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Making sense of GWAS: using epigenomics and genome engineering to understand the functional relevance of SNPs in non-coding regions of the human genome

Overview of attention for article published in Epigenetics & Chromatin, December 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#30 of 547)
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

twitter
42 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
222 Dimensions

Readers on

mendeley
482 Mendeley
citeulike
2 CiteULike
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Title
Making sense of GWAS: using epigenomics and genome engineering to understand the functional relevance of SNPs in non-coding regions of the human genome
Published in
Epigenetics & Chromatin, December 2015
DOI 10.1186/s13072-015-0050-4
Pubmed ID
Authors

Yu Gyoung Tak, Peggy J. Farnham

Abstract

Considerable progress towards an understanding of complex diseases has been made in recent years due to the development of high-throughput genotyping technologies. Using microarrays that contain millions of single-nucleotide polymorphisms (SNPs), Genome Wide Association Studies (GWASs) have identified SNPs that are associated with many complex diseases or traits. For example, as of February 2015, 2111 association studies have identified 15,396 SNPs for various diseases and traits, with the number of identified SNP-disease/trait associations increasing rapidly in recent years. However, it has been difficult for researchers to understand disease risk from GWAS results. This is because most GWAS-identified SNPs are located in non-coding regions of the genome. It is important to consider that the GWAS-identified SNPs serve only as representatives for all SNPs in the same haplotype block, and it is equally likely that other SNPs in high linkage disequilibrium (LD) with the array-identified SNPs are causal for the disease. Because it was hoped that disease-associated coding variants would be identified if the true casual SNPs were known, investigators have expanded their analyses using LD calculation and fine-mapping. However, such analyses also identified risk-associated SNPs located in non-coding regions. Thus, the GWAS field has been left with the conundrum as to how a single-nucleotide change in a non-coding region could confer increased risk for a specific disease. One possible answer to this puzzle is that the variant SNPs cause changes in gene expression levels rather than causing changes in protein function. This review provides a description of (1) advances in genomic and epigenomic approaches that incorporate functional annotation of regulatory elements to prioritize the disease risk-associated SNPs that are located in non-coding regions of the genome for follow-up studies, (2) various computational tools that aid in identifying gene expression changes caused by the non-coding disease-associated SNPs, and (3) experimental approaches to identify target genes of, and study the biological phenotypes conferred by, non-coding disease-associated SNPs.

Twitter Demographics

The data shown below were collected from the profiles of 42 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 482 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 <1%
Spain 2 <1%
Brazil 2 <1%
Czechia 1 <1%
Canada 1 <1%
Hungary 1 <1%
Mexico 1 <1%
Norway 1 <1%
New Zealand 1 <1%
Other 1 <1%
Unknown 469 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 136 28%
Researcher 94 20%
Student > Bachelor 50 10%
Student > Master 49 10%
Student > Doctoral Student 25 5%
Other 63 13%
Unknown 65 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 156 32%
Agricultural and Biological Sciences 150 31%
Medicine and Dentistry 42 9%
Immunology and Microbiology 10 2%
Neuroscience 9 2%
Other 35 7%
Unknown 80 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 22. 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 01 December 2017.
All research outputs
#1,354,774
of 21,376,549 outputs
Outputs from Epigenetics & Chromatin
#30
of 547 outputs
Outputs of similar age
#28,846
of 404,305 outputs
Outputs of similar age from Epigenetics & Chromatin
#6
of 57 outputs
Altmetric has tracked 21,376,549 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 547 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.4. This one has done particularly well, scoring higher than 94% 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 404,305 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 92% of its contemporaries.
We're also able to compare this research output to 57 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.