↓ Skip to main content

An integrative functional genomics framework for effective identification of novel regulatory variants in genome–phenome studies

Overview of attention for article published in Genome Medicine, January 2018
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (73rd percentile)

Mentioned by

twitter
14 tweeters

Citations

dimensions_citation
25 Dimensions

Readers on

mendeley
75 Mendeley
citeulike
1 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
An integrative functional genomics framework for effective identification of novel regulatory variants in genome–phenome studies
Published in
Genome Medicine, January 2018
DOI 10.1186/s13073-018-0513-x
Pubmed ID
Authors

Junfei Zhao, Feixiong Cheng, Peilin Jia, Nancy Cox, Joshua C. Denny, Zhongming Zhao

Abstract

Genome-phenome studies have identified thousands of variants that are statistically associated with disease or traits; however, their functional roles are largely unclear. A comprehensive investigation of regulatory mechanisms and the gene regulatory networks between phenome-wide association study (PheWAS) and genome-wide association study (GWAS) is needed to identify novel regulatory variants contributing to risk for human diseases. In this study, we developed an integrative functional genomics framework that maps 215,107 significant single nucleotide polymorphism (SNP) traits generated from the PheWAS Catalog and 28,870 genome-wide significant SNP traits collected from the GWAS Catalog into a global human genome regulatory map via incorporating various functional annotation data, including transcription factor (TF)-based motifs, promoters, enhancers, and expression quantitative trait loci (eQTLs) generated from four major functional genomics databases: FANTOM5, ENCODE, NIH Roadmap, and Genotype-Tissue Expression (GTEx). In addition, we performed a tissue-specific regulatory circuit analysis through the integration of the identified regulatory variants and tissue-specific gene expression profiles in 7051 samples across 32 tissues from GTEx. We found that the disease-associated loci in both the PheWAS and GWAS Catalogs were significantly enriched with functional SNPs. The integration of functional annotations significantly improved the power of detecting novel associations in PheWAS, through which we found a number of functional associations with strong regulatory evidence in the PheWAS Catalog. Finally, we constructed tissue-specific regulatory circuits for several complex traits: mental diseases, autoimmune diseases, and cancer, via exploring tissue-specific TF-promoter/enhancer-target gene interaction networks. We uncovered several promising tissue-specific regulatory TFs or genes for Alzheimer's disease (e.g. ZIC1 and STX1B) and asthma (e.g. CSF3 and IL1RL1). This study offers powerful tools for exploring the functional consequences of variants generated from genome-phenome association studies in terms of their mechanisms on affecting multiple complex diseases and traits.

Twitter Demographics

The data shown below were collected from the profiles of 14 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 75 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 75 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 23%
Student > Ph. D. Student 13 17%
Student > Bachelor 6 8%
Student > Master 6 8%
Other 4 5%
Other 11 15%
Unknown 18 24%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 24 32%
Agricultural and Biological Sciences 14 19%
Medicine and Dentistry 5 7%
Computer Science 3 4%
Engineering 3 4%
Other 6 8%
Unknown 20 27%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 15 June 2018.
All research outputs
#2,967,538
of 13,090,338 outputs
Outputs from Genome Medicine
#624
of 937 outputs
Outputs of similar age
#91,757
of 347,059 outputs
Outputs of similar age from Genome Medicine
#7
of 9 outputs
Altmetric has tracked 13,090,338 research outputs across all sources so far. Compared to these this one has done well and is in the 77th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 937 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 22.6. This one is in the 33rd percentile – i.e., 33% of its peers scored the same or lower than it.
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 347,059 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.