PLoS Genetics (Jan 2012)

Coexpression network analysis in abdominal and gluteal adipose tissue reveals regulatory genetic loci for metabolic syndrome and related phenotypes.

  • Josine L Min,
  • George Nicholson,
  • Ingileif Halgrimsdottir,
  • Kristian Almstrup,
  • Andreas Petri,
  • Amy Barrett,
  • Mary Travers,
  • Nigel W Rayner,
  • Reedik Mägi,
  • Fredrik H Pettersson,
  • John Broxholme,
  • Matt J Neville,
  • Quin F Wills,
  • Jane Cheeseman,
  • GIANT Consortium,
  • MolPAGE Consortium,
  • Maxine Allen,
  • Chris C Holmes,
  • Tim D Spector,
  • Jan Fleckner,
  • Mark I McCarthy,
  • Fredrik Karpe,
  • Cecilia M Lindgren,
  • Krina T Zondervan

DOI
https://doi.org/10.1371/journal.pgen.1002505
Journal volume & issue
Vol. 8, no. 2
p. e1002505

Abstract

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Metabolic Syndrome (MetS) is highly prevalent and has considerable public health impact, but its underlying genetic factors remain elusive. To identify gene networks involved in MetS, we conducted whole-genome expression and genotype profiling on abdominal (ABD) and gluteal (GLU) adipose tissue, and whole blood (WB), from 29 MetS cases and 44 controls. Co-expression network analysis for each tissue independently identified nine, six, and zero MetS-associated modules of coexpressed genes in ABD, GLU, and WB, respectively. Of 8,992 probesets expressed in ABD or GLU, 685 (7.6%) were expressed in ABD and 51 (0.6%) in GLU only. Differential eigengene network analysis of 8,256 shared probesets detected 22 shared modules with high preservation across adipose depots (D(ABD-GLU) = 0.89), seven of which were associated with MetS (FDR P100,000 individuals; rs10282458, affecting expression of RARRES2 (encoding chemerin), was associated with body mass index (BMI) (P = 6.0×10(-4)); and rs2395185, affecting inter-depot differences of HLA-DRB1 expression, was associated with high-density lipoprotein (P = 8.7×10(-4)) and BMI-adjusted waist-to-hip ratio (P = 2.4×10(-4)). Since many genes and their interactions influence complex traits such as MetS, integrated analysis of genotypes and coexpression networks across multiple tissues relevant to clinical traits is an efficient strategy to identify novel associations.