Multi-tissue transcriptome analyses identify genetic mechanisms underlying neuropsychiatric traits
The genetic architecture of psychiatric disorders is characterized by a large number of small-effect variants 1 located primarily in non-coding regions, suggesting that the underlying causal effects may influence disease risk by modulating gene expression 2 – 4 . We provide comprehensive analyses us...
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| Published in | Nature genetics Vol. 51; no. 6; pp. 933 - 940 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Nature Publishing Group US
01.06.2019
Nature Publishing Group |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1061-4036 1546-1718 1546-1718 |
| DOI | 10.1038/s41588-019-0409-8 |
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| Abstract | The genetic architecture of psychiatric disorders is characterized by a large number of small-effect variants
1
located primarily in non-coding regions, suggesting that the underlying causal effects may influence disease risk by modulating gene expression
2
–
4
. We provide comprehensive analyses using transcriptome data from an unprecedented collection of tissues to gain pathophysiological insights into the role of the brain, neuroendocrine factors (adrenal gland) and gastrointestinal systems (colon) in psychiatric disorders. In each tissue, we perform PrediXcan analysis and identify trait-associated genes for schizophrenia (
n
associations = 499;
n
unique genes = 275), bipolar disorder (
n
associations = 17;
n
unique genes = 13), attention deficit hyperactivity disorder (
n
associations = 19;
n
unique genes = 12) and broad depression (
n
associations = 41;
n
unique genes = 31). Importantly, both PrediXcan and summary-data-based Mendelian randomization/heterogeneity in dependent instruments analyses suggest potentially causal genes in non-brain tissues, showing the utility of these tissues for mapping psychiatric disease genetic predisposition. Our analyses further highlight the importance of joint tissue approaches as 76% of the genes were detected only in difficult-to-acquire tissues.
Multi-tissue transcriptome analyses using PrediXcan identify numerous trait-associated genes for schizophrenia, bipolar disorder, attention deficit hyperactivity disorder and broad depression, and highlight potentially causal genes in non-brain tissues. |
|---|---|
| AbstractList | The genetic architecture of psychiatric disorders is characterized by a large number of small-effect variants
1
located primarily in non-coding regions, suggesting that the underlying causal effects may influence disease risk by modulating gene expression
2
–
4
. We provide comprehensive analyses using transcriptome data from an unprecedented collection of tissues to gain pathophysiological insights into the role of the brain, neuroendocrine factors (adrenal gland) and gastrointestinal systems (colon) in psychiatric disorders. In each tissue, we perform PrediXcan analysis and identify trait-associated genes for schizophrenia (
n
associations = 499;
n
unique genes = 275), bipolar disorder (
n
associations = 17;
n
unique genes = 13), attention deficit hyperactivity disorder (
n
associations = 19;
n
unique genes = 12) and broad depression (
n
associations = 41;
n
unique genes = 31). Importantly, both PrediXcan and summary-data-based Mendelian randomization/heterogeneity in dependent instruments analyses suggest potentially causal genes in non-brain tissues, showing the utility of these tissues for mapping psychiatric disease genetic predisposition. Our analyses further highlight the importance of joint tissue approaches as 76% of the genes were detected only in difficult-to-acquire tissues.
