↓ Skip to main content

Identification and Functional Characterization of G6PC2 Coding Variants Influencing Glycemic Traits Define an Effector Transcript at the G6PC2-ABCB11 Locus

Overview of attention for article published in PLoS Genetics, January 2015
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age

Mentioned by

twitter
3 X users
facebook
2 Facebook pages

Citations

dimensions_citation
96 Dimensions

Readers on

mendeley
167 Mendeley
citeulike
3 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Identification and Functional Characterization of G6PC2 Coding Variants Influencing Glycemic Traits Define an Effector Transcript at the G6PC2-ABCB11 Locus
Published in
PLoS Genetics, January 2015
DOI 10.1371/journal.pgen.1004876
Pubmed ID
Authors

Anubha Mahajan, Xueling Sim, Hui Jin Ng, Alisa Manning, Manuel A. Rivas, Heather M. Highland, Adam E. Locke, Niels Grarup, Hae Kyung Im, Pablo Cingolani, Jason Flannick, Pierre Fontanillas, Christian Fuchsberger, Kyle J. Gaulton, Tanya M. Teslovich, N. William Rayner, Neil R. Robertson, Nicola L. Beer, Jana K. Rundle, Jette Bork-Jensen, Claes Ladenvall, Christine Blancher, David Buck, Gemma Buck, Noël P. Burtt, Stacey Gabriel, Anette P. Gjesing, Christopher J. Groves, Mette Hollensted, Jeroen R. Huyghe, Anne U. Jackson, Goo Jun, Johanne Marie Justesen, Massimo Mangino, Jacquelyn Murphy, Matt Neville, Robert Onofrio, Kerrin S. Small, Heather M. Stringham, Ann-Christine Syvänen, Joseph Trakalo, Goncalo Abecasis, Graeme I. Bell, John Blangero, Nancy J. Cox, Ravindranath Duggirala, Craig L. Hanis, Mark Seielstad, James G. Wilson, Cramer Christensen, Ivan Brandslund, Rainer Rauramaa, Gabriela L. Surdulescu, Alex S. F. Doney, Lars Lannfelt, Allan Linneberg, Bo Isomaa, Tiinamaija Tuomi, Marit E. Jørgensen, Torben Jørgensen, Johanna Kuusisto, Matti Uusitupa, Veikko Salomaa, Timothy D. Spector, Andrew D. Morris, Colin N. A. Palmer, Francis S. Collins, Karen L. Mohlke, Richard N. Bergman, Erik Ingelsson, Lars Lind, Jaakko Tuomilehto, Torben Hansen, Richard M. Watanabe, Inga Prokopenko, Josee Dupuis, Fredrik Karpe, Leif Groop, Markku Laakso, Oluf Pedersen, Jose C. Florez, Andrew P. Morris, David Altshuler, James B. Meigs, Michael Boehnke, Mark I. McCarthy, Cecilia M. Lindgren, Anna L. Gloyn

Abstract

Genome wide association studies (GWAS) for fasting glucose (FG) and insulin (FI) have identified common variant signals which explain 4.8% and 1.2% of trait variance, respectively. It is hypothesized that low-frequency and rare variants could contribute substantially to unexplained genetic variance. To test this, we analyzed exome-array data from up to 33,231 non-diabetic individuals of European ancestry. We found exome-wide significant (P<5×10-7) evidence for two loci not previously highlighted by common variant GWAS: GLP1R (p.Ala316Thr, minor allele frequency (MAF)=1.5%) influencing FG levels, and URB2 (p.Glu594Val, MAF = 0.1%) influencing FI levels. Coding variant associations can highlight potential effector genes at (non-coding) GWAS signals. At the G6PC2/ABCB11 locus, we identified multiple coding variants in G6PC2 (p.Val219Leu, p.His177Tyr, and p.Tyr207Ser) influencing FG levels, conditionally independent of each other and the non-coding GWAS signal. In vitro assays demonstrate that these associated coding alleles result in reduced protein abundance via proteasomal degradation, establishing G6PC2 as an effector gene at this locus. Reconciliation of single-variant associations and functional effects was only possible when haplotype phase was considered. In contrast to earlier reports suggesting that, paradoxically, glucose-raising alleles at this locus are protective against type 2 diabetes (T2D), the p.Val219Leu G6PC2 variant displayed a modest but directionally consistent association with T2D risk. Coding variant associations for glycemic traits in GWAS signals highlight PCSK1, RREB1, and ZHX3 as likely effector transcripts. These coding variant association signals do not have a major impact on the trait variance explained, but they do provide valuable biological insights.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 167 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 2 1%
Finland 1 <1%
France 1 <1%
Germany 1 <1%
Unknown 162 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 41 25%
Student > Ph. D. Student 21 13%
Professor 15 9%
Professor > Associate Professor 10 6%
Student > Master 10 6%
Other 33 20%
Unknown 37 22%
Readers by discipline Count As %
Medicine and Dentistry 38 23%
Biochemistry, Genetics and Molecular Biology 30 18%
Agricultural and Biological Sciences 28 17%
Nursing and Health Professions 5 3%
Psychology 4 2%
Other 20 12%
Unknown 42 25%
Attention Score in Context

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 01 July 2015.
All research outputs
#15,983,384
of 25,732,188 outputs
Outputs from PLoS Genetics
#6,617
of 8,996 outputs
Outputs of similar age
#197,724
of 362,796 outputs
Outputs of similar age from PLoS Genetics
#121
of 174 outputs
Altmetric has tracked 25,732,188 research outputs across all sources so far. This one is in the 36th percentile – i.e., 36% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,996 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 17.8. This one is in the 24th percentile – i.e., 24% of its peers scored the same or lower than it.
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 362,796 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 174 others from the same source and published within six weeks on either side of this one. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.