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The complex genetics of gait speed: genome-wide meta-analysis approach

Overview of attention for article published in Aging, January 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)
  • Good Attention Score compared to outputs of the same age and source (72nd percentile)

Mentioned by

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3 news outlets
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6 X users
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1 Facebook page

Citations

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22 Dimensions

Readers on

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117 Mendeley
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Title
The complex genetics of gait speed: genome-wide meta-analysis approach
Published in
Aging, January 2017
DOI 10.18632/aging.101151
Pubmed ID
Authors

Dan Ben-Avraham, David Karasik, Joe Verghese, Kathryn L. Lunetta, Jennifer A. Smith, John D. Eicher, Rotem Vered, Joris Deelen, Alice M. Arnold, Aron S. Buchman, Toshiko Tanaka, Jessica D. Faul, Maria Nethander, Myriam Fornage, Hieab H. Adams, Amy M. Matteini, Michele L. Callisaya, Albert V. Smith, Lei Yu, Philip L. De Jager, Denis A. Evans, Vilmundur Gudnason, Albert Hofman, Alison Pattie, Janie Corley, Lenore J. Launer, Davis S. Knopman, Neeta Parimi, Stephen T. Turner, Stefania Bandinelli, Marian Beekman, Danielle Gutman, Lital Sharvit, Simon P. Mooijaart, David C. Liewald, Jeanine J. Houwing-Duistermaat, Claes Ohlsson, Matthijs Moed, Vincent J. Verlinden, Dan Mellström, Jos N. van der Geest, Magnus Karlsson, Dena Hernandez, Rebekah McWhirter, Yongmei Liu, Russell Thomson, Gregory J. Tranah, Andre G. Uitterlinden, David R. Weir, Wei Zhao, John M. Starr, Andrew D. Johnson, M. Arfan Ikram, David A. Bennett, Steven R. Cummings, Ian J. Deary, Tamara B. Harris, Sharon L. R. Kardia, Thomas H. Mosley, Velandai K. Srikanth, Beverly G. Windham, Ann B. Newman, Jeremy D. Walston, Gail Davies, Daniel S. Evans, Eline P. Slagboom, Luigi Ferrucci, Douglas P. Kiel, Joanne M. Murabito, Gil Atzmon

Abstract

Emerging evidence suggests that the basis for variation in late-life mobility is attributable, in part, to genetic factors, which may become increasingly important with age. Our objective was to systematically assess the contribution of genetic variation to gait speed in older individuals. We conducted a meta-analysis of gait speed GWASs in 31,478 older adults from 17 cohorts of the CHARGE consortium, and validated our results in 2,588 older adults from 4 independent studies. We followed our initial discoveries with network and eQTL analysis of candidate signals in tissues. The meta-analysis resulted in a list of 536 suggestive genome wide significant SNPs in or near 69 genes. Further interrogation with Pathway Analysis placed gait speed as a polygenic complex trait in five major networks. Subsequent eQTL analysis revealed several SNPs significantly associated with the expression of PRSS16, WDSUB1 and PTPRT, which in addition to the meta-analysis and pathway suggested that genetic effects on gait speed may occur through synaptic function and neuronal development pathways. No genome-wide significant signals for gait speed were identified from this moderately large sample of older adults, suggesting that more refined physical function phenotypes will be needed to identify the genetic basis of gait speed in aging.

X Demographics

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.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 117 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 117 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 19 16%
Student > Master 14 12%
Student > Bachelor 13 11%
Student > Ph. D. Student 11 9%
Professor 7 6%
Other 22 19%
Unknown 31 26%
Readers by discipline Count As %
Medicine and Dentistry 22 19%
Nursing and Health Professions 16 14%
Biochemistry, Genetics and Molecular Biology 12 10%
Neuroscience 8 7%
Agricultural and Biological Sciences 7 6%
Other 11 9%
Unknown 41 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 27. 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 17 March 2021.
All research outputs
#1,344,427
of 24,450,293 outputs
Outputs from Aging
#297
of 3,890 outputs
Outputs of similar age
#28,674
of 430,377 outputs
Outputs of similar age from Aging
#12
of 44 outputs
Altmetric has tracked 24,450,293 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,890 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.1. This one has done particularly well, scoring higher than 92% 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 430,377 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 93% of its contemporaries.
We're also able to compare this research output to 44 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 72% of its contemporaries.