Abstract
BackgroundThere 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.ResultsThe 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 %).ConclusionThese 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.
Highlights
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
Structural equation modeling The final structural equation modeling (SEM) models included a total of 19 previously identified BP-associated single nucleotide polymorphisms (SNPs) available in the Genetic Analysis Workshop 19 (GAW19) data set to identify mediators and effect modifiers to include in the identification of trajectory classes
Latent class growth modeling As the difference in SBP trajectories between men and women were not of primary interest for this study and sex impacted SBP directly and indirectly at multiple time points in the SEM, we adjusted for sex at each time point in the latent class growth modeling (LCGM)
Summary
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. 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. We first used previously identified BP single nucleotide polymorphisms (SNPs) to perform SEM and model the assumed underlying relationships between variables while taking into account potential genetic effects These SEM results were used to inform a semiparametric latent class growth modeling (LCGM), which was used to identify distinct groups of SBP change trajectories within the population, using a widely available statistical package (PROC TRAJ for SAS) [10, 11], for use in the GWA study analyses
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