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Genome-wide significant, replicated and functional risk variants for Alzheimer’s disease

Overview of attention for article published in Journal of Neural Transmission, August 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

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1 blog
twitter
2 tweeters

Citations

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3 Dimensions

Readers on

mendeley
16 Mendeley
Title
Genome-wide significant, replicated and functional risk variants for Alzheimer’s disease
Published in
Journal of Neural Transmission, August 2017
DOI 10.1007/s00702-017-1773-0
Pubmed ID
Authors

Xiaoyun Guo, Wenying Qiu, Rolando Garcia-Milian, Xiandong Lin, Yong Zhang, Yuping Cao, Yunlong Tan, Zhiren Wang, Jing Shi, Jijun Wang, Dengtang Liu, Lisheng Song, Yifeng Xu, Xiaoping Wang, Na Liu, Tao Sun, Jianming Zheng, Justine Luo, Huihao Zhang, Jianying Xu, Longli Kang, Chao Ma, Kesheng Wang, Xingguang Luo

Abstract

Genome-wide association studies (GWASs) have reported numerous associations between risk variants and Alzheimer's disease (AD). However, these associations do not necessarily indicate a causal relationship. If the risk variants can be demonstrated to be biologically functional, the possibility of a causal relationship would be increased. In this article, we reviewed all of the published GWASs to extract the genome-wide significant (p < 5×10(-8)) and replicated associations between risk variants and AD or AD-biomarkers. The regulatory effects of these risk variants on the expression of a novel class of non-coding RNAs (piRNAs) and protein-coding RNAs (mRNAs), the alteration of proteins caused by these variants, the associations between AD and these variants in our own sample, the expression of piRNAs, mRNAs and proteins in human brains targeted by these variants, the expression correlations between the risk genes and APOE, the pathways and networks that the risk genes belonged to, and the possible long non-coding RNAs (LncRNAs) that might regulate the risk genes were analyzed, to investigate the potential biological functions of the risk variants and explore the potential mechanisms underlying the SNP-AD associations. We found replicated and significant associations for AD or AD-biomarkers, surprisingly, only at 17 SNPs located in 11 genes/snRNAs/LncRNAs in eight genomic regions. Most of these 17 SNPs enriched some AD-related pathways or networks, and were potentially functional in regulating piRNAs and mRNAs; some SNPs were associated with AD in our sample, and some SNPs altered protein structures. Most of the protein-coding genes regulated by the risk SNPs were expressed in human brain and correlated with APOE expression. We conclude that these variants were most robust risk markers for AD, and their contributions to AD risk was likely to be causal. As expected, APOE and the lipoprotein metabolism pathway possess the highest weight among these contributions.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 4 25%
Student > Master 3 19%
Professor > Associate Professor 2 13%
Student > Bachelor 2 13%
Researcher 2 13%
Other 3 19%
Readers by discipline Count As %
Unspecified 6 38%
Biochemistry, Genetics and Molecular Biology 4 25%
Agricultural and Biological Sciences 3 19%
Neuroscience 2 13%
Economics, Econometrics and Finance 1 6%
Other 0 0%

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 08 July 2019.
All research outputs
#1,882,797
of 13,226,239 outputs
Outputs from Journal of Neural Transmission
#144
of 1,223 outputs
Outputs of similar age
#54,138
of 265,997 outputs
Outputs of similar age from Journal of Neural Transmission
#10
of 30 outputs
Altmetric has tracked 13,226,239 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,223 research outputs from this source. They receive a mean Attention Score of 4.5. This one has done well, scoring higher than 88% 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 265,997 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 79% of its contemporaries.
We're also able to compare this research output to 30 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 66% of its contemporaries.