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Record W4390195142 · doi:10.1002/alz.078351

Association of low‐frequency and rare variants with cognition in genetic frontotemporal dementia: Results from GENFI

2023· article· en· W4390195142 on OpenAlexaff
Saira Saeed Mirza, Andrew D. Patterson, Maria Carmela Tartaglia, Sara Mitchell, Sandra E. Black, Morris Freedman, David F. Tang‐Wai, Ekaterina Rogaeva, David M. Cash, Martina Bocchetta, John C. van Swieten, Robert Laforce, Fabrizio Tagliavini, Barbara Borroni, Daniela Galimberti, James B. Rowe, Caroline Graff, Elizabeth Finger, Sandro Sorbi, Alexandre de Mendonça, Christopher Butler, Alexander Gerhard, Raquel Sánchez‐Valle, Fermín Moreno, Matthis Synofzik, Rik Vandenberghe, Simon Ducharme, Johannes Levin, Adrian Danek, Markus Otto, Isabel Santana, Jonathan D. Rohrer, Mario Masellis

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsMcGill UniversityWestern UniversityUniversité LavalOntario Brain InstituteBaycrest HospitalHealth Sciences CentreOccupational Cancer Research CentreSunnybrook HospitalUniversity of TorontoSickKids FoundationSunnybrook Health Science Centre
Fundersnot available
KeywordsFrontotemporal dementiaGeneticsDementiaMinor allele frequencyAlleleC9orf72PopulationFrameshift mutationAllele frequencyBiologyPsychologyMedicineMutationDiseaseGeneInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background Frontotemporal dementia (FTD) is a neurodegenerative disorder characterized by heterogeneous clinical, pathological, and genetic features. Mutations in three genes account for the majority of autosomal dominant FTD: GRN, MAPT, and C9orf72. We tested whether gene‐based aggregate burden of genome‐wide low frequency variants contribute to variation in executive function, memory, and language performance in the GENetic Frontotemporal dementia Initiative (GENFI), after controlling for effects of causative mutations. Method GENFI recruits symptomatic and presymptomatic participants from families segregating genetic FTD. We included 565 participants with genotype (Neurochip; imputed against TOPMed), and neuropsychological data. Gene‐based burden tests that aggregate the number of rare alleles by gene, were used to examine the association of low‐frequency variants (minor allele frequency: 0.000001 to <0.05) with factor scores of executive function, memory, and language, controlling for age, sex, education, mutation status, population stratification, and family membership (kinship matrix) in multiple linear regression models. Cognitive factor scores were derived from Confirmatory Factor Analyses. In two separate analyses, we used the following set of annotations to account for (i) loss of function mutations (LOF): start gain, stop loss, start loss, essential splice site, stop gain, normal splice site, and non‐synonymous, (ii) insertions, deletions, and frameshift mutations (indel‐fs). Result Of the 565 participants (316 women), 307 were mutation carriers (symptomatic = 108). For LOF mutations, aggregate burden of variants in FAM183A reached statistical significance (741 genes tested; significance threshold: p = 0.05/731 = 6.7×10−5) for executive function (standardized β:4.2; p = 6.0×10−5), whereas IPO13 (standardized β: ‐1.7; p = 3.1×10−5) and FAAHP1 (standardized β: ‐1.7; p = 1.8×10−5) were significant for memory. For the indel‐fs mutations, HMGB4 was associated with worse memory function (standardized β: ‐0.95; p = 5.9×10−6). No genes were significantly associated with language. [Gene info: (i) FAM183A‐codes a ciliary‐base protein; location1p34.2; associated with autosomal recessive intellectual disability. (ii) IPO13‐a nuclear transporter gene; location 1p34.1; associated with Partington syndrome and Agenesis corpus callosum with abnormal genitalia. (iii) HMGB4‐a transcription regulator gene; location 1p35.1]. Conclusion Identification of low frequency variants contributing to disease phenotypes may help identify genetic modifiers of familial FTD. Replication of results followed by functional studies are required.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.016
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.274
Teacher spread0.244 · 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
Published2023
Admission routes1
Has abstractyes

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