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Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals

Overview of attention for article published in BioData Mining, July 2017
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Mentioned by

twitter
9 tweeters

Citations

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5 Dimensions

Readers on

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41 Mendeley
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Title
Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals
Published in
BioData Mining, July 2017
DOI 10.1186/s13040-017-0145-5
Pubmed ID
Authors

Emily R. Holzinger, Shefali S. Verma, Carrie B. Moore, Molly Hall, Rishika De, Diane Gilbert-Diamond, Matthew B. Lanktree, Nathan Pankratz, Antoinette Amuzu, Amber Burt, Caroline Dale, Scott Dudek, Clement E. Furlong, Tom R. Gaunt, Daniel Seung Kim, Helene Riess, Suthesh Sivapalaratnam, Vinicius Tragante, Erik P.A. van Iperen, Ariel Brautbar, David S. Carrell, David R. Crosslin, Gail P. Jarvik, Helena Kuivaniemi, Iftikhar J. Kullo, Eric B. Larson, Laura J. Rasmussen-Torvik, Gerard Tromp, Jens Baumert, Karen J. Cruickshanks, Martin Farrall, Aroon D. Hingorani, G. K. Hovingh, Marcus E. Kleber, Barbara E. Klein, Ronald Klein, Wolfgang Koenig, Leslie A. Lange, Winfried Mӓrz, Kari E. North, N. Charlotte Onland-Moret, Alex P. Reiner, Philippa J. Talmud, Yvonne T. van der Schouw, James G. Wilson, Mika Kivimaki, Meena Kumari, Jason H. Moore, Fotios Drenos, Folkert W. Asselbergs, Brendan J. Keating, Marylyn D. Ritchie

Abstract

The genetic etiology of human lipid quantitative traits is not fully elucidated, and interactions between variants may play a role. We performed a gene-centric interaction study for four different lipid traits: low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), total cholesterol (TC), and triglycerides (TG). Our analysis consisted of a discovery phase using a merged dataset of five different cohorts (n = 12,853 to n = 16,849 depending on lipid phenotype) and a replication phase with ten independent cohorts totaling up to 36,938 additional samples. Filters are often applied before interaction testing to correct for the burden of testing all pairwise interactions. We used two different filters: 1. A filter that tested only single nucleotide polymorphisms (SNPs) with a main effect of p < 0.001 in a previous association study. 2. A filter that only tested interactions identified by Biofilter 2.0. Pairwise models that reached an interaction significance level of p < 0.001 in the discovery dataset were tested for replication. We identified thirteen SNP-SNP models that were significant in more than one replication cohort after accounting for multiple testing. These results may reveal novel insights into the genetic etiology of lipid levels. Furthermore, we developed a pipeline to perform a computationally efficient interaction analysis with multi-cohort replication.

Twitter Demographics

The data shown below were collected from the profiles of 9 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 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 41 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 22%
Student > Ph. D. Student 8 20%
Professor 5 12%
Student > Master 2 5%
Librarian 2 5%
Other 6 15%
Unknown 9 22%
Readers by discipline Count As %
Agricultural and Biological Sciences 7 17%
Biochemistry, Genetics and Molecular Biology 7 17%
Medicine and Dentistry 5 12%
Computer Science 5 12%
Immunology and Microbiology 1 2%
Other 5 12%
Unknown 11 27%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 14 August 2017.
All research outputs
#2,944,289
of 11,609,687 outputs
Outputs from BioData Mining
#99
of 216 outputs
Outputs of similar age
#81,824
of 265,613 outputs
Outputs of similar age from BioData Mining
#6
of 11 outputs
Altmetric has tracked 11,609,687 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 216 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.2. This one has gotten more attention than average, scoring higher than 54% 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 265,613 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 68% of its contemporaries.
We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.