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Attention Score in Context
| Title |
Identifying gene–gene interactions that are highly associated with four quantitative lipid traits across multiple cohorts
|
|---|---|
| Published in |
Human Genetics, November 2016
|
| DOI | 10.1007/s00439-016-1738-7 |
| Pubmed ID | |
| Authors |
Rishika De, Shefali S. Verma, Emily Holzinger, Molly Hall, Amber Burt, David S. Carrell, David R. Crosslin, Gail P. Jarvik, Helena Kuivaniemi, Iftikhar J. Kullo, Leslie A. Lange, Matthew B. Lanktree, Eric B. Larson, Kari E. North, Alex P. Reiner, Vinicius Tragante, Gerard Tromp, James G. Wilson, Folkert W. Asselbergs, Fotios Drenos, Jason H. Moore, Marylyn D. Ritchie, Brendan Keating, Diane Gilbert-Diamond |
| Abstract |
Genetic loci explain only 25-30 % of the heritability observed in plasma lipid traits. Epistasis, or gene-gene interactions may contribute to a portion of this missing heritability. Using the genetic data from five NHLBI cohorts of 24,837 individuals, we combined the use of the quantitative multifactor dimensionality reduction (QMDR) algorithm with two SNP-filtering methods to exhaustively search for SNP-SNP interactions that are associated with HDL cholesterol (HDL-C), LDL cholesterol (LDL-C), total cholesterol (TC) and triglycerides (TG). SNPs were filtered either on the strength of their independent effects (main effect filter) or the prior knowledge supporting a given interaction (Biofilter). After the main effect filter, QMDR identified 20 SNP-SNP models associated with HDL-C, 6 associated with LDL-C, 3 associated with TC, and 10 associated with TG (permutation P value <0.05). With the use of Biofilter, we identified 2 SNP-SNP models associated with HDL-C, 3 associated with LDL-C, 1 associated with TC and 8 associated with TG (permutation P value <0.05). In an independent dataset of 7502 individuals from the eMERGE network, we replicated 14 of the interactions identified after main effect filtering: 11 for HDL-C, 1 for LDL-C and 2 for TG. We also replicated 23 of the interactions found to be associated with TG after applying Biofilter. Prior knowledge supports the possible role of these interactions in the genetic etiology of lipid traits. This study also presents a computationally efficient pipeline for analyzing data from large genotyping arrays and detecting SNP-SNP interactions that are not primarily driven by strong main effects. |
X Demographics
The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 33% |
| Unknown | 4 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 67% |
| Scientists | 2 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 2% |
| Unknown | 42 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 9 | 21% |
| Student > Ph. D. Student | 8 | 19% |
| Student > Bachelor | 6 | 14% |
| Student > Doctoral Student | 3 | 7% |
| Professor | 3 | 7% |
| Other | 7 | 16% |
| Unknown | 7 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 14 | 33% |
| Biochemistry, Genetics and Molecular Biology | 7 | 16% |
| Medicine and Dentistry | 7 | 16% |
| Nursing and Health Professions | 2 | 5% |
| Computer Science | 1 | 2% |
| Other | 4 | 9% |
| Unknown | 8 | 19% |
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 28 July 2017.
All research outputs
#5,674,782
of 22,901,818 outputs
Outputs from Human Genetics
#725
of 2,955 outputs
Outputs of similar age
#84,143
of 306,450 outputs
Outputs of similar age from Human Genetics
#9
of 14 outputs
Altmetric has tracked 22,901,818 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,955 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. This one has done well, scoring higher than 75% 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 306,450 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 72% of its contemporaries.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.