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Record W4293063745 · doi:10.1038/s41380-022-01710-8

Genome-wide meta-analyses reveal novel loci for verbal short-term memory and learning

2022· article· en· W4293063745 on OpenAlexaff
Jari Lahti, Samuli Tuominen, Qiong Yang, Giulio Pergola, Shahzad Ahmad, Najaf Amin, Nicola J. Armstrong, Alexa Beiser, Katharina Bey, Joshua C. Bis, Eric Boerwinkle, Jan Bressler, Archie Campbell, Harry Campbell, Qiang Chen, Janie Corley, Simon R. Cox, Gail Davies, Philip L. De Jager, Eske M. Derks, Jessica D. Faul, Annette L. Fitzpatrick, Alison E. Fohner, Ian Ford, Myriam Fornage, Zachary F. Gerring, Hans J. Grabe, Francine Grodstein, Vilmundur Guðnason, Eleanor M. Simonsick, Peter K. Joshi, Eero Kajantie, Jaakko Kaprio, Pauliina Karell, Luca Kleineidam, Maria J. Knol, Nicole A. Kochan, John B. Kwok, Markus Leber, Max Lam, Teresa Lee, Shuo Li, Anu Loukola, Tobias Luck, Riccardo E. Marioni, Karen A. Mather, Sarah E. Medland, Saira Saeed Mirza, Mike A. Nalls, Kwangsik Nho, Adrienne O’Donnell, Christopher Oldmeadow, Jodie N. Painter, Alison Pattie, Simone Reppermund, Shannon L. Risacher, Richard J. Rose, Vijay Sadashivaiah, Markus Scholz, Claudia L. Satizábal, Peter W. Schofield, Katharina E. Schraut, Rodney J. Scott, Jeannette Simino, Albert V. Smith, Jennifer A. Smith, David J. Stott, Ida Surakka, Alexander Teumer, Anbupalam Thalamuthu, Stella Trompet, Stephen T. Turner, Sven J. van der Lee, Arno Villringer, Uwe Völker, R. J. Wilson, Katharina Wittfeld, Eero Vuoksimaa, Rui Xia, Kristine Yaffe, Lei Yu, Habil Zare, Wei Zhao, David Ames, John Attia, David A. Bennett, Henry Brodaty, Daniel I. Chasman, Aaron L. Goldman, Caroline Hayward, M. Arfan Ikram, J. Wouter Jukema, Sharon L. R. Kardia, Todd Lencz, Markus Loeffler, Venkata S. Mattay, Aarno Palotie, Bruce M. Psaty, Alfredo Ramı́rez, Paul M. Ridker, Steffi G. Riedel‐Heller, Perminder S. Sachdev, Andrew J. Saykin, Martin Scherer, Peter R. Schofield, Stephen Sidney, John M. Starr, Julian N. Trollor, William S. Ulrich, Michael Wagner, David R. Weir, James F. Wilson, Margaret J. Wright, Daniel R. Weinberger, Stéphanie Debette, Johan G. Eriksson, Thomas H. Mosley, Lenore J. Launer, Cornelia M. van Duijn, Ian J. Deary, Sudha Seshadri, Katri Räikkönen

Bibliographic record

VenueMolecular Psychiatry · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersNational Center for Research ResourcesNational Institute on Alcohol Abuse and AlcoholismNational Cancer InstituteWellcome TrustNational Human Genome Research InstituteNational Heart, Lung, and Blood InstituteNational Institute of Neurological Disorders and StrokeMedical Research CouncilU.S. National Library of MedicineNational Institute on AgingBiotechnology and Biological Sciences Research CouncilNovo Nordisk Fonden
KeywordsNeurocognitiveVerbal memoryVerbal learningWorking memoryGenome-wide association studyPsychologyBiologyGeneticsNeuroscienceCognitionSingle-nucleotide polymorphismGenotypeGene

Abstract

fetched live from OpenAlex

Understanding the genomic basis of memory processes may help in combating neurodegenerative disorders. Hence, we examined the associations of common genetic variants with verbal short-term memory and verbal learning in adults without dementia or stroke (N = 53,637). We identified novel loci in the intronic region of CDH18, and at 13q21 and 3p21.1, as well as an expected signal in the APOE/APOC1/TOMM40 region. These results replicated in an independent sample. Functional and bioinformatic analyses supported many of these loci and further implicated POC1. We showed that polygenic score for verbal learning associated with brain activation in right parieto-occipital region during working memory task. Finally, we showed genetic correlations of these memory traits with several neurocognitive and health outcomes. Our findings suggest a role of several genomic loci in verbal memory processes.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.007
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
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 head0.043
GPT teacher head0.316
Teacher spread0.273 · 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

Citations20
Published2022
Admission routes1
Has abstractyes

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