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Am J Hum Genet. 1994 Feb;54(2):361-73.

The contribution of pleiotropy to blood pressure and body-mass index variation: the Gubbio Study.

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1
Department of Medicine, School of Public Health, University of Michigan, Ann Arbor.

Abstract

Blood pressure (BP), body-mass index (BMI), and quantitative phenotypes thought to influence BP (e.g., lithium-sodium countertransport activity) were studied in 2,184 households comprising 5,376 people in Gubbio, Italy. Variance-components models were used to partition the variation of these phenotypes into components characterizing the effects of age-related, measured environmental, additive genetic, pleiotropic, unmeasured shared-household, and individual-specific (or random) factors. The goal of the investigation was to estimate the contribution of pleiotropy to variation in BP and BMI in population-based samples. Although our results suggest that numerous significant bivariate genetic correlations exist between BP and some of the traits investigated, they ultimately lead us to reject a prominent role for any individual bivariate pleiotropic system influencing the natural variation of BP. However, because we found evidence that many traits enter into small-impact pleiotropic relationships with BP, we cannot rule out the possibility that pleiotropic genes, when considered collectively, may contribute to BP variation at the population level. Similar results were obtained when BMI was taken as the primary variable of interest. We argue that the small but significant portion of BP variation explained by individual genes displaying bivariate pleiotropic effects is intuitive, in light of the relatively low heritabilities associated with quantitative cardiovascular phenotypes and the low phenotypic correlations between BP, BMI, and many other physiologically linked measures of cardiovascular function. Our results not only bear directly on both the nature of the multifactorial determinants of BP and the maintenance of BP variation in the population at large, but also emphasize the utility of variance-components models in epidemiologic and population genetics research. We discuss the implications of our results for genetic epidemiologists and medical researchers studying hypertension, as well as the limitations of our study and areas for future research.

PMID:
8304351
PMCID:
PMC1918169
[Indexed for MEDLINE]
Free PMC Article
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