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Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types

Overview of attention for article published in Nature Genetics, April 2018
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  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (95th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

news
1 news outlet
blogs
1 blog
twitter
83 X users
patent
2 patents

Citations

dimensions_citation
804 Dimensions

Readers on

mendeley
770 Mendeley
citeulike
4 CiteULike
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Title
Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types
Published in
Nature Genetics, April 2018
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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 83 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 770 Mendeley readers of this research output. Click here to see the associated Mendeley record.

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%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 63. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 October 2023.
All research outputs
#669,182
of 25,157,832 outputs
Outputs from Nature Genetics
#1,286
of 7,541 outputs
Outputs of similar age
#15,222
of 335,213 outputs
Outputs of similar age from Nature Genetics
#40
of 65 outputs
Altmetric has tracked 25,157,832 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,541 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 43.0. This one has done well, scoring higher than 82% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 335,213 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 65 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.