Unknown

Dataset Information

0

The Contribution of the Framingham Heart Study to Gene Identification for Cardiovascular Risk Factors and Coronary Heart Disease.


ABSTRACT: Genome-wide association studies have been published since 2005 and remain exemplary in translating knowledge fostered by the human genome project into genomic lessons on health and disease. Although our understanding of the basis of complex disease remains by far incomplete, the knowledge of the genetic basis of cardiovascular risk factors and their end organ damage has been significantly improved. The Framingham Heart Study was one of the earliest population-based studies to apply genomic methods and is an important contributor to large disease-based consortia as the International Consortium for Blood Pressure Genome-Wide Association Studies (ICBP), the Global Lipids Genetics Consortium (GLGC), the DIAbetes Genetics Replication And Meta-analysis consortium (DIAGRAM), and the Coronary ARtery DIsease Genome-wide Replication And Meta-Analysis consortium (CARDIoGRAM). The variability of these principal cardiovascular risk factors is to large extent genetic and knowledge on the genetic basis originated largely from analysis of monogenic disease in rare syndromes before the use of genome-wide, common SNP analysis. Genome-wide association studies have identified ~45 common variants associated with systolic- and diastolic blood pressure, ~65 common variants for type 2 diabetes and ~95 common variants for lipid traits. One major end organ damage is coronary heart disease and ~25 loci could be shown to be associated. Risk scores using multiple cardiovascular risk factor SNPs are clearly correlated with cardiovascular outcome. This review summarizes recent findings by genome-wide association studies and the contributions by the Framingham Heart Study on the basis of seminal articles and gives an outlook on some of the future experiments.

SUBMITTER: Ehret GB 

PROVIDER: S-EPMC3708670 | biostudies-other | 2013 Mar

REPOSITORIES: biostudies-other

Similar Datasets

| S-EPMC9896471 | biostudies-literature
| S-EPMC3292865 | biostudies-literature
| S-EPMC2748236 | biostudies-literature
| S-EPMC2789428 | biostudies-literature
| S-EPMC2453683 | biostudies-literature
| S-EPMC3042701 | biostudies-other
| S-EPMC4802453 | biostudies-literature
| S-EPMC8515503 | biostudies-literature
| S-EPMC2734141 | biostudies-literature
| S-EPMC3673738 | biostudies-other