Genome-wide linkage scans for loci affecting total cholesterol, HDL-C, and triglycerides: The family blood pressure program

Suzette J. Bielinski, Weihong Tang, James S. Pankow, Michael B. Miller, Thomas H. Mosley, Eric Boerwinkle, Richard A. Olshen, J. David Curb, Cashell E. Jaquish, D. C. Rao, Alan Weder, Donna K. Arnett

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

Atherosclerosis accounts for 75% of all deaths from cardiovascular disease and includes coronary heart disease (CHD), stroke, and other diseases of the arteries. More than half of all CHD is attributable to abnormalities in levels and metabolism of lipids. To locate genes that affect total cholesterol, high density lipoprotein cholesterol (HDL-C), and triglycerides, genome-wide linkage scans for quantitative trait loci were performed using variance components methods as implemented in SOLAR on a large diverse sample recruited as part of the Family Blood Pressure Program. Phenotype and genetic marker data were available for 9,299 subjects in 2,953 families for total cholesterol, 8,668 subjects in 2,736 families for HDL, and 7,760 subjects in 2,499 families for triglycerides. Mean lipid levels were adjusted for the effects of sex, age, age2, age-by-sex interaction, body mass index, smoking status, and field center. HDL-C and triglycerides were further adjusted for average total alcoholic drinks per week and estrogen use. Significant linkage was found for total cholesterol on chromosome 2 (LOD = 3.1 at 43 cM) in Hispanics and for HDL-C on chromosome 3 (LOD = 3.0 at 182 cM) and 12 (LOD = 3.5 at 124 cM) in Asians. In addition, there were 13 regions that showed suggestive linkage (LOD ≥ 2.0); 7 for total cholesterol, 4 for HDL, and 2 for triglycerides. The identification of these loci affecting lipid phenotypes and the apparent congruence with previous linkage results provides increased support that these regions contain genes influencing lipid levels.

Original languageEnglish (US)
Pages (from-to)371-380
Number of pages10
JournalHuman Genetics
Volume120
Issue number3
DOIs
StatePublished - Oct 2006

Bibliographical note

Funding Information:
Acknowledgments We are grateful for resources from the University of Minnesota Supercomputing Institute, the NIH Training Grant in Cardiovascular Disease Genetic Epidemiology (#5 T32 HL007972), and the National Heart Lung and Blood Institute Family Blood Pressure Program including networks GenNet (U01 HL54512, U01 HL54508, U01 HL54485, U01 HL54466, U01 HL64777), GENOA (U01 HL54481, U01 HL54504, U01 HL54463, U01 HL54526, U01 HL54457, U01 HL54464), Hyper-GEN (U01 HL54471, U01 HL54472, U01 HL54495, U01 HL54497, U01 HL54509, U01 HL54496, U01 HL54473), and SAPPHIRe (U01 HL54527, U01 HL54498). http://www.biostat. wustl.edu/fbpp/Acknowledgments.html

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