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Rare variants in the splicing regulatory elements of EXOC3L4 are associated with brain glucose metabolism in Alzheimer’s disease

Overview of attention for article published in BMC Medical Genomics, September 2018
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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 (81st percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

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1 news outlet
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3 tweeters

Citations

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10 Dimensions

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31 Mendeley
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Title
Rare variants in the splicing regulatory elements of EXOC3L4 are associated with brain glucose metabolism in Alzheimer’s disease
Published in
BMC Medical Genomics, September 2018
DOI 10.1186/s12920-018-0390-6
Pubmed ID
Authors

Jason E. Miller, Manu K. Shivakumar, Younghee Lee, Seonggyun Han, Emrin Horgousluoglu, Shannon L. Risacher, Andrew J. Saykin, Kwangsik Nho, Dokyoon Kim

Abstract

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases that causes problems related to brain function. To some extent it is understood on a molecular level how AD arises, however there are a lack of biomarkers that can be used for early diagnosis. Two popular methods to identify AD-related biomarkers use genetics and neuroimaging. Genes and neuroimaging phenotypes have provided some insights as to the potential for AD biomarkers. While the field of imaging-genomics has identified genetic features associated with structural and functional neuroimaging phenotypes, it remains unclear how variants that affect splicing could be important for understanding the genetic etiology of AD. In this study, rare variants (minor allele frequency < 0.01) in splicing regulatory element (SRE) loci from whole genome sequencing (WGS) in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, were used to identify genes that are associated with global brain cortical glucose metabolism in AD measured by FDG PET-scans. Gene-based associated analyses of rare variants were performed using the program BioBin and the optimal Sequence Kernel Association Test (SKAT-O). The gene, EXOC3L4, was identified as significantly associated with global cortical glucose metabolism (FDR (false discovery rate) corrected p < 0.05) using SRE coding variants only. Three loci that may affect splicing within EXOC3L4 contribute to the association. Based on sequence homology, EXOC3L4 is likely a part of the exocyst complex. Our results suggest the possibility that variants which affect proper splicing of EXOC3L4 via SREs may impact vesicle transport, giving rise to AD related phenotypes. Overall, by utilizing WGS and functional neuroimaging we have identified a gene significantly associated with an AD related endophenotype, potentially through a mechanism that involves splicing.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 31 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 16%
Other 4 13%
Student > Master 4 13%
Student > Bachelor 3 10%
Student > Ph. D. Student 3 10%
Other 4 13%
Unknown 8 26%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 23%
Medicine and Dentistry 6 19%
Neuroscience 3 10%
Agricultural and Biological Sciences 2 6%
Immunology and Microbiology 1 3%
Other 2 6%
Unknown 10 32%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 26 August 2022.
All research outputs
#2,767,360
of 22,103,655 outputs
Outputs from BMC Medical Genomics
#114
of 1,183 outputs
Outputs of similar age
#55,278
of 297,044 outputs
Outputs of similar age from BMC Medical Genomics
#1
of 9 outputs
Altmetric has tracked 22,103,655 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,183 research outputs from this source. They receive a mean Attention Score of 4.5. This one has done particularly well, scoring higher than 90% 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 297,044 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 81% of its contemporaries.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them