Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
BMC Proceedings
Published in: BMC Proceedings 7/2016

Open Access 01-10-2016 | Proceedings

Genome-wide association of trajectories of systolic blood pressure change

Authors: Anne E. Justice, Annie Green Howard, Geetha Chittoor, Lindsay Fernandez-Rhodes, Misa Graff, V. Saroja Voruganti, Guoqing Diao, Shelly-Ann M. Love, Nora Franceschini, Jeffrey R. O’Connell, Christy L. Avery, Kristin L. Young, Kari E. North

Published in: BMC Proceedings | Special Issue 7/2016

Login to get access

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.
Literature
1.
Chalmers J, MacMahon S, Mancia G, Whitworth J, Beilin L, Hansson L, Neal B, Rodgers A, Ni Mhurchu C, Clark T. 1999 World Health Organization-International Society of Hypertension Guidelines for the management of hypertension. Guidelines sub-committee of the World Health Organization. Clin Exp Hypertens. 1999;21(5-6):1009–60.CrossRefPubMed
2.
Levy D, DeStefano AL, Larson MG, O’Donnell CJ, Lifton RP, Gavras H, Cupples LA, Myers RH. Evidence for a gene influencing blood pressure on chromosome 17. Genome scan linkage results for longitudinal blood pressure phenotypes in subjects from the framingham heart study. Hypertension. 2000;36(4):477–83.CrossRefPubMed
3.
4.
Hossain A, Beyene J. Analysis of baseline, average, and longitudinally measured blood pressure data using linear mixed models. BMC Proc. 2014;8 Suppl 1:S80.CrossRefPubMedPubMedCentral
5.
Cho SC, Yoo HJ, Park M, Cho IH, Kim BN, Kim JW, Shin MS, Park TW, Son JW, Chung US, et al. Genome-wide association scan of korean autism spectrum disorders with language delay: a preliminary study. Psychiatry Investig. 2011;8(1):61–6.CrossRefPubMedPubMedCentral
6.
Connolly JJ, Glessner JT, Hakonarson H. A genome-wide association study of autism incorporating autism diagnostic interview-revised, autism diagnostic observation schedule, and social responsiveness scale. Child Dev. 2013;84(1):17–33.CrossRefPubMed
7.
Arnedo J, Svrakic DM, Del Val C, Romero-Zaliz R, Hernández-Cuervo H, Molecular Genetics of Schizophrenia Consortium, Fanous AH, Pato MT, Pato CN, de Erausquin GA, et al. Uncovering the hidden risk architecture of the schizophrenias: confirmation in three independent genome-wide association studies. Am J Psychiatry. 2015;172(2):139–53.CrossRefPubMed
8.
Hoadley KA, Yau C, Wolf DM, Cherniack AD, Tamborero D, Ng S, Leiserson MD, Niu B, McLellan MD, Uzunangelov V, et al. Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell. 2014;158(4):929–44.CrossRefPubMedPubMedCentral
9.
Londono D, Chen KM, Musolf A, Wang R, Shen T, Brandon J, Herring JA, Wise CA, Zou H, Jin M, Yu L, et al. A novel method for analyzing genetic association with longitudinal phenotypes. Stat Appl Genet Mol Biol. 2013;12(2):241–61.PubMed
10.
Jones B, Nagin D, Roeder K. A SAS procedure based on mixture models for estimating developmental trajectories. Sociol Methods Res. 2001;29:374–93.CrossRef
11.
12.
Almasy L, Dyer TD, Peralta JM, Jun G, Wood AR, Fuchsberger C, Almeida MA, Kent Jr JW, Fowler S, Blackwell TW, et al. Data for Genetic Analysis Workshop 18: human whole genome sequence, blood pressure, and simulated phenotypes in extended pedigrees. BMC Proc. 2014;8 Suppl 1:S2.CrossRefPubMedPubMedCentral
13.
Tobin MD, Sheehan NA, Scurrah KJ, Burton PR. Adjusting for treatment effects in studies of quantitative traits: antihypertensive therapy and systolic blood pressure. Stat Med. 2005;24(19):2911–35.CrossRefPubMed
14.
Kato N, Takeuchi F, Tabara Y, Kelly TN, Go MJ, Sim X, Tay WT, Chen CH, Zhang Y, Yamamoto K, et al. Meta-analysis of genome-wide association studies identifies common variants associated with blood pressure variation in east Asians. Nat Genet. 2011;43(6):531–8.CrossRefPubMedPubMedCentral
15.
