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Genetic Determinants of Lipid Traits in Diverse Populations from the Population Architecture using Genomics and Epidemiology (PAGE) Study

Overview of attention for article published in PLoS Genetics, June 2011
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (75th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (54th percentile)

Mentioned by

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7 tweeters

Citations

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118 Dimensions

Readers on

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91 Mendeley
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2 CiteULike
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Title
Genetic Determinants of Lipid Traits in Diverse Populations from the Population Architecture using Genomics and Epidemiology (PAGE) Study
Published in
PLoS Genetics, June 2011
DOI 10.1371/journal.pgen.1002138
Pubmed ID
Authors

Logan Dumitrescu, Cara L. Carty, Kira Taylor, Fredrick R. Schumacher, Lucia A. Hindorff, José L. Ambite, Garnet Anderson, Lyle G. Best, Kristin Brown-Gentry, Petra Bůžková, Christopher S. Carlson, Barbara Cochran, Shelley A. Cole, Richard B. Devereux, Dave Duggan, Charles B. Eaton, Myriam Fornage, Nora Franceschini, Jeff Haessler, Barbara V. Howard, Karen C. Johnson, Sandra Laston, Laurence N. Kolonel, Elisa T. Lee, Jean W. MacCluer, Teri A. Manolio, Sarah A. Pendergrass, Miguel Quibrera, Ralph V. Shohet, Lynne R. Wilkens, Christopher A. Haiman, Loïc Le Marchand, Steven Buyske, Charles Kooperberg, Kari E. North, Dana C. Crawford

Abstract

For the past five years, genome-wide association studies (GWAS) have identified hundreds of common variants associated with human diseases and traits, including high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglyceride (TG) levels. Approximately 95 loci associated with lipid levels have been identified primarily among populations of European ancestry. The Population Architecture using Genomics and Epidemiology (PAGE) study was established in 2008 to characterize GWAS-identified variants in diverse population-based studies. We genotyped 49 GWAS-identified SNPs associated with one or more lipid traits in at least two PAGE studies and across six racial/ethnic groups. We performed a meta-analysis testing for SNP associations with fasting HDL-C, LDL-C, and ln(TG) levels in self-identified European American (~20,000), African American (~9,000), American Indian (~6,000), Mexican American/Hispanic (~2,500), Japanese/East Asian (~690), and Pacific Islander/Native Hawaiian (~175) adults, regardless of lipid-lowering medication use. We replicated 55 of 60 (92%) SNP associations tested in European Americans at p<0.05. Despite sufficient power, we were unable to replicate ABCA1 rs4149268 and rs1883025, CETP rs1864163, and TTC39B rs471364 previously associated with HDL-C and MAFB rs6102059 previously associated with LDL-C. Based on significance (p<0.05) and consistent direction of effect, a majority of replicated genotype-phentoype associations for HDL-C, LDL-C, and ln(TG) in European Americans generalized to African Americans (48%, 61%, and 57%), American Indians (45%, 64%, and 77%), and Mexican Americans/Hispanics (57%, 56%, and 86%). Overall, 16 associations generalized across all three populations. For the associations that did not generalize, differences in effect sizes, allele frequencies, and linkage disequilibrium offer clues to the next generation of association studies for these traits.

Twitter Demographics

The data shown below were collected from the profiles of 7 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 91 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 3%
Italy 1 1%
France 1 1%
Denmark 1 1%
Switzerland 1 1%
Unknown 84 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 22 24%
Researcher 21 23%
Other 8 9%
Student > Doctoral Student 8 9%
Student > Bachelor 7 8%
Other 24 26%
Unknown 1 1%
Readers by discipline Count As %
Agricultural and Biological Sciences 33 36%
Medicine and Dentistry 23 25%
Unspecified 10 11%
Biochemistry, Genetics and Molecular Biology 10 11%
Computer Science 2 2%
Other 12 13%
Unknown 1 1%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 2011.
All research outputs
#2,893,860
of 12,091,105 outputs
Outputs from PLoS Genetics
#2,777
of 6,176 outputs
Outputs of similar age
#2,799,401
of 11,444,922 outputs
Outputs of similar age from PLoS Genetics
#2,742
of 6,064 outputs
Altmetric has tracked 12,091,105 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 6,176 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.6. 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 11,444,922 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 75% of its contemporaries.
We're also able to compare this research output to 6,064 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.