Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types
We introduce an approach to identify disease-relevant tissues and cell types by analyzing gene expression data together with genome-wide association study (GWAS) summary statistics. Our approach uses stratified linkage disequilibrium (LD) score regression to test whether disease heritability is enri...
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| Published in: | Nature genetics Vol. 50; no. 4; pp. 621 - 629 |
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
Nature Publishing Group US
01.04.2018
Nature Publishing Group |
| Subjects: | |
| ISSN: | 1061-4036, 1546-1718, 1546-1718 |
| Online Access: | Get full text |
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| Abstract | We introduce an approach to identify disease-relevant tissues and cell types by analyzing gene expression data together with genome-wide association study (GWAS) summary statistics. Our approach uses stratified linkage disequilibrium (LD) score regression to test whether disease heritability is enriched in regions surrounding genes with the highest specific expression in a given tissue. We applied our approach to gene expression data from several sources together with GWAS summary statistics for 48 diseases and traits (average
N
= 169,331) and found significant tissue-specific enrichments (false discovery rate (FDR) < 5%) for 34 traits. In our analysis of multiple tissues, we detected a broad range of enrichments that recapitulated known biology. In our brain-specific analysis, significant enrichments included an enrichment of inhibitory over excitatory neurons for bipolar disorder, and excitatory over inhibitory neurons for schizophrenia and body mass index. Our results demonstrate that our polygenic approach is a powerful way to leverage gene expression data for interpreting GWAS signals.
A new method tests whether disease heritability is enriched near genes with high tissue-specific expression. The authors use gene expression data together with GWAS summary statistics for 48 diseases and traits to identify disease-relevant tissues. |
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| AbstractList | We introduce an approach to identify disease-relevant tissues and cell types by analyzing gene expression data together with genome-wide association study (GWAS) summary statistics. Our approach uses stratified linkage disequilibrium (LD) score regression to test whether disease heritability is enriched in regions surrounding genes with the highest specific expression in a given tissue. We applied our approach to gene expression data from several sources together with GWAS summary statistics for 48 diseases and traits (average N = 169,331) and found significant tissue-specific enrichments (false discovery rate (FDR) < 5%) for 34 traits. In our analysis of multiple tissues, we detected a broad range of enrichments that recapitulated known biology. In our brain-specific analysis, significant enrichments included an enrichment of inhibitory over excitatory neurons for bipolar disorder, and excitatory over inhibitory neurons for schizophrenia and body mass index. Our results demonstrate that our polygenic approach is a powerful way to leverage gene expression data for interpreting GWAS signals. We introduce an approach to identify disease-relevant tissues and cell types by analyzing gene expression data together with genome-wide association study (GWAS) summary statistics. Our approach uses stratified linkage disequilibrium (LD) score regression to test whether disease heritability is enriched in regions surrounding genes with the highest specific expression in a given tissue. We applied our approach to gene expression data from several sources together with GWAS summary statistics for 48 diseases and traits (average N = 169,331) and found significant tissue-specific enrichments (false discovery rate (FDR) < 5%) for 34 traits. In our analysis of multiple tissues, we detected a broad range of enrichments that recapitulated known biology. In our brain-specific analysis, significant enrichments included an enrichment of inhibitory over excitatory neurons for bipolar disorder, and excitatory over inhibitory neurons for schizophrenia and body mass index. Our results demonstrate that our polygenic approach is a powerful way to leverage gene expression data for interpreting GWAS signals. A new method tests whether disease heritability is enriched near genes with high tissue-specific expression. The authors use gene expression data together with GWAS summary statistics for 48 diseases and traits to identify disease-relevant tissues. We introduce an approach to identify disease-relevant tissues and cell types by analyzing gene expression data together with genome-wide association study (GWAS) summary statistics. Our approach uses stratified linkage disequilibrium (LD) score regression to test whether disease heritability is enriched in regions surrounding genes with the highest specific expression in a given tissue. We applied our approach to gene expression data from several sources together with GWAS summary statistics for 48 diseases and traits (average N = 169,331) and found significant tissue-specific enrichments (false discovery rate (FDR) < 5%) for 34 traits. In our analysis of multiple tissues, we detected a broad range of enrichments that recapitulated known biology. In our brain-specific analysis, significant enrichments included an enrichment of inhibitory over excitatory neurons for bipolar disorder, and excitatory over inhibitory neurons for schizophrenia and body mass index. Our results demonstrate that our polygenic approach is a powerful way to leverage gene expression data for interpreting GWAS signals. A new method tests whether disease heritability is enriched near genes with high tissue-specific expression. The authors use gene expression data together with GWAS summary