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GWASeq: targeted re-sequencing follow up to GWAS

Overview of attention for article published in BMC Genomics, March 2016
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  • Above-average Attention Score compared to outputs of the same age (57th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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4 tweeters

Citations

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8 Dimensions

Readers on

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51 Mendeley
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1 CiteULike
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Title
GWASeq: targeted re-sequencing follow up to GWAS
Published in
BMC Genomics, March 2016
DOI 10.1186/s12864-016-2459-y
Pubmed ID
Authors

Matthew P. Salomon, Wai Lok Sibon Li, Christopher K. Edlund, John Morrison, Barbara K. Fortini, Aung Ko Win, David V. Conti, Duncan C. Thomas, David Duggan, Daniel D. Buchanan, Mark A. Jenkins, John L. Hopper, Steven Gallinger, Loïc Le Marchand, Polly A. Newcomb, Graham Casey, Paul Marjoram

Abstract

For the last decade the conceptual framework of the Genome-Wide Association Study (GWAS) has dominated the investigation of human disease and other complex traits. While GWAS have been successful in identifying a large number of variants associated with various phenotypes, the overall amount of heritability explained by these variants remains small. This raises the question of how best to follow up on a GWAS, localize causal variants accounting for GWAS hits, and as a consequence explain more of the so-called "missing" heritability. Advances in high throughput sequencing technologies now allow for the efficient and cost-effective collection of vast amounts of fine-scale genomic data to complement GWAS. We investigate these issues using a colon cancer dataset. After QC, our data consisted of 1993 cases, 899 controls. Using marginal tests of associations, we identify 10 variants distributed among six targeted regions that are significantly associated with colorectal cancer, with eight of the variants being novel to this study. Additionally, we perform so-called 'SNP-set' tests of association and identify two sets of variants that implicate both common and rare variants in the etiology of colorectal cancer. Here we present a large-scale targeted re-sequencing resource focusing on genomic regions implicated in colorectal cancer susceptibility previously identified in several GWAS, which aims to 1) provide fine-scale targeted sequencing data for fine-mapping and 2) provide data resources to address methodological questions regarding the design of sequencing-based follow-up studies to GWAS. Additionally, we show that this strategy successfully identifies novel variants associated with colorectal cancer susceptibility and can implicate both common and rare variants.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 2%
United States 1 2%
Belgium 1 2%
Unknown 48 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 31%
Student > Ph. D. Student 9 18%
Professor > Associate Professor 5 10%
Student > Bachelor 2 4%
Student > Postgraduate 2 4%
Other 6 12%
Unknown 11 22%
Readers by discipline Count As %
Agricultural and Biological Sciences 19 37%
Biochemistry, Genetics and Molecular Biology 13 25%
Medicine and Dentistry 4 8%
Unknown 15 29%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 07 March 2016.
All research outputs
#6,061,901
of 11,293,566 outputs
Outputs from BMC Genomics
#3,190
of 6,784 outputs
Outputs of similar age
#120,219
of 292,280 outputs
Outputs of similar age from BMC Genomics
#117
of 220 outputs
Altmetric has tracked 11,293,566 research outputs across all sources so far. This one is in the 45th percentile – i.e., 45% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,784 research outputs from this source. They receive a mean Attention Score of 4.2. This one has gotten more attention than average, scoring higher than 50% 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 292,280 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 57% of its contemporaries.
We're also able to compare this research output to 220 others from the same source and published within six weeks on either side of this one. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.