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Record W3128690417 · doi:10.1001/jamaneurol.2020.5257

Identification of Candidate Parkinson Disease Genes by Integrating Genome-Wide Association Study, Expression, and Epigenetic Data Sets

2021· article· en· W3128690417 on OpenAlexfundno aff
Demis A. Kia, David Zhang, Sebastian Guelfi, Claudia Manzoni, Leon Hubbard, Regina H. Reynolds, Juan A. Botía, Mina Ryten, Raffaele Ferrari, Patrick A. Lewis, Nigel Williams, Daniah Trabzuni, John Hardy, Nicholas Wood, Alastair J. Noyce, Rauan Kaiyrzhanov, Ben Middlehurst, Manuela Tan, Henry Houlden, Huw R. Morris, Hélène Plun‐Favreau, Peter Holmans, José Brás, John P. Quinn, Kin Y. Mok, Kerri J. Kinghorn, Kimberley J. Billingsley, Sebastian R. Schreglmann, Rita Guerreiro, Ruth C. Lovering, Lea R’Bibo, Mie Rizig, Valentina Escott‐Price, Viorica Chelban, Thomas Foltynie, Alexis Brice, Fabrice Danjou, Suzanne Lesage, Jean‐Christophe Corvol, María Martínez, Claudia Schulte, Kathrin Brockmann, Javier Simón‐Sánchez, Peter Heutink, Patrizia Rizzu, Manu Sharma, Thomas Gasser, Aude Nicolas, Mark Cookson, Sara Bandrés‐Ciga, Cornelis Blauwendraat, David W. Craig, Faraz Faghri, J. Raphael Gibbs, Dena Hernández, Kendall Van Keuren‐Jensen, Joshua Shulman, Hampton L. Leonard, Mike A. Nalls, Laurie Robak, Steven Lubbe, Steven Finkbeiner, Niccolò E. Mencacci, Codrin Lungu, Andrew Singleton, Sonja W. Scholz, Xylena Reed, Roy N. Alcalay, Ziv Gan‐Or, Guy A. Rouleau, Lynne Krohn, Jacobus J. van Hilten, Johan Marinus, Astrid Adarmes‐Gómez, Miquel Aguilar, Ignacio Álvarez, Victoria Álvarez, Francisco Javier Barrero, Jesús Alberto Bergareche Yarza, Inmaculada Bernal‐Bernal, Marta Blázquez Estrada, Marta Bonilla‐Toribio, María Teresa Boungiorno, Dolores Buiza‐Rueda, Anna Maria Novella Càmara, Fátima Carrillo, Mario Carrión‐Claro, Debora Cerdan, Jordi Clarimón, Yaroslau Compta, Mónica Díez-Fairén, Oriol Dols‐Icardo, J. Duarte, Raquel Durán, Francisco Escamilla‐Sevilla, Mario Ezquerra, Cici Feliz, Manel Fernández, Rubén Fernández‐Santiago, Ciara García, Pedro Ruiz, Pilar Gómez‐Garre, María José Gómez Heredia, Isabel González Aramburu, Ana Gorostidi Pagola, Janet Hoenicka, Jon Infante, Adriano Jiménez‐Escrig, Jaime Kulisevsky, Miguel A. Labrador‐Espinosa, José Luis López-Sendón, Adolfo López de Munaín Arregui, Daniel Macías, Irene Martínez‐Torres, Juan Marín‐Lahoz, Marı́a José Martı́, Juan Carlos Martínez‐Castrillo, Carlota Méndez‐del‐Barrio, Manuel Menéndez‐González, Marina Mata Adolfo Mínguez, Pablo Mir, Elisabet Mondragón Rezola, Esteban Muñoz, Javier Pagonabarraga, Pau Pástor, Francisco Pérez Errazquin, María Teresa Periñán, Javier Ruiz‐Martínez, Clara Ruz, A Rodríguez, María Sierra, Esther Suárez-Sanmartín, César Tabernero, Juan Pablo Tartari, Cristina Tejera‐Parrado, Eduard Tolosa, Francesc Valldeoriola, Laura Vargas‐González, Lydia Vela, Francisco Vives, Alexander Zimprich, Lasse Pihlstrøm, Mathias Toft, Sulev Kõks, Pille Taba, Sharon Hassin‐Baer, Michael E. Weale, Adaikalavan Ramasamy, Colin Smith, Manuel Sebastian Guelfi, Karishma D’Sa, Paola Forabosco

