Genome-wide linkage scan for the metabolic syndrome in the HERITAGE Family Study

J Clin Endocrinol Metab. 2003 Dec;88(12):5935-43. doi: 10.1210/jc.2003-030553.

Abstract

The metabolic syndrome involves multiple and interactive effects of genes and environmental factors. To identify chromosomal regions encoding genes possibly predisposing to the metabolic syndrome, we performed a genome-wide scan with 456 white and 217 black participants from 204 nuclear families of the HERITAGE Family Study, using regression-based, single- and multipoint linkage analyses on 509 markers. A principal component analysis was performed on 7 metabolic syndrome-related phenotypes. Two principal components, PC1 and PC2 (55% of the variance), were used as metabolic syndrome phenotypes. ANOVA was used to quantify the familial aggregation of PC1 and PC2. Family membership contributed significantly (P < 0.0023) to the variance in PC1 (r(2) = 0.38 in whites; r(2) = 0.55 in blacks) and PC2 (r(2) = 0.51; r(2) = 0.48). In whites, promising evidence for linkage (P < 0.0023) was found for PC1 (2 markers on 10p11.2) and PC2 (a marker on 19q13.4). Suggestive evidence of linkage (0.01 > P > 0.0023) appeared for PC1 (1q41 and 9p13.1) and PC2 (2p22.3). In blacks, promising linkage was found for PC2 on 1p34.1, and suggestive linkage was found on 7q31.3 and 9q21.1. The genome-wide scan revealed evidence for quantitative trait loci on chromosomal regions that have been previously linked with individual cardiovascular disease and type 2 diabetes risk factors. Some of these chromosomal regions harbor promising potential candidate genes.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Adolescent
  • Adult
  • Analysis of Variance
  • Black People / genetics
  • Chromosome Mapping
  • Female
  • Genetic Linkage
  • Genome, Human
  • Humans
  • Male
  • Metabolic Syndrome / genetics*
  • Middle Aged
  • Phenotype
  • Principal Component Analysis
  • Quantitative Trait Loci
  • White People / genetics