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A large genome-wide association study of age-related macular degeneration highlights contributions of rare and common variants

Overview of attention for article published in Nature Genetics, December 2015
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

20 news outlets
3 blogs
57 tweeters
2 Facebook pages
1 Wikipedia page


545 Dimensions

Readers on

565 Mendeley
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A large genome-wide association study of age-related macular degeneration highlights contributions of rare and common variants
Published in
Nature Genetics, December 2015
DOI 10.1038/ng.3448
Pubmed ID

Lars G Fritsche, Wilmar Igl, Jessica N Cooke Bailey, Felix Grassmann, Sebanti Sengupta, Jennifer L Bragg-Gresham, Kathryn P Burdon, Scott J Hebbring, Cindy Wen, Mathias Gorski, Ivana K Kim, David Cho, Donald Zack, Eric Souied, Hendrik P N Scholl, Elisa Bala, Kristine E Lee, David J Hunter, Rebecca J Sardell, Paul Mitchell, Joanna E Merriam, Valentina Cipriani, Joshua D Hoffman, Tina Schick, Yara T E Lechanteur, Robyn H Guymer, Matthew P Johnson, Yingda Jiang, Chloe M Stanton, Gabriëlle H S Buitendijk, Xiaowei Zhan, Alan M Kwong, Alexis Boleda, Matthew Brooks, Linn Gieser, Rinki Ratnapriya, Kari E Branham, Johanna R Foerster, John R Heckenlively, Mohammad I Othman, Brendan J Vote, Helena Hai Liang, Emmanuelle Souzeau, Ian L McAllister, Timothy Isaacs, Janette Hall, Stewart Lake, David A Mackey, Ian J Constable, Jamie E Craig, Terrie E Kitchner, Zhenglin Yang, Zhiguang Su, Hongrong Luo, Daniel Chen, Hong Ouyang, Ken Flagg, Danni Lin, Guanping Mao, Henry Ferreyra, Klaus Stark, Claudia N von Strachwitz, Armin Wolf, Caroline Brandl, Guenther Rudolph, Matthias Olden, Margaux A Morrison, Denise J Morgan, Matthew Schu, Jeeyun Ahn, Giuliana Silvestri, Evangelia E Tsironi, Kyu Hyung Park, Lindsay A Farrer, Anton Orlin, Alexander Brucker, Mingyao Li, Christine A Curcio, Saddek Mohand-Saïd, José-Alain Sahel, Isabelle Audo, Mustapha Benchaboune, Angela J Cree, Christina A Rennie, Srinivas V Goverdhan, Michelle Grunin, Shira Hagbi-Levi, Peter Campochiaro, Nicholas Katsanis, Frank G Holz, Frédéric Blond, Hélène Blanché, Jean-François Deleuze, Robert P Igo, Barbara Truitt, Neal S Peachey, Stacy M Meuer, Chelsea E Myers, Emily L Moore, Ronald Klein, Michael A Hauser, Eric A Postel, Monique D Courtenay, Stephen G Schwartz, Jaclyn L Kovach, William K Scott, Gerald Liew, Ava G Tan, Bamini Gopinath, John C Merriam, R Theodore Smith, Jane C Khan, Humma Shahid, Anthony T Moore, J Allie McGrath, Reneé Laux, Milam A Brantley, Anita Agarwal, Lebriz Ersoy, Albert Caramoy, Thomas Langmann, Nicole T M Saksens, Eiko K de Jong, Carel B Hoyng, Melinda S Cain, Andrea J Richardson, Tammy M Martin, John Blangero, Daniel E Weeks, Bal Dhillon, Cornelia M van Duijn, Kimberly F Doheny, Jane Romm, Caroline C W Klaver, Caroline Hayward, Michael B Gorin, Michael L Klein, Paul N Baird, Anneke I den Hollander, Sascha Fauser, John R W Yates, Rando Allikmets, Jie Jin Wang, Debra A Schaumberg, Barbara E K Klein, Stephanie A Hagstrom, Itay Chowers, Andrew J Lotery, Thierry Léveillard, Kang Zhang, Murray H Brilliant, Alex W Hewitt, Anand Swaroop, Emily Y Chew, Margaret A Pericak-Vance, Margaret DeAngelis, Dwight Stambolian, Jonathan L Haines, Sudha K Iyengar, Bernhard H F Weber, Gonçalo R Abecasis, Iris M Heid


Advanced age-related macular degeneration (AMD) is the leading cause of blindness in the elderly, with limited therapeutic options. Here we report on a study of >12 million variants, including 163,714 directly genotyped, mostly rare, protein-altering variants. Analyzing 16,144 patients and 17,832 controls, we identify 52 independently associated common and rare variants (P < 5 × 10(-8)) distributed across 34 loci. Although wet and dry AMD subtypes exhibit predominantly shared genetics, we identify the first genetic association signal specific to wet AMD, near MMP9 (difference P value = 4.1 × 10(-10)). Very rare coding variants (frequency <0.1%) in CFH, CFI and TIMP3 suggest causal roles for these genes, as does a splice variant in SLC16A8. Our results support the hypothesis that rare coding variants can pinpoint causal genes within known genetic loci and illustrate that applying the approach systematically to detect new loci requires extremely large sample sizes.

Twitter Demographics

The data shown below were collected from the profiles of 57 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 565 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 <1%
Denmark 1 <1%
Australia 1 <1%
Netherlands 1 <1%
Italy 1 <1%
Korea, Republic of 1 <1%
Thailand 1 <1%
Japan 1 <1%
United States 1 <1%
Other 0 0%
Unknown 555 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 122 22%
Student > Ph. D. Student 102 18%
Student > Bachelor 63 11%
Student > Master 50 9%
Student > Doctoral Student 44 8%
Other 113 20%
Unknown 71 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 154 27%
Medicine and Dentistry 123 22%
Agricultural and Biological Sciences 108 19%
Neuroscience 15 3%
Computer Science 14 2%
Other 55 10%
Unknown 96 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 194. 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 15 November 2020.
All research outputs
of 16,258,614 outputs
Outputs from Nature Genetics
of 6,513 outputs
Outputs of similar age
of 371,249 outputs
Outputs of similar age from Nature Genetics
of 66 outputs
Altmetric has tracked 16,258,614 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,513 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 34.2. This one has done particularly well, scoring higher than 96% 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 371,249 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 66 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 93% of its contemporaries.