↓ Skip to main content

Genome-Wide Association Study of Metabolic Traits Reveals Novel Gene-Metabolite-Disease Links

Overview of attention for article published in PLoS Genetics, February 2014
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (89th percentile)
  • Good Attention Score compared to outputs of the same age and source (74th percentile)

Mentioned by

1 blog
8 tweeters


56 Dimensions

Readers on

148 Mendeley
2 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.
Genome-Wide Association Study of Metabolic Traits Reveals Novel Gene-Metabolite-Disease Links
Published in
PLoS Genetics, February 2014
DOI 10.1371/journal.pgen.1004132
Pubmed ID

Rico Rueedi, Mirko Ledda, Andrew W. Nicholls, Reza M. Salek, Pedro Marques-Vidal, Edgard Morya, Koichi Sameshima, Ivan Montoliu, Laeticia Da Silva, Sebastiano Collino, François-Pierre Martin, Serge Rezzi, Christoph Steinbeck, Dawn M. Waterworth, Gérard Waeber, Peter Vollenweider, Jacques S. Beckmann, Johannes Le Coutre, Vincent Mooser, Sven Bergmann, Ulrich K. Genick, Zoltán Kutalik


Metabolic traits are molecular phenotypes that can drive clinical phenotypes and may predict disease progression. Here, we report results from a metabolome- and genome-wide association study on (1)H-NMR urine metabolic profiles. The study was conducted within an untargeted approach, employing a novel method for compound identification. From our discovery cohort of 835 Caucasian individuals who participated in the CoLaus study, we identified 139 suggestively significant (P<5×10(-8)) and independent associations between single nucleotide polymorphisms (SNP) and metabolome features. Fifty-six of these associations replicated in the TasteSensomics cohort, comprising 601 individuals from São Paulo of vastly diverse ethnic background. They correspond to eleven gene-metabolite associations, six of which had been previously identified in the urine metabolome and three in the serum metabolome. Our key novel findings are the associations of two SNPs with NMR spectral signatures pointing to fucose (rs492602, P = 6.9×10(-44)) and lysine (rs8101881, P = 1.2×10(-33)), respectively. Fine-mapping of the first locus pinpointed the FUT2 gene, which encodes a fucosyltransferase enzyme and has previously been associated with Crohn's disease. This implicates fucose as a potential prognostic disease marker, for which there is already published evidence from a mouse model. The second SNP lies within the SLC7A9 gene, rare mutations of which have been linked to severe kidney damage. The replication of previous associations and our new discoveries demonstrate the potential of untargeted metabolomics GWAS to robustly identify molecular disease markers.

Twitter Demographics

The data shown below were collected from the profiles of 8 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 148 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 2%
Switzerland 3 2%
United Kingdom 2 1%
Qatar 1 <1%
Canada 1 <1%
Russia 1 <1%
Spain 1 <1%
Japan 1 <1%
Germany 1 <1%
Other 0 0%
Unknown 134 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 43 29%
Researcher 40 27%
Student > Master 18 12%
Professor > Associate Professor 11 7%
Professor 7 5%
Other 16 11%
Unknown 13 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 59 40%
Biochemistry, Genetics and Molecular Biology 29 20%
Medicine and Dentistry 16 11%
Chemistry 8 5%
Nursing and Health Professions 3 2%
Other 16 11%
Unknown 17 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 14. 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 10 March 2017.
All research outputs
of 12,796,263 outputs
Outputs from PLoS Genetics
of 6,454 outputs
Outputs of similar age
of 187,301 outputs
Outputs of similar age from PLoS Genetics
of 193 outputs
Altmetric has tracked 12,796,263 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,454 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.1. This one has done well, scoring higher than 77% 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 187,301 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 89% of its contemporaries.
We're also able to compare this research output to 193 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 74% of its contemporaries.