Title |
Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types
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Published in |
Nature Genetics, April 2018
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DOI | 10.1038/s41588-018-0081-4 |
Pubmed ID | |
Authors |
Hilary K. Finucane, Yakir A. Reshef, Verneri Anttila, Kamil Slowikowski, Alexander Gusev, Andrea Byrnes, Steven Gazal, Po-Ru Loh, Caleb Lareau, Noam Shoresh, Giulio Genovese, Arpiar Saunders, Evan Macosko, Samuela Pollack, John R. B. Perry, Jason D. Buenrostro, Bradley E. Bernstein, Soumya Raychaudhuri, Steven McCarroll, Benjamin M. Neale, Alkes L. Price |
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. |
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United Kingdom | 7 | 8% |
Germany | 6 | 7% |
Spain | 3 | 4% |
Canada | 2 | 2% |
Israel | 2 | 2% |
Finland | 2 | 2% |
Comoros | 2 | 2% |
Iceland | 1 | 1% |
Other | 10 | 12% |
Unknown | 24 | 29% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 39 | 47% |
Scientists | 37 | 45% |
Practitioners (doctors, other healthcare professionals) | 6 | 7% |
Unknown | 1 | 1% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Germany | 1 | <1% |
Unknown | 769 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 172 | 22% |
Student > Ph. D. Student | 166 | 22% |
Student > Bachelor | 54 | 7% |
Student > Master | 50 | 6% |
Student > Doctoral Student | 38 | 5% |
Other | 117 | 15% |
Unknown | 173 | 22% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 216 | 28% |
Agricultural and Biological Sciences | 138 | 18% |
Medicine and Dentistry | 56 | 7% |
Neuroscience | 51 | 7% |
Computer Science | 27 | 4% |
Other | 81 | 11% |
Unknown | 201 | 26% |