Genetics and beyond--the transcriptome of human monocytes and disease susceptibility - PubMed (original) (raw)

. 2010 May 18;5(5):e10693.

doi: 10.1371/journal.pone.0010693.

Philipp Wild, Silke Szymczak, Maxime Rotival, Arne Schillert, Raphaele Castagne, Seraya Maouche, Marine Germain, Karl Lackner, Heidi Rossmann, Medea Eleftheriadis, Christoph R Sinning, Renate B Schnabel, Edith Lubos, Detlev Mennerich, Werner Rust, Claire Perret, Carole Proust, Viviane Nicaud, Joseph Loscalzo, Norbert Hübner, David Tregouet, Thomas Münzel, Andreas Ziegler, Laurence Tiret, Stefan Blankenberg, François Cambien

Affiliations

Genetics and beyond--the transcriptome of human monocytes and disease susceptibility

Tanja Zeller et al. PLoS One. 2010.

Abstract

Background: Variability of gene expression in human may link gene sequence variability and phenotypes; however, non-genetic variations, alone or in combination with genetics, may also influence expression traits and have a critical role in physiological and disease processes.

Methodology/principal findings: To get better insight into the overall variability of gene expression, we assessed the transcriptome of circulating monocytes, a key cell involved in immunity-related diseases and atherosclerosis, in 1,490 unrelated individuals and investigated its association with >675,000 SNPs and 10 common cardiovascular risk factors. Out of 12,808 expressed genes, 2,745 expression quantitative trait loci were detected (P<5.78x10(-12)), most of them (90%) being cis-modulated. Extensive analyses showed that associations identified by genome-wide association studies of lipids, body mass index or blood pressure were rarely compatible with a mediation by monocyte expression level at the locus. At a study-wide level (P<3.9x10(-7)), 1,662 expression traits (13.0%) were significantly associated with at least one risk factor. Genome-wide interaction analyses suggested that genetic variability and risk factors mostly acted additively on gene expression. Because of the structure of correlation among expression traits, the variability of risk factors could be characterized by a limited set of independent gene expressions which may have biological and clinical relevance. For example expression traits associated with cigarette smoking were more strongly associated with carotid atherosclerosis than smoking itself.

Conclusions/significance: This study demonstrates that the monocyte transcriptome is a potent integrator of genetic and non-genetic influences of relevance for disease pathophysiology and risk assessment.

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Conflict of interest statement

Competing Interests: In this study, Boehringer Ingelheim provided payment for two employees for expression microarray analyses and array purchase and PHILIPS Medical Systems provided instruments for ultrasound studies. However, this does not alter the authors' adherence to all the PLoS ONE policies on sharing data and materials, as detailed online in the guide for authors of the Journal.

Figures

Figure 1

Figure 1. Number of eQTLs according to the significance threshold adopted and corresponding cis/trans eQTL ratio.

The vertical arrow indicates the study-wise level of significance correcting for the number of hypotheses tested. Some eQTLs being associated with both cis and _trans_-acting eSNPs, the sum of cis and trans eQTLs is greater than the total number of eQTLs.

Figure 2

Figure 2. Comparison of the distributions of _P_-values of cis eQTLs reported as significant in three previous association studies with _P_-values observed in GHS for the same eQTLs.

For each of the 3 comparisons, we selected in GHS the subset of gene expressions claimed as significant in the study of comparison. Only autosomal genes were considered in these comparisons. The data used to generate this figure are provided in

Files S2–S4

. See also footnote of

Table S3

for details.

Figure 3

Figure 3. Comparison of the heritability of cis eQTLs estimated in the SAFHS study with the R2 of the corresponding cis eQTLs in GHS.

Data were extracted from Supplementary Table 4 in Göring et al. and comparisons were restricted to genes having a corresponding gene symbol in GHS. Heritability in the SAFHS was estimated by linkage analysis and accounts for the whole variability at a locus while R2 refers to a single eSNP (the best eSNP) and therefore underestimates the global variability affecting gene expression at a locus. The data used to generate this figure are provided in

File S5

. The median R2 was globally lower than the heritability, consistent with the fact that the R2 is referring to a single SNP whereas heritability reflects the whole genetic variation at a locus.

Figure 4

Figure 4. The loci affecting CDKN2B expression and CAD on chromosome 9p21 are independent.

The lead SNP rs1333049 generally reported at the CAD locus was not present on the Affymetrix 6.0 array, we therefore selected its best proxy, rs10757272 (position 22078260, r2 = 0.9 with rs1333049), using SNAP (

https://www.broadinstitute.org/mpg/snap

). Positions of genotyped SNPs are shown using a green link and position of the proxy SNP, rs10757272, is represented by a green triangle. The red curve reflect the –log10(_P_-value) for the association between SNPs and CDKN2B expression. The LD (r2) between pairs of SNPs is shown at the bottom of the figure using a range of colors between white (r2 = 0) and black (r2 = 1). The CDKN2B and CAD-associated SNPs are located in different blocks of LD strongly suggesting that the genetic effects on CDKN2B expression and CAD are independent.

Figure 5

Figure 5. Effect of the best cis eSNP and smoking on expression of smoking-related eQTLs.

The proportion of variability of expression explained by the best cis eSNP varied from 3.1% for CLEC10A to 27.2% for GFRA2 while the proportion explained by smoking varied from 2.8% for SMAD6 to 21.6% for SASH1. The lowest _P_-value for interaction between SNP and smoking was 0.02 for STAB1.

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