Genome scan linkage results for longitudinal systolic blood pressure phenotypes in subjects from the Framingham Heart Study

• Published in: BMC genetics Vol. 4 Suppl 1; no. Suppl 1; p. S83
• Main Authors: James, Katherine, Weitzel, Lindsay-Rae B, Engelman, Corinne D, Zerbe, Gary, Norris, Jill M
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 31.12.2003; BioMed Central
• Subjects: Age Factors; Blood Pressure - genetics; Body Mass Index; Cardiovascular Diseases - epidemiology; Cardiovascular Diseases - genetics; Chromosome Mapping - statistics & numerical data; Chromosomes, Human, Pair 1 - genetics; Chromosomes, Human, Pair 17 - genetics; Chromosomes, Human, Pair 3 - genetics; Chromosomes, Human, Pair 5 - genetics; Chromosomes, Human, Pair 8 - genetics; Cohort Studies; Female; Genetic Linkage - genetics; Genetic Markers - genetics; Genetic Testing - statistics & numerical data; Genome, Human; Humans; Hypertension - epidemiology; Hypertension - genetics; Longitudinal Studies; Male; Phenotype; Proceedings
• ISSN: 1471-2156; 1471-2156
• DOI: 10.1186/1471-2156-4-S1-S83

The relationship between elevated blood pressure and cardiovascular and cerebrovascular disease risk is well accepted. Both systolic and diastolic hypertension are associated with this risk increase, but systolic blood pressure appears to be a more important determinant of cardiovascular risk than diastolic blood pressure. Subjects for this study are derived from the Framingham Heart Study data set. Each subject had five records of clinical data of which systolic blood pressure, age, height, gender, weight, and hypertension treatment were selected to characterize the phenotype in this analysis. We modeled systolic blood pressure as a function of age using a mixed modeling methodology that enabled us to characterize the phenotype for each individual as the individual's deviation from the population average rate of change in systolic blood pressure for each year of age while controlling for gender, body mass index, and hypertension treatment. Significant (p = 0.00002) evidence for linkage was found between this normalized phenotype and a region on chromosome 1. Similar linkage results were obtained when we estimated the phenotype while excluding values obtained during hypertension treatment. The use of linear mixed models to define phenotypes is a methodology that allows for the adjustment of the main factor by covariates. Future work should be done in the area of combining this phenotype estimation directly with the linkage analysis so that the error in estimating the phenotype can be properly incorporated into the genetic analysis, which, at present, assumes that the phenotype is measured (or estimated) without error.