DESCRIPTION:

List of positional candidate genes after correcting for multiple testing and controlling the false discovery rate from genome wide association studies (GWAS) retrieved from the NHGRI-EBI Catalog of published genome-wide association studies (http://www.ebi.ac.uk/gwas/). The disease/trait examined in this study, as reported by the authors, was Systolic blood pressure change trajectories. The EFO term systolic blood pressure change measurement was annotated to this set after curation by NHGRI-EBI. Intergenic SNPS were mapped to both the upstream and downstream gene. P-value uploaded. This gene set was generated using gwas2gs v. 0.1.8 and the GWAS Catalog v. 1.0.1.

LABEL:

GWAS: systolic blood pressure change measurement

SCORE TYPE:

P-Value

DATE ADDED:

2017-05-02

DATE UPDATED:

2024-04-25

SPECIES:

AUTHORS:

AE Justice, AG Howard, G Chittoor, L Fernandez-Rhodes, M Graff, VS Voruganti, G Diao, SM Love, N Franceschini, JR O'Connell, CL Avery, KL Young, KE North

TITLE:

Genome-wide association of trajectories of systolic blood pressure change.

JOURNAL:

BMC proceedings None 2016, Vol 10, pp. 321-327

ABSTRACT:

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. PUBMED: 27980656
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