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Record W3126149304 · doi:10.1212/nxg.0000000000000557

Genome-Wide Association Study Meta-Analysis for Parkinson Disease Motor Subtypes

2021· article· en· W3126149304 on OpenAlexfundno aff
Isabel Alfradique‐Dunham, Rami Al‐Ouran, Rainer von Coelln, Cornelis Blauwendraat, Emily J. Hill, Lan Luo, Emily Young, Anita Kaw, Manuela Tan, Calwing Liao, Dena Hernández, Lasse Pihlstrøm, Donald G. Grosset, Lisa M. Shulman, Zhandong Liu, Guy A. Rouleau, Mike A. Nalls, Andrew Singleton, Huw R. Morris, Joseph Jankovic, Joshua Shulman

Bibliographic record

VenueNeurology Genetics · 2021
Typearticle
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsnot available
FundersInstitute of GeneticsNational Institute of Environmental Health SciencesNational Institute of Neurological Disorders and StrokeMedical Research CouncilNational Institutes of HealthAssistance publique-Hôpitaux de ParisFondation de FranceNational Cancer InstituteLandspítali HáskólasjúkrahúsBundesministerium für Bildung und ForschungUniversity of GlasgowParkinson's UKNational Institute on AgingNational Institute for Health and Care ResearchMultiple System Atrophy CoalitionBiogenHuffington FoundationAgence Nationale de la RechercheSol Goldman Charitable TrustCanada First Research Excellence FundAmerican Parkinson Disease AssociationConsortium canadien en neurodégénérescence associée au vieillissementDeutsche ForschungsgemeinschaftMcGill UniversityDemensförbundetCenter for Individualized Medicine, Mayo ClinicH. Lundbeck A/SBurroughs Wellcome FundLewy Body Dementia AssociationUniversity College LondonWellcome TrustUniversity of DundeeHelsingin YliopistoU.S. Department of DefenseItä-Suomen YliopistoLittle Family FoundationParkinson VerenigingMichael J. Fox Foundation for Parkinson's ResearchMayo ClinicU.S. Department of Health and Human ServicesHelsingin ja Uudenmaan SairaanhoitopiiriEU Joint Programme – Neurodegenerative Disease Research
KeywordsGenome-wide association studyConfidence intervalOdds ratioLogistic regressionInternal medicineDiseaseGenetic modelMeta-analysisMedicineGeneticsBiologyGenotypeSingle-nucleotide polymorphismGene

Abstract

fetched live from OpenAlex

Objective To discover genetic determinants of Parkinson disease (PD) motor subtypes, including tremor dominant (TD) and postural instability/gait difficulty (PIGD) forms. Methods In 3,212 PD cases of European ancestry, we performed a genome-wide association study (GWAS) examining 2 complementary outcome traits derived from the Unified Parkinson9s Disease Rating Scale, including dichotomous motor subtype (TD vs PIGD) or a continuous tremor/PIGD score ratio. Logistic or linear regression models were adjusted for sex, age at onset, disease duration, and 5 ancestry principal components, followed by meta-analysis. Results Among 71 established PD risk variants, we detected multiple suggestive associations with PD motor subtype, including GPNMB (rs199351, psubtype = 0.01, pratio = 0.03), SH3GL2 (rs10756907, psubtype = 0.02, pratio = 0.01), HIP1R (rs10847864, psubtype = 0.02), RIT2 (rs12456492, psubtype = 0.02), and FBRSL1 (rs11610045, psubtype = 0.02). A PD genetic risk score integrating all 71 PD risk variants was also associated with subtype ratio (p = 0.026, ß = −0.04, 95% confidence interval = −0.07–0). Based on top results of our GWAS, we identify a novel suggestive association at the STK32B locus (rs2301857, pratio = 6.6 × 10−7), which harbors an independent risk allele for essential tremor. Conclusions Multiple PD risk alleles may also modify clinical manifestations to influence PD motor subtype. The discovery of a novel variant at STK32B suggests a possible overlap between genetic risk for essential tremor and tremor-dominant PD.

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)0.008
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.014
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0060.033
Bibliometrics0.0030.005
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.057
GPT teacher head0.301
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 designMeta-analysis
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

Citations43
Published2021
Admission routes1
Has abstractyes

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