International Effort Finds New Genetic Variants Associated With Lipid Levels, Risk For Coronary Artery Disease
Coronary Artery Disease
Environmental and genetic factors influence a person's blood fat, or lipid levels, important risk factors for coronary artery disease (CAD). While there is some understanding of the environmental contribution, the role of genetics has been less defined. Now, in an international collaboration supported primarily by the National Institutes of Health (NIH), scientists have discovered more than 25 genetic variants in 18 genes connected to cholesterol and lipid levels. Seven of the 18 genes previously had not been connected to these levels, while the 11 others confirm previous discoveries. In the investigation, published online January 13 and in the February print issue of Nature Genetics, the associated genes were found through studies of more than 20,000 individuals and more than 2 million genetic variants, spanning the entire genome. These variants potentially open the door to strategies for the treatment and prevention of CAD.
"Heart disease is a leading cause of illness, disability and death in industrialized countries, particularly for older people," says National Institute on Aging (NIA) Director Richard J. Hodes, M.D. "We know that certain lifestyle factors like smoking, diet and physical activity greatly affect a person's lipid profiles. This study is an important, basic step in finding the genes that influence lipid levels and heart disease so that we can better understand the genetic contribution to cardiovascular risk."
Cristen Willer, Ph.D., at the University of Michigan's School of Public Health, Ann Arbor, and Serena Sanna, Ph.D., at the C.N.R. Institute of Neurogenetics and Neuropharmacology, Monserrato, Italy, and other members of the SardiNIA Study of Aging, including investigators at NIA, conducted the study, along with members of the Finland-United States Investigation of Non-Insulin-Dependent Diabetes Mellitus Genetics (FUSION) study, which included investigators in North Carolina, Michigan, Finland, Los Angeles and from the National Human Genome Research Institute (NHGRI). SardiNIA and FUSION investigators also coordinated the efforts of other groups in France, the United Kingdom and across the United States.
The purpose of the study was to identify comprehensively genetic variants that influence lipid levels and to examine the relationships between these genetic variants and risk of CAD. High levels of low-density lipoprotein (LDL) ("bad" cholesterol) appear to increase the risk of CAD by narrowing or blocking arteries that carry blood to the heart. High levels of high-density lipoprotein (HDL) ("good" cholesterol) appear to lower the risk. High levels of triglycerides, which make up a large part of the body's fat and are also found in the bloodstream, are also associated with increased risk of CAD.
To identify genetic variants that play a role in lipid levels, researchers turned to a relatively new approach, known as a genome-wide association study (GWAS). The GWAS strategy enables researchers to survey the entire human genetic blueprint, or genome, not just the genetic variants in a few genes. The human genome contains approximately 3 billion base pairs, or letters, of DNA. Small, single-letter variations naturally occur about once in every 1,000 letters of the DNA code. Most of these genetic variants have not yet been associated with particular traits or disease risks. However, in some instances, people with a certain trait, such as higher levels of LDL cholesterol, tend to have one version of the variant, while those with lower levels are more likely to have the other version. In such instances, researchers may infer that there is an association between the values of the trait and the variants in the gene.
Typically, GWAS studies have been carried out in samples where all individuals are examined with the same gene chip, an experimental device that allows investigators to measure more than 100,000 genetic variants in a single experiment. But in this study, investigators developed and employed new statistical methods that allowed them to combine data across different gene chips and thus examine much larger numbers of participants.