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Record W7073723295

LRRK2 exonic variants and risk of multiple system atrophy

2014· article· en· W7073723295 on OpenAlexaff

Bibliographic record

VenueResearch Portal (King's College London) · 2014
Typearticle
Languageen
FieldMedicine
TopicPregnancy and preeclampsia studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLRRK2Series (stratigraphy)AtrophyHaplotypeGenetic associationGene
DOInot available

Abstract

fetched live from OpenAlex

Objective: The aim of this study was to evaluate the association between common exonic variants in the leucine-rich repeat kinase 2 (LRRK2) gene and risk of multiple system atrophy (MSA). Methods: One series from the United States (92 patients with pathologically confirmed MSA, 416 controls) and a second series from the United Kingdom (85 patients with pathologically confirmed MSA, 352 controls) were included in this case-control study.We supplemented these data with those of 53 patients from the United States with clinically probable or possible MSA. Seventeen common LRRK2 exonic variants were genotyped and assessed for association withMSA. Results: In the combined series of 177 patients with pathologically confirmed MSA and 768 controls, there was a significant association between LRRK2 p.M2397T and MSA (odds ratio [OR] = 0.60, p = 0.002). This protective effect was observed more strongly in the US series (OR = 0.46, p = 0.0008) than the UK series (OR = 0.82, p = 0.41). We observed other noteworthy associations with MSA for p.G1624G (OR = 0.63, p = 0.006) and p.N2081D (OR = 0.15, p = 0.010). The p.G1624G-M2397T haplotype was significantly associated with MSA in the US series (p <0.0001) and combined series (p = 0.003) but not the UK series (p = 0.67). Results were consistent when additionally including the US patients with clinical MSA, where the strongest singlevariant association was again observed for p.M2397T (OR = 0.59, p = 0.0005). Conclusions: These findings provide evidence that LRRK2 exonic variants may contribute to susceptibility to MSA. Validation in other series and meta-analytic studies will be important.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.061
Threshold uncertainty score0.774

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.033
GPT teacher head0.308
Teacher spread0.274 · 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

Citations5
Published2014
Admission routes1
Has abstractyes

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