Genomewide linkage scan for combined obesity phenotypes using principal component analysis

L. N. He, Y. J. Liu, P. Xiao, L. Zhang, Y. Guo, T. L. Yang, L. J. Zhao, B. Drees, J. Hamilton, H. Y. Deng, R. R. Recker, H. W. Deng

Research output: Contribution to journalArticle

15 Scopus citations

Abstract

Traditional whole genome linkage scans for obesity were usually performed for a number of correlated obesity related phenotypes separately without considering their correlations. The purpose of this study was to identify quantitative trait loci (QTLs) underlying variations in multiple correlated obesity phenotypes. We performed principal component analysis (PCA) for four highly correlated obesity phenotypes (body mass index [BMI], fat mass, percentage of fat mass [PFM], and lean mass) in a sample of 427 pedigrees (comprising 3,273 individuals) and generated two independent principal components (PC1 and PC2). A whole genome linkage scan (WGS) was then conducted for PC1 and PC2. For PC1, the strongest linkage signal was identified on chromosome 20p12 (LOD = 2.67). For PC2, two suggestive linkages were found on 5q35 (LOD = 2.03) and 7p22 (LOD = 2.18). This study provided evidence supporting several previously identified linkage regions for obesity (e.g., 1p36, 6p23 and 7q34). In addition, our approach by linear combination of highly correlated obesity phenotypes identified several novel QTLs which were not found in genome linkage scans for individual phenotypes.

Original languageEnglish (US)
Pages (from-to)319-326
Number of pages8
JournalAnnals of Human Genetics
Volume72
Issue number3
DOIs
StatePublished - May 2008

Keywords

  • Linkage
  • Obesity phenotypes
  • Principal component analysis
  • QTL

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)

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  • Cite this

    He, L. N., Liu, Y. J., Xiao, P., Zhang, L., Guo, Y., Yang, T. L., Zhao, L. J., Drees, B., Hamilton, J., Deng, H. Y., Recker, R. R., & Deng, H. W. (2008). Genomewide linkage scan for combined obesity phenotypes using principal component analysis. Annals of Human Genetics, 72(3), 319-326. https://doi.org/10.1111/j.1469-1809.2007.00423.x