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Analysis of Metabolic Syndrome Phenotypes in Framingham Heart Study Families from Genetic Analysis Workshop 13

  • Lynn R. Goldin
  • , Nicola J. Camp
  • , Kevin J. Keen
  • , Lisa J. Martin
  • , Roxana Moslehi
  • , Saurabh Ghosh
  • , Kari E. North
  • , Diego F. Wyszynski
  • , Deborah Blacker
  • National Institutes of Health
  • University of Utah
  • Case Western Reserve University
  • Cincinnati Children's Hospital Medical Center
  • Washington University St. Louis
  • Indian Statistical Institute
  • University of North Carolina at Chapel Hill
  • Boston University
  • Massachusetts General Hospital

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

Twelve teams of investigators constituted a group which analyzed phenotypes related to metabolic syndrome, making use of the available longitudinal measurements from the family component of the Framingham Heart Study or the simulated data, as distributed by Genetic Analysis Workshop 13 (GAW13). Body mass index, obesity, lipid abnormalities, glucose, or combinations of these traits were analyzed by this group. A wide variety of approaches were taken to construct phenotypes from the longitudinal measurements, including considering single or multiple cross-sectional time points, single ages, minimum values, maximum values, means, other lifetime values, ever/never dichotomy, or age at onset of some threshold value. Approaches also differed in the family structures utilized (sib pairs to full extended pedigrees), the genetic data considered (two-point or multipoint), and the statistics calculated (model-free and parametric), and led to a diverse set of analyses being performed. Inferences were made about heritability, and attempts were made to map underlying genes. Over 40 genome-wide linkage analyses were conducted. Despite the broad range of approaches, several regions of the genome were repeatedly identified across multiple analyses.

Original languageEnglish
Pages (from-to)S78-S89
JournalGenetic Epidemiology
Volume25
Issue numberSUPPL. 1
DOIs
StatePublished - 2003

Keywords

  • Linkage methods
  • Longitudinal measures
  • Metabolic syndrome
  • Susceptibility genes

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