Multi-tissue transcriptome analyses using PrediXcan identify numerous trait-associated genes for schizophrenia, bipolar disorder, attention deficit hyperactivity disorder and broad depression, and highlight potentially causal genes in non-brain tissues. The genetic architecture of psychiatric disorders is characterized by a large number of small-effect variants1 located primarily in non-coding regions, suggesting that the underlying causal effects may influence disease risk by modulating gene expression2–4. We provide comprehensive analyses using transcriptome data from an unprecedented collection of tissues to gain pathophysiological insights into the role of the brain, neuroendocrine factors (adrenal gland) and gastrointestinal systems (colon) in psychiatric disorders. In each tissue, we perform PrediXcan analysis and identify trait-associated genes for schizophrenia (n-associations = 499; n-unique genes = 275), bipolar disorder (n-associations = 17; n-unique genes = 13), attention deficit hyperactivity disorder (n-associations = 19; n-unique genes = 12), and broad depression (n-associations = 41; n-unique genes = 31). Importantly, both PrediXcan and Summary-data-based Mendelian Randomization/HEIDI analyses propose potentially causal genes in non-brain tissues, showing the utility of these tissues for mapping psychiatric disease genetic predisposition. Our analyses further highlight the importance of joint-tissue approaches as 76% of the genes were detected only in difficult-to-acquire tissues. Multi-tissue transcriptome analyses using PrediXcan identify numerous trait-associated genes for schizophrenia, bipolar disorder, attention deficit hyperactivity disorder, and broad depression, and highlight potentially causal genes in non-brain tissues. The genetic architecture of psychiatric disorders is characterized by a large number of small-effect variants1 located primarily in non-coding regions, suggesting that the underlying causal effects may influence disease risk by modulating gene expression2-4. We provide comprehensive analyses using transcriptome data from an unprecedented collection of tissues to gain pathophysiological insights into the role of the brain, neuroendocrine factors (adrenal gland) and gastrointestinal systems (colon) in psychiatric disorders. In each tissue, we perform PrediXcan analysis and identify trait-associated genes for schizophrenia (n associations = 499; n unique genes = 275), bipolar disorder (n associations = 17; n unique genes = 13), attention deficit hyperactivity disorder (n associations = 19; n unique genes = 12) and broad depression (n associations = 41; n unique genes = 31). Importantly, both PrediXcan and summary-data-based Mendelian randomization/heterogeneity in dependent instruments analyses suggest potentially causal genes in non-brain tissues, showing the utility of these tissues for mapping psychiatric disease genetic predisposition. Our analyses further highlight the importance of joint tissue approaches as 76% of the genes were detected only in difficult-to-acquire tissues.The genetic architecture of psychiatric disorders is characterized by a large number of small-effect variants1 located primarily in non-coding regions, suggesting that the underlying causal effects may influence disease risk by modulating gene expression2-4. We provide comprehensive analyses using transcriptome data from an unprecedented collection of tissues to gain pathophysiological insights into the role of the brain, neuroendocrine factors (adrenal gland) and gastrointestinal systems (colon) in psychiatric disorders. In each tissue, we perform PrediXcan analysis and identify trait-associated genes for schizophrenia (n associations = 499; n unique genes = 275), bipolar disorder (n associations = 17; n unique genes = 13), attention deficit hyperactivity disorder (n associations = 19; n unique genes = 12) and broad depression (n associations = 41; n unique genes = 31). Importantly, both PrediXcan and summary-data-based Mendelian randomization/heterogeneity in dependent instruments analyses suggest potentially causal genes in non-brain tissues, showing the utility of these tissues for mapping psychiatric disease genetic predisposition. Our analyses further highlight the importance of joint tissue approaches as 76% of the genes were detected only in difficult-to-acquire tissues. The genetic architecture of psychiatric disorders is characterized by a large number of small-effect variants.sup.1 located primarily in non-coding regions, suggesting that the underlying causal effects may influence disease risk by modulating gene expression.sup.2-4. We provide comprehensive analyses using transcriptome data from an unprecedented collection of