Literature DB >> 14578304

Linkage analysis of a composite factor for the multiple metabolic syndrome: the National Heart, Lung, and Blood Institute Family Heart Study.

Weihong Tang1, Michael B Miller, Stephen S Rich, Kari E North, James S Pankow, Ingrid B Borecki, Richard H Myers, Paul N Hopkins, Mark Leppert, Donna K Arnett.   

Abstract

Recent studies have demonstrated significant genetic and phenotypic correlation underlying the clustering of traits involved in the multiple metabolic syndrome (MMS). The aim of this study was to identify chromosomal regions contributing to MMS-related traits represented by composite factors derived from factor analysis. Data from the National Heart, Lung, and Blood Institute (NHLBI) Family Heart Study were subjected to a maximum likelihood-based factor analysis. These analyses generated an MMS factor that was loaded by BMI, waist-to-hip ratio, subscapular skinfold, triglycerides, HDL, homeostasis model assessment index, plasminogen activator inhibitor-1 antigen, and serum uric acid. Genetic data were obtained for 2,467 subjects from 387 three-generation families (402 markers, the NHLBI Mammalian Genotyping Service) and 1,082 subjects from 256 sibships (243 markers, the Utah Molecular Genetics Laboratory). Multipoint variance components linkage analysis (GENEHUNTER version 2.1) of the MMS factor was conducted in the combined marker set sample. The greatest evidence for linkage was found on chromosome 2, with a peak LOD of 3.34 at 240 cM. Suggestive linkage was also observed for regions on chromosomes 7, 12, 14, and 15. In summary, a genomic region on chromosome 2 may contain a pleiotropic locus contributing to the clustering of MMS-related phenotypes.

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Year:  2003        PMID: 14578304     DOI: 10.2337/diabetes.52.11.2840

Source DB:  PubMed          Journal:  Diabetes        ISSN: 0012-1797            Impact factor:   9.461


  32 in total

1.  Serum uric acid concentrations and SLC2A9 genetic variation in Hispanic children: the Viva La Familia Study.

Authors:  V Saroja Voruganti; Sandra Laston; Karin Haack; Nitesh R Mehta; Shelley A Cole; Nancy F Butte; Anthony G Comuzzie
Journal:  Am J Clin Nutr       Date:  2015-01-28       Impact factor: 7.045

2.  Practical way to assess metabolic syndrome using a continuous score obtained from principal components analysis.

Authors:  T A Hillier; A Rousseau; C Lange; P Lépinay; M Cailleau; M Novak; E Calliez; P Ducimetière; B Balkau
Journal:  Diabetologia       Date:  2006-05-16       Impact factor: 10.122

3.  Quantitative founder-effect analysis of French Canadian families identifies specific loci contributing to metabolic phenotypes of hypertension.

Authors:  P Hamet; E Merlo; O Seda; U Broeckel; J Tremblay; M Kaldunski; D Gaudet; G Bouchard; B Deslauriers; F Gagnon; G Antoniol; Z Pausová; M Labuda; M Jomphe; F Gossard; G Tremblay; R Kirova; P Tonellato; S N Orlov; J Pintos; J Platko; T J Hudson; J D Rioux; T A Kotchen; A W Cowley
Journal:  Am J Hum Genet       Date:  2005-03-30       Impact factor: 11.025

4.  Are there common genetic and environmental factors behind the endophenotypes associated with the metabolic syndrome?

Authors:  B Benyamin; T I A Sørensen; K Schousboe; M Fenger; P M Visscher; K O Kyvik
Journal:  Diabetologia       Date:  2007-07-12       Impact factor: 10.122

5.  A prevalent caveolin-1 gene variant is associated with the metabolic syndrome in Caucasians and Hispanics.

Authors:  Rene Baudrand; Mark O Goodarzi; Anand Vaidya; Patricia C Underwood; Jonathan S Williams; Xavier Jeunemaitre; Paul N Hopkins; Nancy Brown; Benjamin A Raby; Jessica Lasky-Su; Gail K Adler; Jinrui Cui; Xiuqing Guo; Kent D Taylor; Yii-Der I Chen; Anny Xiang; Leslie J Raffel; Thomas A Buchanan; Jerome I Rotter; Gordon H Williams; Luminita H Pojoga
Journal:  Metabolism       Date:  2015-09-12       Impact factor: 8.694

Review 6.  Genetics of metabolic syndrome.

Authors:  Alena Stančáková; Markku Laakso
Journal:  Rev Endocr Metab Disord       Date:  2014-12       Impact factor: 6.514

7.  Genome-wide linkage analysis of multiple metabolic factors: evidence of genetic heterogeneity.

Authors:  Ching-Yu Cheng; Kristine E Lee; Priya Duggal; Emily L Moore; Alexander F Wilson; Ronald Klein; Joan E Bailey-Wilson; Barbara E K Klein
Journal:  Obesity (Silver Spring)       Date:  2009-05-14       Impact factor: 5.002

8.  Genome-wide linkage scan for factors of metabolic syndrome in a Chinese population.

Authors:  Claudia H T Tam; Vincent K L Lam; Wing-Yee So; Ronald C W Ma; Juliana C N Chan; Maggie C Y Ng
Journal:  BMC Genet       Date:  2010-02-24       Impact factor: 2.797

9.  A genomewide scan for early-onset coronary artery disease in 438 families: the GENECARD Study.

Authors:  Elizabeth R Hauser; David C Crossman; Christopher B Granger; Jonathan L Haines; Christopher J H Jones; Vincent Mooser; Brendan McAdam; Bernhard R Winkelmann; Alan H Wiseman; J Brent Muhlestein; Alan G Bartel; Charles A Dennis; Elaine Dowdy; Susan Estabrooks; Karen Eggleston; Sheila Francis; Kath Roche; Paula W Clevenger; Liling Huang; Bonnie Pedersen; Svati Shah; Silke Schmidt; Carol Haynes; Sandra West; Donny Asper; Michael Booze; Sanjay Sharma; Scott Sundseth; Lefkos Middleton; Allen D Roses; Michael A Hauser; Jeffery M Vance; Margaret A Pericak-Vance; William E Kraus
Journal:  Am J Hum Genet       Date:  2004-07-22       Impact factor: 11.025

10.  Multivariate association analysis of the components of metabolic syndrome from the Framingham Heart Study.

Authors:  Allison R Baker; Robert J Goodloe; Emma K Larkin; Dan J Baechle; Yeunjoo E Song; Lynette S Phillips; Courtney L Gray-McGuire
Journal:  BMC Proc       Date:  2009-12-15
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