Evidence that a single gene with gender- and age-dependent effects influences systolic blood pressure determination in a population-based sample.

Citation
Pérusse, Louis et al., Evidence that a single gene with gender- and age-dependent effects influences systolic blood pressure determination in a population-based sample., American journal of human genetics , 49-I(1), 1991, pp. 94-105
ISSN journal
00029297
Volume
49-I
Issue
1
Year of publication
1991
Pages
94 - 105
Database
ACNP
SICI code
Abstract
A biometrical study was carried out to evaluate the role of genetic variation in determining interindividual differences in systolic blood pressure (SBP) in the population at large.SBP was measured in 1,266 Caucasian individuals in 278 pedigrees ascertained through children enrolled in the Rochester, MN, school system.The sample included 646 males and 620 females 550 years of age and not taking antihypertensive medication or oral contraceptives.Complex segregation analysis was first applied to these data by using a regression model for age, in which the intercept was gender and ousiotype specific but in which the slope was only gender specific.When the slope was independent of ousiotype, neither variation at a single gene combined with polygenic effects (mixed genetic model) nor variation in a single environmental factor combined with polygenetic effects (mixed environmental model) explained the distribution of SBP in this sample.However, when the regression model for age allowed both the intercept and slope to be gender and ousiotype specific, the mixed environmental model was rejected whereas the mixed genetic model was not.These results suggest that variability in SBP may be influenced by major effects of allelic variation at a single gene that are both gender and age dependent.This study (1) suggests that particular genotypes determined by a single gene are associated with a steeper increase of SBP with age among males and females 550 years of age in the general population and (2) illustrates the need to consider models that more realistically represent the relationship between genotypic variability and phenotypic variability, to understand the genetics of human quantitative traits.