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

Mapping the genetic architecture of gene regulation in whole blood.

Author information

1
Institute of Human Genetics, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany; Institute of Human Genetics, Technical University Munich, München, Germany.
2
Research Unit of Molecular Epidemiology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany; Institute of Epidemiology II, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany; German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany.
3
Interfaculty Institute for Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Greifswald, Germany.
4
Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany; German Center for Diabetes Research (DZD e.V.), partner site Düsseldorf, Germany.
5
Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia; Estonian Genome Center, University of Tartu, Tartu, Estonia.
6
Department of Prosthetic Dentistry, Gerostomatology and Dental Materials, University Medicine Greifswald, Greifswald, Germany.
7
Institute of Human Genetics, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany.
8
Institute of Genetic Epidemiology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany.
9
Department of Psychiatry and Psychotherapy, Helios Hospital Stralsund, University Medicine of Greifswald, Greifswald, Germany.
10
Institute of Bioinformatics and Systems Biology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany.
11
Estonian Genome Center, University of Tartu, Tartu, Estonia.
12
Estonian Genome Center, University of Tartu, Tartu, Estonia; Estonian Biocentre, Tartu, Estonia.
13
Institute of Human Genetics, Technical University Munich, München, Germany.
14
Institute of Clinical Chemistry and Laboratory Medicine, Greifswald, Germany.
15
Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany; German Center for Diabetes Research (DZD e.V.), partner site Düsseldorf, Germany; Division of Endocrinology and Diabetology, University Hospital Düsseldorf, Düsseldorf, Germany.
16
Institute of Genetic Epidemiology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany; Institute of Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-Universität Munich, Neuherberg, Germany.
17
Institute of Bioinformatics and Systems Biology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany; Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Education City, Qatar Foundation, Doha, Qatar.
18
Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany.
19
Research Unit of Molecular Epidemiology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany; Institute of Epidemiology II, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany.
20
Institute of Human Genetics, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany; Institute of Human Genetics, Technical University Munich, München, Germany; Munich Heart Alliance, München, Germany.
21
Hannover Unified Biobank, Hannover Medical School, Hannover, Germany.

Abstract

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.

PMID:
24740359
PMCID:
PMC3989189
DOI:
10.1371/journal.pone.0093844
[Indexed for MEDLINE]
Free PMC Article
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