MétaCan
Menu
Back to cohort
Record W4297991585 · doi:10.1101/2022.09.28.22280461

Exploring the Genetic and Genomic Connection Underlying Neurodegeneration with Brain Iron Accumulation and the Risk for Parkinson’s Disease

2022· preprint· en· W4297991585 on OpenAlexaff
Pilar Álvarez Jerez, José Luis Alcantud, Lucía de los Reyes‐Ramírez, Anni Moore, Clara Ruz, Francisco Vives Montero, Noela Rodríguez-Losada, Prabhjyot Saini, Ziv Gan‐Or, Chelsea X. Alvarado, Mary B. Makarious, Kimberley J. Billingsley, Cornelis Blauwendraat, Alastair J. Noyce, Andrew Singleton, Raquel Durán, Sara Bandrés‐Ciga

Bibliographic record

VenuemedRxiv · 2022
Typepreprint
Languageen
FieldNeuroscience
TopicNeurological diseases and metabolism
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersMedical Research CouncilMichael J. Fox Foundation for Parkinson's Research
KeywordsNeurodegenerationDiseaseGeneticsGeneParkinson's diseaseBiologyExpression quantitative trait lociMedicineInternal medicineSingle-nucleotide polymorphismGenotype

Abstract

fetched live from OpenAlex

Abstract Background Neurodegeneration with brain iron accumulation (NBIA) represents a group of neurodegenerative disorders characterized by abnormal iron accumulation and the presence of axonal spheroids in the brain. In Parkinson’s Disease (PD), iron accumulation is a cardinal feature of degenerating regions in the brain and seems to be a key player in mechanisms that precipitate cell death. Objectives The aim of this present study was to comprehensively explore the genetic and genomic connection between NBIA and PD etiology. Methods We screened the presence of known and rare pathogenic mutations in autosomal dominant and recessive genes linked to NBIA in a total of 4,481 PD cases and 10,253 controls from the Accelerating Medicines Partnership Parkinsons’ Disease Program and the UKBiobank. We further examined whether a genetic burden of NBIA variants contributes to PD risk through single-gene, gene-set, and single-variant association analyses. To investigate the potential effect of NBIA gene expression on PD, we assessed publicly available expression quantitative trait loci (eQTL) data through Summary-based Mendelian Randomization and conducted transcriptomic analyses in blood of 1,886 PD cases and 1,285 controls. Results Out of 28 previously reported NBIA screened coding variants, four missense were found to be associated with PD risk at a nominal p value < 0.05 (p.T402M- ATP13A2 , p.T207M- FA2H , p.P60L- C19orf12 , p.C422S- PANK2 ). No enrichment of heterozygous variants in NBIA-related genes risk was identified in PD cases versus controls. Burden analyses did not reveal a cumulative effect of rare NBIA genetic variation on PD risk. Transcriptomic analyses suggested that DCAF17 is differentially expressed in blood from PD cases and controls. Conclusions Taking into account the very low mutation occurrence in the datasets and the lack of replication, our analyses suggest that NBIA and PD may be separate molecular entities, supporting the notion that the mechanisms underpinning iron accumulation in PD are likely not shared with NBIA. Elevated nigral iron levels may not contribute to PD etiology and may vary with anti-parkinsonian drugs used for treatment, environmental factors, or iron sequestration in tissue as a response to PD pathological change.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.168
GPT teacher head0.305
Teacher spread0.137 · 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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venuemedRxivSame topicNeurological diseases and metabolismFrench-language works237,207