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Record W2181472449 · doi:10.1186/s12974-015-0436-z

Epistasis analysis links immune cascades and cerebral amyloidosis

2015· article· en· W2181472449 on OpenAlexafffund
Andréa Lessa Benedet, Aurélie Labbe, Philippe Lemay, Eduardo R. Zimmer, Tharick A. Pascoal, Antoine Leuzy, Sulantha Mathotaarachchi, Sara Mohades, Monica Shin, Alexandre Dionne‐Laporte, Thomas Beaudry, Cynthia Picard, Serge Gauthier, Judes Poirier, Guy A. Rouleau, Pedro Rosa‐Neto

Bibliographic record

VenueJournal of Neuroinflammation · 2015
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsMontreal Neurological Institute and HospitalUniversité de MontréalMcGill University Health CentreDouglas Mental Health University InstituteMcGill University
FundersNational Institute on AgingFonds de Recherche du Québec - SantéNational Institute of Biomedical Imaging and BioengineeringCanadian Institutes of Health ResearchNational Institutes of HealthGenentechIXICOServierEisaiCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorNorthern California Institute for Research and EducationUniversity of California, San DiegoPfizerBiogenBioClinicaF. Hoffmann-La RocheSynarcUniversity of Southern CaliforniaMedpaceNovartis Pharmaceuticals CorporationU.S. Department of DefenseEli Lilly and CompanyBristol-Myers SquibbAlzheimer's Disease Neuroimaging InitiativeMeso Scale DiagnosticsAlzheimer's AssociationFoundation for the National Institutes of Health
KeywordsGenome-wide association studyNeuroinflammationApolipoprotein ESingle-nucleotide polymorphismBiologyInternal medicineMedicineGeneticsGeneImmunologyInflammationDiseaseGenotype

Abstract

fetched live from OpenAlex

BACKGROUND: Several lines of evidence suggest the involvement of neuroinflammatory changes in Alzheimer's disease (AD) pathophysiology such as amyloidosis and neurodegeneration. In fact, genome-wide association studies (GWAS) have shown a link between genes involved in neuroinflammation and AD. In order to further investigate whether interactions between candidate genetic variances coding for neuroinflammatory molecules are associated with brain amyloid β (Aβ) fibrillary accumulation, we conducted an epistasis analysis on a pool of genes associated with molecular mediators of inflammation. METHODS: [(18)F]Florbetapir positron emission tomography (PET) imaging was employed to assess brain Aβ levels in 417 participants from ADNI-GO/2 and posteriorly 174 from ADNI-1. IL-1β, IL4, IL6, IL6r, IL10, IL12, IL18, C5, and C9 genes were chosen based on previous studies conducted in AD patients. Using the [(18)F]florbetapir standardized uptake value ratio (SUVR) as a quantitative measure of fibrillary Aβ, epistasis analyses were performed between two sets of markers of immune-related genes using gender, diagnosis, and apolipoprotein E (APOE) as covariates. Voxel-based analyses were also conducted. The results were corrected for multiple comparison tests. Cerebrospinal fluid (CSF) Aβ1-42/phosphorylated tau (p-tau) ratio concentrations were used to confirm such associations. RESULTS: Epistasis analysis unveiled two significant single nucleotide polymorphism (SNP)-SNP interactions (false discovery rate (FDR) threshold 0.1), both interactions between C9 gene (rs261752) and IL6r gene (rs4240872, rs7514452). In a combined sample, the interactions were confirmed (p ≤ 10-5) and associated with amyloid accumulation within cognitively normal and AD spectrum groups. Voxel-based analysis corroborated initial findings. CSF biomarker (Aβ1-42/p-tau) confirmed the genetic interaction. Additionally, rs4240872 and rs7514452 SNPs were shown to be associated with CSF and plasma concentrations of IL6r protein. CONCLUSIONS: Certain allele combinations involving IL6r and C9 genes are associated with Aβ burden in the brain. Hypothesis-driven search for epistasis is a valuable strategy for investigating imaging endophenotypes in complex neurodegenerative diseases.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.306

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.040
GPT teacher head0.316
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2015
Admission routes2
Has abstractyes

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