tissues to gain pathophysiological insights into the role of the brain, neuroendocrine factors (adrenal gland) and gastrointestinal systems (colon) in psychiatric disorders. In each tissue, we perform PrediXcan analysis and identify trait-associated genes for schizophrenia (n associations = 499; n unique genes = 275), bipolar disorder (n associations = 17; n unique genes = 13), attention deficit hyperactivity disorder (n associations = 19; n unique genes = 12) and broad depression (n associations = 41; n unique genes = 31). Importantly, both PrediXcan and summary-data-based Mendelian randomization/heterogeneity in dependent instruments analyses suggest potentially causal genes in non-brain tissues, showing the utility of these tissues for mapping psychiatric disease genetic predisposition. Our analyses further highlight the importance of joint tissue approaches as 76% of the genes were detected only in difficult-to-acquire tissues. The genetic architecture of psychiatric disorders is characterized by a large number of small-effect variants located primarily in non-coding regions, suggesting that the underlying causal effects may influence disease risk by modulating gene expression . We provide comprehensive analyses using transcriptome data from an unprecedented collection of tissues to gain pathophysiological insights into the role of the brain, neuroendocrine factors (adrenal gland) and gastrointestinal systems (colon) in psychiatric disorders. In each tissue, we perform PrediXcan analysis and identify trait-associated genes for schizophrenia (n associations = 499; n unique genes = 275), bipolar disorder (n associations = 17; n unique genes = 13), attention deficit hyperactivity disorder (n associations = 19; n unique genes = 12) and broad depression (n associations = 41; n unique genes = 31). Importantly, both PrediXcan and summary-data-based Mendelian randomization/heterogeneity in dependent instruments analyses suggest potentially causal genes in non-brain tissues, showing the utility of these tissues for mapping psychiatric disease genetic predisposition. Our analyses further highlight the importance of joint tissue approaches as 76% of the genes were detected only in difficult-to-acquire tissues. The genetic architecture of psychiatric disorders is characterized by a large number of small-effect variants.sup.1 located primarily in non-coding regions, suggesting that the underlying causal effects may influence disease risk by modulating gene expression.sup.2-4. We provide comprehensive analyses using transcriptome data from an unprecedented collection of tissues to gain pathophysiological insights into the role of the brain, neuroendocrine factors (adrenal gland) and gastrointestinal systems (colon) in psychiatric disorders. In each tissue, we perform PrediXcan analysis and identify trait-associated genes for schizophrenia (n associations = 499; n unique genes = 275), bipolar disorder (n associations = 17; n unique genes = 13), attention deficit hyperactivity disorder (n associations = 19; n unique genes = 12) and broad depression (n associations = 41; n unique genes = 31). Importantly, both PrediXcan and summary-data-based Mendelian randomization/heterogeneity in dependent instruments analyses suggest potentially causal genes in non-brain tissues, showing the utility of these tissues for mapping psychiatric disease genetic predisposition. Our analyses further highlight the importance of joint tissue approaches as 76% of the genes were detected only in difficult-to-acquire tissues. Multi-tissue transcriptome analyses using PrediXcan identify numerous trait-associated genes for schizophrenia, bipolar disorder, attention deficit hyperactivity disorder and broad depression, and highlight potentially causal genes in non-brain tissues. The genetic architecture of psychiatric disorders is characterized by a large number of small-effect variants1 located primarily in non-coding regions, suggesting that the underlying causal effects may influence disease risk by modulating gene expression2-4. We provide comprehensive analyses using transcriptome data from an unprecedented collection of tissues to gain pathophysiological insights into the role of the brain, neuroendocrine factors (adrenal gland) and gastrointestinal systems (colon) in psychiatric disorders. In each tissue, we perform PrediXcan analysis and identify trait-associated genes for schizophrenia (n associations = 499; n unique genes = 275), bipolar disorder (n associations = 17; n unique genes = 13), attention deficit hyperactivity disorder (n associations = 19; n unique genes = 12) and broad depression (n associations = 41; n unique genes = 31). Importantly, both PrediXcan and summary-data-based Mendelian randomization/heterogeneity in dependent instruments analyses suggest potentially causal genes in non-brain tissues, showing the utility of these tissues for mapping psychiatric disease genetic predisposition. Our analyses further highlight the importance of joint tissue approaches as 76% of the genes were detected only in difficult-to-acquire tissues. |
