Journal Articles

Genome-wide association of trajectories of systolic blood pressure change

December 15, 2016

Justice AE, Howard AG, Chittoor G (equal contribution as first author), Fernandez-Rhodes L, Graff M, Voruganti VS, Diao G, Love SM, Franceschini, N, O’Connell JR, Avery CL, Young KL, North KE. Genome-wide association of trajectories of systolic blood pressure change. BMC Proceedings. 10(Suppl 7):56.

Author Affiliations

1. Department of Epidemiology University of North Carolina Chapel Hill USA
2. Department of Biostatistics University of North Carolina Chapel Hill USA
3. Department of Nutrition, and UNC Nutrition Research Institute University of North Carolina Kannapolis USA
4. Department of Statistics George Mason University Fairfax USA
5. School of Medicine University of Maryland Baltimore USA

Abstract

Background

There is great interindividual variation in systolic blood pressure (SBP) as a result of the influences of several factors, including sex, ancestry, smoking status, medication use, and, especially, age. The majority of genetic studies have examined SBP measured cross-sectionally; however, SBP changes over time, and not necessarily in a linear fashion. Therefore, this study conducted a genome-wide association (GWA) study of SBP change trajectories using data available through the Genetic Analysis Workshop 19 (GAW19) of 959 individuals from 20 extended Mexican American families from the San Antonio Family Studies with up to 4 measures of SBP. We performed structural equation modeling (SEM) while taking into account potential genetic effects to identify how, if at all, to include covariates in estimating the SBP change trajectories using a mixture model based latent class growth modeling (LCGM) approach for use in the GWA analyses.

Results

The semiparametric LCGM approach identified 5 trajectory classes that captured SBP changes across age. Each LCGM identified trajectory group was ranked based on the average number of cumulative years as hypertensive. Using a pairwise comparison of these classes the heritability estimates range from 12 to 94 % (SE = 17 to 40 %).

Conclusion

These identified trajectories are significantly heritable, and we identified a total of 8 promising loci that influence one’s trajectory in SBP change across age. Our results demonstrate the potential utility of capitalizing on extant genetic data and longitudinal SBP assessments available through GAW19 to explore novel analytical methods with promising results.

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