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Abstract
Understanding the difference in genetic regulation of gene expression between brain and blood is important for discovering genes for brain-related traits and disorders. Here, we estimate the correlation of genetic effects at the top-associated cis-expression or -DNA methylation (DNAm) quantitative trait loci (cis-eQTLs or cis-mQTLs) between brain and blood (rb). Using publicly available data, we find that genetic effects at the top cis-eQTLs or mQTLs are highly correlated between independent brain and blood samples (\[\hat r_b = 0.70\] for cis-eQTLs and \[\hat r_ b = 0.78\] for cis-mQTLs). Using meta-analyzed brain cis-eQTL/mQTL data (n = 526 to 1194), we identify 61 genes and 167 DNAm sites associated with four brain-related phenotypes, most of which are a subset of the discoveries (97 genes and 295 DNAm sites) using data from blood with larger sample sizes (n = 1980 to 14,115). Our results demonstrate the gain of power in gene discovery for brain-related phenotypes using blood cis-eQTL/mQTL data with large sample sizes.
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1 Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
2 Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia; Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
3 Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia; The Eye Hospital, School of Ophthalmology & Optometry, Wenzhou Medical University, Wenzhou, Zhejiang, China
4 Computational Biology, Ontario Institute for Cancer Research, Toronto, ON, Canada
5 Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
6 Singapore Immunology Network, Agency for Science, Technology and Research, Singapore, Singapore
7 Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
8 Heart Center Leipzig, Universität Leipzig, Leipzig, Germany
9 Department of Genetics, University Medical Centre Groningen, Groningen, The Netherlands
10 Faculty of Genes, Behavior and Health, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
11 Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
12 Estonian Genome Center, University of Tartu, Tartu, Estonia
13 Exeter Medical School, University of Exeter, Exeter, UK
14 Department of Medicine, University of Washington, Seattle, WA, USA
15 School of Biological Sciences, Georgia Tech, Atlanta, GA, USA
16 MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
17 Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland; Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland
18 National Institute for Health and Welfare, University of Helsinki, Helsinki, Finland
19 Institute für Medizinische InformatiK, Statistik und Epidemiologie, LIFE–Leipzig Research Center for Civilization Diseases, Universität Leipzig, Leipzig, Germany
20 IFB Adiposity Diseases, Department of Medicine, Universität Leipzig, Leipzig, Germany
21 Interdisciplinary Center for Clinical Research, Faculty of Medicine, Universität Leipzig, Leipzig, Germany
22 Lausanne University Hospital, Lausanne, Switzerland
23 Department of Clinical Chemistry, Fimlab Laboratories and Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland
24 Institut für Medizinische InformatiK, Statistik und Epidemiologie, LIFE–Leipzig Research Center for Civilization Diseases, Universität Leipzig, Leipzig, Germany
25 Institute of Genetic Epidemiology, Helmholtz Zentrum München, München, Germany
26 Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
27 Institute of Human Genetics, Helmholtz Zentrum München, München, Germany
28 Cardiovascular Health Research Unit, Departments of Epidemiology, Medicine, and Health Services, University of Washington, Seattle, WA, USA
29 Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland; University of Turku, Turku, Finland
30 School of Social and Community Medicine, University of Bristol, Bristol, UK
31 Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Solna, Sweden
32 Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
33 Institute for Laboratory Medicine, LIFE–Leipzig Research Center for Civilization Diseases, Universität Leipzig, Leipzig, Germany
34 Division of Endocrinology and Nephrology, Department of Medicine, Universität Leipzig, Leipzig, Germany
35 Department of Internal Medicine, Erasmus Medical Centre, Rotterdam, The Netherlands
36 Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
37 Medical Genetics Section, Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK; Department of Psychology, Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
38 Department of Psychology, Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
39 Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia; Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia; The Eye Hospital, School of Ophthalmology & Optometry, Wenzhou Medical University, Wenzhou, Zhejiang, China