Bibliographic record

VenueJAMA Neurology · 2021
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
FundersNational Institute of Environmental Health SciencesNational Institute of Neurological Disorders and StrokeMedical Research CouncilAssistance publique-Hôpitaux de ParisLandspítali HáskólasjúkrahúsMcGill UniversityDeutsche ForschungsgemeinschaftWellcome TrustUniversity College LondonParkinson's UKFondation de FranceKing Faisal Specialist Hospital and Research CentreBundesministerium für Bildung und ForschungNational Institute on AgingNational Institute for Health and Care ResearchMultiple System Atrophy CoalitionAgence Nationale de la RechercheAlzheimer's SocietyCanada First Research Excellence FundAmerican Parkinson Disease AssociationConsortium canadien en neurodégénérescence associée au vieillissementUniversity of DundeeParkinson VerenigingMichael J. Fox Foundation for Parkinson's ResearchU.S. Department of Health and Human ServicesNational Institutes of HealthRosetrees TrustU.S. Department of DefenseEU Joint Programme – Neurodegenerative Disease Research
KeywordsGenome-wide association studyBiologyGeneticsGeneCandidate geneEpigeneticsGenetic associationTranscriptomeComputational biologyDNA methylationGene expressionAlternative splicingSingle-nucleotide polymorphismGenotypeExon

Abstract

fetched live from OpenAlex

Importance: Substantial genome-wide association study (GWAS) work in Parkinson disease (PD) has led to the discovery of an increasing number of loci shown reliably to be associated with increased risk of disease. Improved understanding of the underlying genes and mechanisms at these loci will be key to understanding the pathogenesis of PD. Objective: To investigate what genes and genomic processes underlie the risk of sporadic PD. Design and Setting: This genetic association study used the bioinformatic tools Coloc and transcriptome-wide association study (TWAS) to integrate PD case-control GWAS data published in 2017 with expression data (from Braineac, the Genotype-Tissue Expression [GTEx], and CommonMind) and methylation data (derived from UK Parkinson brain samples) to uncover putative gene expression and splicing mechanisms associated with PD GWAS signals. Candidate genes were further characterized using cell-type specificity, weighted gene coexpression networks, and weighted protein-protein interaction networks. Main Outcomes and Measures: It was hypothesized a priori that some genes underlying PD loci would alter PD risk through changes to expression, splicing, or methylation. Candidate genes are presented whose change in expression, splicing, or methylation are associated with risk of PD as well as the functional pathways and cell types in which these genes have an important role. Results: Gene-level analysis of expression revealed 5 genes (WDR6 [OMIM 606031], CD38 [OMIM 107270], GPNMB [OMIM 604368], RAB29 [OMIM 603949], and TMEM163 [OMIM 618978]) that replicated using both Coloc and TWAS analyses in both the GTEx and Braineac expression data sets. A further 6 genes (ZRANB3 [OMIM 615655], PCGF3 [OMIM 617543], NEK1 [OMIM 604588], NUPL2 [NCBI 11097], GALC [OMIM 606890], and CTSB [OMIM 116810]) showed evidence of disease-associated splicing effects. Cell-type specificity analysis revealed that gene expression was overall more prevalent in glial cell types compared with neurons. The weighted gene coexpression performed on the GTEx data set showed that NUPL2 is a key gene in 3 modules implicated in catabolic processes associated with protein ubiquitination and in the ubiquitin-dependent protein catabolic process in the nucleus accumbens, caudate, and putamen. TMEM163 and ZRANB3 were both important in modules in the frontal cortex and caudate, respectively, indicating regulation of signaling and cell communication. Protein interactor analysis and simulations using random networks demonstrated that the candidate genes interact significantly more with known mendelian PD and parkinsonism proteins than would be expected by chance. Conclusions and Relevance: Together, these results suggest that several candidate genes and pathways are associated with the findings observed in PD GWAS studies.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.005
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.017
GPT teacher head0.279
Teacher spread0.262 · 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 designSimulation or modeling
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".

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Citations203
Published2021
Admission routes1
Has abstractyes

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Same venueJAMA NeurologySame topicParkinson's Disease Mechanisms and TreatmentsFrench-language works237,207