Coexpression Network Analysis in Abdominal and Gluteal Adipose Tissue Reveals Regulatory Genetic Loci for Metabolic Syndrome and Related Phenotypes

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

doi:10.1371/journal.pgen.1002505

Abstract

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 (DABD-GLU = 0.89), seven of which were associated with MetS (FDR P<0.01). The strongest associated module, significantly enriched for immune response–related processes, contained 94/620 (15%) genes with inter-depot differences. In an independent cohort of 145/141 twins with ABD and WB longitudinal expression data, median variability in ABD due to familiality was greater for MetS–associated versus un-associated modules (ABD: 0.48 versus 0.18, P = 0.08; GLU: 0.54 versus 0.20, P = 7.8×10−4). Cis-eQTL analysis of probesets associated with MetS (FDR P<0.01) and/or inter-depot differences (FDR P<0.01) provided evidence for 32 eQTLs. Corresponding eSNPs were tested for association with MetS–related phenotypes in two GWAS of >100,000 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.

Highlights

Genome-wide association (GWA) studies are routinely employed to identify common genetic variants contributing to complex diseases
Given that most cellular components exert their functions through interactions with other cellular components, even the largest of genomewide association (GWA) studies may often not detect their effects, nor necessarily provide insight into the complex molecular mechanisms of the disease
We identified modules of genes related to Metabolic Syndrome (MetS) significantly enriched for immune response and oxidative phosphorylation pathways

Summary

Introduction

Genome-wide association (GWA) studies are routinely employed to identify common genetic variants contributing to complex diseases. Statistically significant signals resulting from GWA studies do not necessarily lead directly to the identification of genes associated with disease or provide limited insights into the molecular mechanisms of the disease phenotype. The associated SNPs explain a very small proportion of the heritability estimated for the complex trait [7]. A GWA study for BMI in up to ,250,000 individuals identified 32 loci that, together, explain only 4.5% of the phenotypic variation or ,6–11% of the genetic variation in BMI [3]. The 1000 Genomes Project Consortium tested ,95% of common variation

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