Levy D, Ehret GB, Rice K, Verwoert GC, Launer LJ, Dehghan A, Glazer NL, Morrison AC, Johnson AD, Aspelund T, et al. Genome-wide association study of blood pressure and hypertension. Nat Genet. 2009;41(6):677–87.CrossRefPubMedPubMedCentral
16.
Newton-Cheh C, Johnson T, Gateva V, Tobin MD, Bochud M, Coin L, Najjar SS, Zhao JH, Heath SC, Eyheramendy S, et al. Genome-wide association study identifies eight loci associated with blood pressure. Nat Genet. 2009;41(6):666–76.CrossRefPubMedPubMedCentral
17.
He J, Kelly TN, Zhao Q, Li H, Huang J, Wang L, Jaquish CE, Sung YJ, Shimmin LC, Lu F, et al. Genome-wide association study identifies 8 novel loci associated with blood pressure responses to interventions in Han Chinese. Circ Cardiovasc Genet. 2013;6(6):598–607.CrossRefPubMedPubMedCentral
18.
Franceschini N, Kelly TN, Zhao Q, Li H, Huang J, Wang L, Jaquish CE, Sung YJ, Shimmin LC, Lu F, et al. Genome-wide association analysis of blood-pressure traits in African-ancestry individuals reveals common associated genes in African and non-African populations. Am J Hum Genet. 2013;93(3):545–54.CrossRefPubMedPubMedCentral
19.
Bhatnagar P, Barron-Casella E, Bean CJ, Milton JN, Baldwin CT, Steinberg MH, Debaun M, Casella JF, Arking DE. Genome-wide meta-analysis of systolic blood pressure in children with sickle cell disease. PLoS One. 2013;8(9):e74193.CrossRefPubMedPubMedCentral
20.
Hu LT, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equ Modeling. 1999;6(1):1–55.CrossRef
21.
22.
Tucker L, Lewis C. A reliability coefficient for maximum likelihood factor analysis. Psychometrika. 1973;38(1):1–10.CrossRef
23.
Nagin DS. Analyzing developmental trajectories: a semiparametric, group-based approach. Psychol Methods. 1999;4(2):139–57.CrossRef
24.
Nagin D. Group-based modeling of development. Cambridge: Harvard University Press; 2005.CrossRef
25.
Andruff H, Carraro N, Thompson A, Gaudreau P. Latent class growth modelling: a tutorial. Tutor Quant Methods Psychol. 2009;5(1):11–24.CrossRef
26.
Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 2006;38(8):904–9.CrossRefPubMed
27.
Quillen EE, Voruganti VS, Chittoor G, Rubicz R, Peralta JM, Almeida MA, Kent Jr JW, Diego VP, Dyer TD, Comuzzie AG, et al. Evaluation of estimated genetic values and their application to genome-wide investigation of systolic blood pressure. BMC Proc. 2014;8 Suppl 1:S66.CrossRefPubMedPubMedCentral
28.
29.
O’Connell JR. MMAP user guide. Baltimore: University of Maryland; 2014.
30.
Voruganti VS, Kent Jr JW, Debnath S, Cole SA, Haack K, Göring HH, Carless MA, Curran JE, Johnson MP, Almasy L, et al. Genome-wide association analysis confirms and extends the association of SLC2A9 with serum uric acid levels to Mexican Americans. Front Genet. 2013;4:279.CrossRefPubMedPubMedCentral
Metadata
Title
Genome-wide association of trajectories of systolic blood pressure change
Authors
Anne E. Justice
Annie Green Howard
Geetha Chittoor
Lindsay Fernandez-Rhodes
Misa Graff
V. Saroja Voruganti
Guoqing Diao
Shelly-Ann M. Love
Nora Franceschini
Jeffrey R. O’Connell
Christy L. Avery
Kristin L. Young
Kari E. North
Publication date
01-10-2016
Publisher
BioMed Central
Published in
BMC Proceedings / Issue Special Issue 7/2016
Electronic ISSN: 1753-6561
DOI
https://doi.org/10.1186/s12919-016-0050-9

Other articles of this Special Issue 7/2016

BMC Proceedings 7/2016 Go to the issue

Proceedings

The impact of genetic structure on sequencing analysis

Proceedings

Genome-wide joint analysis of single-nucleotide variant sets and gene expression for hypertension and related phenotypes

Proceedings

Identification of interactions using model-based multifactor dimensionality reduction

Proceedings

Powerful association test combining rare variant and gene expression using family data from Genetic Analysis Workshop 19

Proceedings

Imputing rare variants in families using a two-stage approach

Proceedings

Prioritization of family member sequencing for the detection of rare variants