statistics for 48 diseases and traits to identify disease-relevant tissues. We introduce an approach to identify disease-relevant tissues and cell types by analyzing gene expression data together with genome-wide association study (GWAS) summary statistics. Our approach uses stratified linkage disequilibrium (LD) score regression to test whether disease heritability is enriched in regions surrounding genes with the highest specific expression in a given tissue. We applied our approach to gene expression data from several sources together with GWAS summary statistics for 48 diseases and traits (average N = 169,331) and found significant tissue-specific enrichments (false discovery rate (FDR) < 5%) for 34 traits. In our analysis of multiple tissues, we detected a broad range of enrichments that recapitulated known biology. In our brain-specific analysis, significant enrichments included an enrichment of inhibitory over excitatory neurons for bipolar disorder, and excitatory over inhibitory neurons for schizophrenia and body mass index. Our results demonstrate that our polygenic approach is a powerful way to leverage gene expression data for interpreting GWAS signals.We introduce an approach to identify disease-relevant tissues and cell types by analyzing gene expression data together with genome-wide association study (GWAS) summary statistics. Our approach uses stratified linkage disequilibrium (LD) score regression to test whether disease heritability is enriched in regions surrounding genes with the highest specific expression in a given tissue. We applied our approach to gene expression data from several sources together with GWAS summary statistics for 48 diseases and traits (average N = 169,331) and found significant tissue-specific enrichments (false discovery rate (FDR) < 5%) for 34 traits. In our analysis of multiple tissues, we detected a broad range of enrichments that recapitulated known biology. In our brain-specific analysis, significant enrichments included an enrichment of inhibitory over excitatory neurons for bipolar disorder, and excitatory over inhibitory neurons for schizophrenia and body mass index. Our results demonstrate that our polygenic approach is a powerful way to leverage gene expression data for interpreting GWAS signals. We introduce an approach for identifying disease-relevant tissues and cell types by analyzing gene expression data together with genome-wide association study (GWAS) summary statistics. Our approach uses stratified LD score regression to test whether disease heritability is enriched in regions surrounding genes with the highest specific expression in a given tissue. We apply our approach to gene expression data from several sources together with GWAS summary statistics for 48 diseases and traits (average N=169K), detecting significant tissue-specific enrichments (FDR<5%) for 34 traits. In our analysis of multiple tissues, we detect a broad range of enrichments that recapitulate known biology. In our brain-specific and immune-specific analyses, significant enrichments include an enrichment of inhibitory over excitatory neurons for bipolar disorder but excitatory over inhibitory neurons for schizophrenia and body mass index. Our results demonstrate that our polygenic approach is a powerful way to leverage gene expression data for interpreting GWAS signal. |
| Audience | Academic |
| Author | Anttila, Verneri Loh, Po-Ru Macosko, Evan Finucane, Hilary K. Price, Alkes L. Neale, Benjamin M. Pollack, Samuela Gusev, Alexander Genovese, Giulio Lareau, Caleb Saunders, Arpiar Shoresh, Noam Slowikowski, Kamil Bernstein, Bradley E. McCarroll, Steven Buenrostro, Jason D. Raychaudhuri, Soumya Perry, John R. B. Byrnes, Andrea Gazal, Steven Reshef, Yakir A. |
| AuthorAffiliation | 10 Harvard Society of Fellows, Harvard University, Cambridge, MA 02138, USA 13 Division of Rheumatology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA 15 Faculty of Medical and Human Sciences, University of Manchester, Manchester, UK 14 Partners Center for Personalized Genetic Medicine, Boston, Massachusetts, USA 1 Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA 11 Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA 5 Analytic and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA 9 Medical Research Council (MRC) Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK 12 Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA 8 Department of Genetics, Harvard Medical School, Boston, Mas |
| AuthorAffiliation_xml | – name: 2 Department of Mathematics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA – name: 10 Harvard Society of Fellows, Harvard University, Cambridge, MA 02138, USA – name: 14 Partners Center for Personalized Genetic Medicine, Boston, Massachusetts, USA – name: 8 Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA – name: 13 Division of Rheumatology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA – name: 12 Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA – name: 11 Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA – name: 4 Department of Computer Science, Harvard University, Cambridge, Massachusetts, USA – name: 5 Analytic and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA – name: 9 Medical Research Council (MRC) Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK – name: 6 Bioinformatics and Integrative Genomics, Harvard University, Cambridge, MA – name: 1 Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA – name: 3 Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA – name: 15 Faculty of Medical and Human Sciences, University of Manchester, Manchester, UK – name: 7 Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA |
