Genome-wide association of trajectories of systolic blood pressure change

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  • Justice, Anne E
    • Affiliation: Gillings School of Global Public Health, Department of Epidemiology
  • Howard, Annie Green
    • Affiliation: Gillings School of Global Public Health, Department of Biostatistics
  • Chittoor, Geetha
    • Affiliation: Gillings School of Global Public Health, Department of Nutrition
  • Fernández-Rhodes, Lindsay
    • Affiliation: Gillings School of Global Public Health, Department of Epidemiology
  • Graff, Misa
    • Affiliation: Gillings School of Global Public Health, Department of Epidemiology
  • Voruganti, Venkata S
    • Affiliation: Gillings School of Global Public Health, Department of Nutrition
  • Diao, Guoqing
    • Other Affiliation: Department of Statistics, George Mason University, Fairfax, VA 22030, USA
  • Love, Shelly-Ann M
    • Affiliation: Gillings School of Global Public Health, Department of Epidemiology
  • Franceschini, Nora
    • Affiliation: Gillings School of Global Public Health, Department of Epidemiology
  • O’Connell, Jeffrey R
    • Other Affiliation: School of Medicine, University of Maryland, Baltimore, MD 21201, USA
  • Avery, Christy
    • Affiliation: Gillings School of Global Public Health, Department of Epidemiology
  • Young, Kristin L
    • Affiliation: Gillings School of Global Public Health, Department of Epidemiology
  • North, Kari
    • Affiliation: Gillings School of Global Public Health, Department of Epidemiology
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.
Date of publication
DOI
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Resource type
  • Article
Rights statement
  • In Copyright
Rights holder
  • The Author(s).
Journal title
  • BMC Proceedings
Journal volume
  • 10
Journal issue
  • Suppl 7
Page start
  • 321
Page end
  • 327
Language
  • English
Bibliographic citation
  • BMC Proceedings. 2016 Oct 18;10(Suppl 7):56
Publisher
  • BioMed Central

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