Published online before print August 28, 2003, doi: 10.1161/01.ATV.0000093281.10213.DB
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© 2003 American Heart Association, Inc.
Atherosclerosis and Lipoproteins |
Meta-Analysis of 4 Coronary Heart Disease Genome-Wide Linkage Studies Confirms a Susceptibility Locus on Chromosome 3q
From the Division of Genetics and Development, Guy’s, King’s, and St. Thomas’ School of Medicine, London, England.
Correspondence to Dr B.D. Chiodini, Division of Genetics and Development, Guy’s, King’s, and St. Thomas’ School of Medicine, 8th Floor, Guy’s Tower, Guy’s Hospital, London SE1 9RT, UK, E-mail benedetta.chiodini{at}kcl.ac.uk
Objective— In coronary heart disease (CHD), 4 independent, genome-wide screens have now been published on Finnish, Mauritian, European, and Australian families. Results from these studies are inconclusive. We performed a meta-analysis to identify genetic regions that show evidence for susceptibility genes across studies.
Methods and Results— The rank-based genome-scan meta-analysis (GSMA) method was applied to the 4 CHD genome-wide linkage studies. The strongest evidence for linkage was found on chromosomes 3q26–27 (P=0.0001) and 2q34–37 (P=0.009). Analysis weighted by study size confirmed linkage in these regions (3q26–27, P=0.0002; 2q34–37, P=0.014).
Conclusions— The genetic regions 3q26–27 and 2q34–37 might contain susceptibility genes for CHD. Linkage to the 3q26-qter region has previously been shown in type 2 diabetes mellitus, metabolic syndrome, cholesterol concentration in LDL size fractions, and renal function in hypertensive subjects. The 2q34–37 region lies close to the type 2 diabetes NIDDM1 locus. Both of these regions harbor several candidate genes involved in the homeostasis of glucose and lipid metabolism. These results are particularly intriguing, given the growing evidence of an association between CHD risk and metabolic abnormalities, such as insulin resistance, type 2 diabetes, abdominal obesity, and dyslipidemia.
Key Words: meta-analysis • coronary heart disease • genome scan • linkage analysis • genetics
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