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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 (52nd percentile)
  • Average Attention Score compared to outputs of the same age and source

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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.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 54 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 51 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 28%
Student > Ph. D. Student 10 19%
Professor > Associate Professor 5 9%
Professor 2 4%
Student > Bachelor 2 4%
Other 8 15%
Unknown 12 22%
Readers by discipline Count As %
Agricultural and Biological Sciences 19 35%
Biochemistry, Genetics and Molecular Biology 13 24%
Medicine and Dentistry 5 9%
Neuroscience 1 2%
Unknown 16 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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
#13,511,215
of 23,310,485 outputs
Outputs from BMC Genomics
#4,834
of 10,742 outputs
Outputs of similar age
#140,954
of 299,821 outputs
Outputs of similar age from BMC Genomics
#98
of 211 outputs
Altmetric has tracked 23,310,485 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,742 research outputs from this source. They receive a mean Attention Score of 4.7. This one has gotten more attention than average, scoring higher than 53% 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 299,821 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 52% of its contemporaries.
We're also able to compare this research output to 211 others from the same source and published within six weeks on either side of this one. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.