| Author_xml | – sequence: 1 givenname: Hilary K. orcidid: 0000-0003-3864-9828 surname: Finucane fullname: Finucane, Hilary K. email: finucane@broadinstitute.org organization: Broad Institute of MIT and Harvard, Department of Mathematics, Massachusetts Institute of Technology, Department of Epidemiology, Harvard T. H. Chan School of Public Health – sequence: 2 givenname: Yakir A. orcidid: 0000-0001-6463-4203 surname: Reshef fullname: Reshef, Yakir A. organization: Department of Computer Science, Harvard University – sequence: 3 givenname: Verneri surname: Anttila fullname: Anttila, Verneri organization: Broad Institute of MIT and Harvard, Analytic and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School – sequence: 4 givenname: Kamil orcidid: 0000-0002-2843-6370 surname: Slowikowski fullname: Slowikowski, Kamil organization: Broad Institute of MIT and Harvard, Bioinformatics and Integrative Genomics, Harvard University, Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School – sequence: 5 givenname: Alexander orcidid: 0000-0002-7980-4620 surname: Gusev fullname: Gusev, Alexander organization: Department of Epidemiology, Harvard T. H. Chan School of Public Health – sequence: 6 givenname: Andrea surname: Byrnes fullname: Byrnes, Andrea organization: Broad Institute of MIT and Harvard, Analytic and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School – sequence: 7 givenname: Steven orcidid: 0000-0003-4510-5730 surname: Gazal fullname: Gazal, Steven organization: Department of Epidemiology, Harvard T. H. Chan School of Public Health – sequence: 8 givenname: Po-Ru surname: Loh fullname: Loh, Po-Ru organization: Department of Epidemiology, Harvard T. H. Chan School of Public Health – sequence: 9 givenname: Caleb surname: Lareau fullname: Lareau, Caleb organization: Broad Institute of MIT and Harvard, Department of Biostatistics, Harvard T. H. Chan School of Public Health – sequence: 10 givenname: Noam surname: Shoresh fullname: Shoresh, Noam organization: Broad Institute of MIT and Harvard – sequence: 11 givenname: Giulio orcidid: 0000-0003-3066-5575 surname: Genovese fullname: Genovese, Giulio organization: Broad Institute of MIT and Harvard – sequence: 12 givenname: Arpiar surname: Saunders fullname: Saunders, Arpiar organization: Department of Genetics, Harvard Medical School – sequence: 13 givenname: Evan surname: Macosko fullname: Macosko, Evan organization: Department of Genetics, Harvard Medical School – sequence: 14 givenname: Samuela surname: Pollack fullname: Pollack, Samuela organization: Department of Epidemiology, Harvard T. H. Chan School of Public Health – sequence: 16 givenname: John R. B. surname: Perry fullname: Perry, John R. B. organization: Medical Research Council (MRC) Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science – sequence: 17 givenname: Jason D. surname: Buenrostro fullname: Buenrostro, Jason D. organization: Broad Institute of MIT and Harvard, Harvard Society of Fellows, Harvard University – sequence: 18 givenname: Bradley E. surname: Bernstein fullname: Bernstein, Bradley E. organization: Broad Institute of MIT and Harvard, Department of Pathology, Massachusetts General Hospital and Harvard Medical School – sequence: 19 givenname: Soumya surname: Raychaudhuri fullname: Raychaudhuri, Soumya organization: Broad Institute of MIT and Harvard, Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Division of Rheumatology, Brigham and Women’s Hospital, Harvard Medical School, Partners Center for Personalized Genetic Medicine, Faculty of Medical and Human Sciences, University of Manchester – sequence: 20 givenname: Steven surname: McCarroll fullname: McCarroll, Steven organization: Broad Institute of MIT and Harvard, Department of Genetics, Harvard Medical School – sequence: 21 givenname: Benjamin M. orcidid: 0000-0003-1513-6077 surname: Neale fullname: Neale, Benjamin M. organization: Broad Institute of MIT and Harvard, Analytic and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School – sequence: 22 givenname: Alkes L. orcidid: 0000-0002-2971-7975 surname: Price fullname: Price, Alkes L. email: aprice@hsph.harvard.edu organization: Broad Institute of MIT and Harvard, Department of Epidemiology, Harvard T. H. Chan School of Public Health |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29632380$$D View this record in MEDLINE/PubMed |
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| Snippet | We introduce an approach to identify disease-relevant tissues and cell types by analyzing gene expression data together with genome-wide association study... We introduce an approach for identifying disease-relevant tissues and cell types by analyzing gene expression data together with genome-wide association study... |
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| SubjectTerms | 631/114 631/208/199 631/208/205/2138 Agriculture Alzheimer's disease Analysis Animal Genetics and Genomics Annotations Biochemistry Biomedical and Life Sciences Biomedicine Bipolar disorder Bipolar Disorder - genetics Body Mass Index Body size Brain Brain - metabolism Cancer Research Chromatin - genetics Datasets Enrichment Epigenesis, Genetic Gene Expression Gene Expression Profiling - statistics & numerical data Gene Function Genes Genetic Predisposition to Disease Genome-wide association studies Genome-Wide Association Study - statistics & numerical data Genomes Genomics Heritability Human Genetics Humans Immune System Diseases - genetics Linkage Disequilibrium Mental disorders Metabolism Models, Genetic Multifactorial Inheritance Neurons Neurons - metabolism Polygenic inheritance Regression analysis Schizophrenia Schizophrenia - genetics Statistical analysis Statistical tests Statistics Tissue Distribution - genetics Tissues Tourette syndrome Type 2 diabetes |
| Title | Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types |
| URI | https://link.springer.com/article/10.1038/s41588-018-0081-4 https://www.ncbi.nlm.nih.gov/pubmed/29632380 https://www.proquest.com/docview/2247501553 https://www.proquest.com/docview/2023728614 https://pubmed.ncbi.nlm.nih.gov/PMC5896795 |
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