medicine , genetics , endocrinology , gene , computer science , programming language
In most genetic studies in humans the variability in a quantitative trait is adjusted for variability in concomitants (age, sex, etc) using a single regression equation prior to analyses of pedigree data. To illustrate an alternative approach, a single locus genetic model was tested. This model incorporates genotypic effects on the level of the trait, the variability in the trait, and the relationship between a concomitant and the trait. In this study, the model was applied to measures of age and low‐density lipoprotein (LDL) cholesterol in a large kindred with familial hypercholesterolemia. The application of this model to 322 individuals in four generations provided evidence that genotypic variation at a single locus influences LDL levels early in life, the rate of increase of LDL with age and the phenotypic variance. A model with genotype‐dependent slope and variance fit the data signifcantly better than a model with slope and variance independent of genotype. The inclusion of age‐specific genotypic differences contributed to identification of high‐risk individuals, to statistical support for a major locus, and to evidence for genetic determination of the tracking of LDL levels. Models that incorporate genotype‐specific concomitant effects have the potential to represent more realiscally the relationship between genotypic variability and quantitative phenotypic variation than models that assume that these effects do not exist.