Evidence map›Paper›PMID 30255815›Full record

ArticleBMC medical genomics2018

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

Jason E Miller, Manu K Shivakumar, Younghee Lee, Seonggyun Han, Emrin Horgousluoglu, Shannon L Risacher, Andrew J Saykin, Kwangsik Nho, Dokyoon Kim, Alzheimer’s Disease Neuroimaging Initiative

Open access · goldAbstract read
In one paragraph

Article in BMC medical genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.6field-weighted citation impact, top 31% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 16 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Review
  7. Transcriptomics in Alzheimer's Disease: Aspects and Challenges.International journal of molecular sciences · 2020
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 4 institutions in 1 country.

Jason E MillerBiomedical and Translational Informatics Institute, Geisinger Health System, Danville, PA, USA.
Manu K ShivakumarBiomedical and Translational Informatics Institute, Geisinger Health System, Danville, PA, USA.
Younghee LeeDepartment of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT, 84106, USA.
Seonggyun HanDepartment of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT, 84106, USA.
Emrin HorgousluogluDepartment of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA.
Shannon L RisacherDepartment of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA.
Andrew J SaykinDepartment of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA.
Kwangsik NhoDepartment of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA. knho@iupui.edu.
Dokyoon KimBiomedical and Translational Informatics Institute, Geisinger Health System, Danville, PA, USA. dkim@geisinger.edu.
Alzheimer’s Disease Neuroimaging Initiative
Indiana University School of MedicineUniversity of Utah · USGeisinger Health System · USPennsylvania State University · US

Funding

Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0M
National Centralized Repository for Alzheimer's Disease and Related Dementias (NCRAD)U24AG021886 · NIA · INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS · PI TATIANA M. FOROUD · 2002 to 2026
$119.8M
Postdoctoral Training Program in Genomic MedicineT32HG009495 · NHGRI · UNIVERSITY OF PENNSYLVANIA · PI Katherine L. Nathanson, Bogdan Pasaniuc · 2017 to 2026
$4.2M
Integrating Neuroimaging, Multi-omics, and Clinical Data in Complex DiseaseR01LM012535 · NLM · INDIANA UNIVERSITY INDIANAPOLIS · PI NHO, KWANGSIK TIMOTHY · 2017 to 2021
$1.7M
Neurogenesis in Adult Brain: Gene Networks and Alzheimer’s DiseaseR03AG054936 · NIA · INDIANA UNIVERSITY INDIANAPOLIS · PI NHO, KWANGSIK TIMOTHY · 2017 to 2018
$145k
CIHRNHGRI NIH HHS T32 HG009495NIA NIH HHS R03 AG054936NIA NIH HHS U01 AG024904NIA NIH HHS U24 AG021886NLM NIH HHS R01 LM012535
6 · The paper itself

Abstract

backgroundAlzheimer'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.

methodsIn 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).

resultsThe 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.

conclusionBased 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.

Indexed as

Polymorphism, Single NucleotideRegulatory Sequences, Nucleic AcidAlzheimer DiseaseBiomarkersBrainCohort StudiesComputational BiologyGlucoseHumansNeuroimagingPhenotypeRNA SplicingVesicular Transport ProteinsWhole Genome SequencingBiomarkersEXOC4 protein, humanGlucoseVesicular Transport ProteinsAlternative splicingAlzheimer’s diseaseImaging genomicsRare variantsWhole genome sequencing

Identifiers

PMID30255815
PMCPMC6156983
OpenAlexW2891009494

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.