Mapping the genetic architecture of gene regulation in whole blood

PLoS One. 2014 Apr 16;9(4):e93844. doi: 10.1371/journal.pone.0093844. eCollection 2014.


Background: We aimed to assess whether whole blood expression quantitative trait loci (eQTLs) with effects in cis and trans are robust and can be used to identify regulatory pathways affecting disease susceptibility.

Materials and methods: We performed whole-genome eQTL analyses in 890 participants of the KORA F4 study and in two independent replication samples (SHIP-TREND, N = 976 and EGCUT, N = 842) using linear regression models and Bonferroni correction.

Results: In the KORA F4 study, 4,116 cis-eQTLs (defined as SNP-probe pairs where the SNP is located within a 500 kb window around the transcription unit) and 94 trans-eQTLs reached genome-wide significance and overall 91% (92% of cis-, 84% of trans-eQTLs) were confirmed in at least one of the two replication studies. Different study designs including distinct laboratory reagents (PAXgene™ vs. Tempus™ tubes) did not affect reproducibility (separate overall replication overlap: 78% and 82%). Immune response pathways were enriched in cis- and trans-eQTLs and significant cis-eQTLs were partly coexistent in other tissues (cross-tissue similarity 40-70%). Furthermore, four chromosomal regions displayed simultaneous impact on multiple gene expression levels in trans, and 746 eQTL-SNPs have been previously reported to have clinical relevance. We demonstrated cross-associations between eQTL-SNPs, gene expression levels in trans, and clinical phenotypes as well as a link between eQTLs and human metabolic traits via modification of gene regulation in cis.

Conclusions: Our data suggest that whole blood is a robust tissue for eQTL analysis and may be used both for biomarker studies and to enhance our understanding of molecular mechanisms underlying gene-disease associations.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Blood / metabolism*
  • Chromosome Mapping
  • Gene Expression Regulation*
  • Gene Regulatory Networks
  • Genetic Association Studies
  • Genetic Markers
  • Humans
  • Linear Models
  • Polymorphism, Single Nucleotide
  • Quantitative Trait Loci


  • Genetic Markers

Grant support

The KORA research platform and the KORA Augsburg studies are financed by the Helmholtz Zentrum München, German Research Center for Environmental Health, which is funded by the BMBF and by the State of Bavaria. Furthermore, KORA research was supported within the Munich Center of Health Sciences (MC Health), Ludwig Maximilians-Universität, as part of the LMUinnovative and in part by a grant from the BMBF to the German Center for Diabetes Research (DZD). The German Diabetes Center is funded by the German Federal Ministry of Health and the Ministry of School, Science and Research of the State of North-Rhine-Westphalia. This study was supported by the BMBF funded Systems Biology of Metabotypes grant (SysMBo#0315494A). Additional support was obtained from the BMBF (National Genome Research Network NGFNplus Atherogenomics, 01GS0834) and from the European Commission's Seventh Framework Programme (FP7/2007-2013, HEALTH-F2-2011, grant agreement No. 277984, TIRCON). K. Suhre is supported by ‘Biomedical Research Program’ funds at Weill Cornell Medical College in Qatar, a program funded by the Qatar Foundation. SHIP-TREND is part of the Community Medicine Research net of the University of Greifswald, Germany, which is funded by the BMBF (German Ministry of Education and Research), the Ministry of Cultural Affairs as well as the Social Ministry of the Federal State of Mecklenburg-West Pomerania. Analyses were supported by the ‘Greifswald Approach to Individualized Medicine (GANI_MED)’ consortium funded by the BMBF (grant 03IS2061A). Genome-wide genotyping and expression data have been supported by the BMBF (grant no. 03ZIK012) and the Federal State of Mecklenburg, West Pomerania. The University of Greifswald is a member of the ‘Center of Knowledge Interchange’ program of the Siemens AG and the Caché Campus program of the InterSystems GmbH. EGCUT studies were financed by University of Tartu (grant “Center of Translational Genomics”), by Estonian Goverment (grant #SF0180142s08) and by European Commission through the European Regional Development Fund in the frame of grant “Centre of Excellence in Genomics” and Estonian Research Infrastructure's Roadmap and through FP7 grant #313010. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.