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Record W7119537544 · doi:10.1002/alz70856_106121

Rare genetic variants influence regional cortical and subcortical grey matter volumes in genetic frontotemporal dementia: A GENFI Study

2025· article· en· W7119537544 on OpenAlexaff
Saira S. Mirza, Maurice Pasternak, Andrew D. Paterson, Carmela Tartaglia, Sandra E. Black, Sara Mitchell, Morris Freedman, David F. Tang‐Wai, Ekaterina Rogaeva, David M Cash, Martina Bocchetta, John van Swieten, Robert Laforce, B. Borroni, Daniela Galimberti, James Rowe, Caroline Graff, Elizabeth Finger, Sandro Sorbi, Alexandre Mendonca, Christopher R. Butler, Alexander Gerhard, Raquel Sánchez‐Valle, Fermin Moreno, Matthis Synofzik, Rik Vandenberghe, Simon Ducharme, Johannes Levin, Markus Otto, Isabel Santana, Jonathan D. Rohrer

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsMcGill UniversityBaycrest HospitalOntario Brain InstituteHospital for Sick ChildrenKidney Foundation of CanadaUniversity Health NetworkOccupational Cancer Research CentreHealth Sciences CentreSunnybrook HospitalMontreal Neurological Institute and HospitalToronto Dementia Research AllianceWestern UniversityUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsFrontotemporal dementiaGrey matterGenetic heterogeneityAllelePopulationMutationGenetic variationGenetic architecturesplice

Abstract

fetched live from OpenAlex

Abstract Background There is substantial heterogeneity in clinical presentation of genetic Frontotemporal Dementia (FTD), even within the same family. This suggests that additional heritability may exist and contribute to this variable presentation. We examined whether gene‐based aggregate burden of genome‐wide rare variants (minor allele frequency [MAF]: ≤1%) contribute to variation in regional cortical and subcortical grey matter volumes, after controlling for effects of causative mutations in GRN , MAPT , and C9orf72 . Method This study was embedded within the GENetic Frontotemporal dementia Initiative (GENFI), which recruits genetic FTD cases and their asymptomatic at‐risk family members, both carriers and non‐carriers of FTD mutations. We included 518 participants with genotype (Neurochip; imputed against TOPMed), and T1w‐MRI brain volumetric data. Gene‐based burden tests that aggregate the number of rare variants by gene were used to examine the association of rare variants (MAF: ≤1%) with regional cortical and subcortical grey matter volumes (70 regions of interest [ROIs]), controlling for age, sex, total intracranial volume, mutation status, scanner site, population stratification, and family membership (kinship matrix) using RVTests. Annotations for loss of function mutations (LOF): start gain, stop loss, start loss, essential splice site, stop gain, normal splice site, and non‐synonymous. Multiple testing correction accounted for the number of genes and number of independent grey matter volumes as calculated by matSpD ( p ‐value threshold: 0.05/(17,053x42) = 6.98 x10 ‐8 ) . Result Aggregate burden of LOF mutations ( DNAJB8‐AS1, WDR26, RDM1P5, BSND, CNOT2, DDA1, ASAH2B, PPM1A, HOXD13, ALDH1A1, CENATAC, ANKRD45) was associated with significantly lower volumes within the left temporal lobe (ROIs: left temporal and lateral temporal left), and greater volume in the putamen bilaterally ( TSACC) . All genes are protein coding, except the DNAJB8‐AS1 (antisense RNA) and RDM1P5 (pseudogene), and are variably expressed in the brain. Molecular functions of significant genes involve regulation of gene expression, transcription, and cell cycle, ion channel function, and chromosomal segregation. WDR26 and CNOT2 genes have been implicated in neurodevelopment and neurological disorders respectively; BSND gene is involved in neurotransmission. Conclusion Identification of deleterious or protective rare variants contributing to FTD imaging phenotypes may help identify genetic modifiers of familial FTD. Replication in larger cohorts is needed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.027
GPT teacher head0.304
Teacher spread0.276 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractyes

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