| Audience | Academic |
| Author | Zwinderman, Aeilko H. Cox, Nancy J. Denys, Damiaan Derks, Eske M. Gamazon, Eric R. |
| AuthorAffiliation | 5 Clare Hall, University of Cambridge, Cambridge, UK 4 Data Science Institute, Vanderbilt University, Nashville, TN, USA 2 Division of Genetic Medicine, Department of Medicine, Vanderbilt University, Nashville, TN, USA 3 Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA 6 QIMR Berghofer, Translational Neurogenomics group, Brisbane, Australia 1 Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands |
| AuthorAffiliation_xml | – name: 6 QIMR Berghofer, Translational Neurogenomics group, Brisbane, Australia – name: 5 Clare Hall, University of Cambridge, Cambridge, UK – name: 4 Data Science Institute, Vanderbilt University, Nashville, TN, USA – name: 3 Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA – name: 2 Division of Genetic Medicine, Department of Medicine, Vanderbilt University, Nashville, TN, USA – name: 1 Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands |
| Author_xml | – sequence: 1 givenname: Eric R. orcidid: 0000-0003-4204-8734 surname: Gamazon fullname: Gamazon, Eric R. email: egamazon@uchicago.edu organization: Academic Medical Center, University of Amsterdam, Division of Genetic Medicine, Department of Medicine, Vanderbilt University, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Data Science Institute, Vanderbilt University, Clare Hall, University of Cambridge – sequence: 2 givenname: Aeilko H. surname: Zwinderman fullname: Zwinderman, Aeilko H. organization: Academic Medical Center, University of Amsterdam – sequence: 3 givenname: Nancy J. surname: Cox fullname: Cox, Nancy J. organization: Division of Genetic Medicine, Department of Medicine, Vanderbilt University, Vanderbilt Genetics Institute, Vanderbilt University Medical Center – sequence: 4 givenname: Damiaan surname: Denys fullname: Denys, Damiaan organization: Academic Medical Center, University of Amsterdam – sequence: 5 givenname: Eske M. orcidid: 0000-0002-6292-6883 surname: Derks fullname: Derks, Eske M. email: eske.derks@qimrberghofer.edu.au organization: Academic Medical Center, University of Amsterdam, QIMR Berghofer, Translational Neurogenomics Group |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31086352$$D View this record in MEDLINE/PubMed |
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| Snippet | The genetic architecture of psychiatric disorders is characterized by a large number of small-effect variants
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located primarily in non-coding regions,... The genetic architecture of psychiatric disorders is characterized by a large number of small-effect variants located primarily in non-coding regions,... The genetic architecture of psychiatric disorders is characterized by a large number of small-effect variants.sup.1 located primarily in non-coding regions,... The genetic architecture of psychiatric disorders is characterized by a large number of small-effect variants1 located primarily in non-coding regions,... |
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| SubjectTerms | 38 38/91 45 45/23 45/43 631/208/199 631/208/212 631/208/212/2019 692/699/476/1799 Adrenal glands Agriculture Algorithms Animal Genetics and Genomics Attention deficit hyperactivity disorder Biomedical and Life Sciences Biomedicine Bipolar disorder Blood pressure Brain Brain mapping Cancer Research Colon Computational Biology - methods Consortia Diagnosis Disorders Gene expression Gene Expression Profiling Gene Expression Regulation Gene Function Gene mapping Gene Regulatory Networks Genes Genetic analysis Genetic aspects Genetic Association Studies - methods Genetic Predisposition to Disease Genome-Wide Association Study Genomes Health risks Heterogeneity Human Genetics Humans Letter Mapping Mental depression Mental disorders Mental Disorders - diagnosis Mental Disorders - genetics Mental Disorders - psychology Mental illness Nervous system diseases Non-coding RNA Organ Specificity Polymorphism, Single Nucleotide Quantitative Trait Loci Quantitative Trait, Heritable Schizophrenia Studies Tissue analysis Tissues Transcriptome Uniqueness |
| Title | Multi-tissue transcriptome analyses identify genetic mechanisms underlying neuropsychiatric traits |
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