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Record W4292847057 · doi:10.1073/pnas.2202764119

Genome-wide analyses of individual differences in quantitatively assessed reading- and language-related skills in up to 34,000 people

2022· article· en· W4292847057 on OpenAlexafffund
Else Eising, Nazanin Mirza‐Schreiber, Eveline L. de Zeeuw, Carol A. Wang, Dongnhu T. Truong, Andrea G. Allegrini, Chin Yang Shapland, Gu Zhu, Karen Wigg, Margot Gerritse, Barbara Molz, Gökberk Alagöz, Alessandro Gialluisi, Filippo Abbondanza, Kaili Rimfeld, Marjolein van Donkelaar, Zhijie Liao, Philip R. Jansen, Till F. M. Andlauer, Timothy C. Bates, Manon Bernard, Kirsten Blokland, Milene Bonte, Anders D. Børglum, Thomas Bourgeron, Daniel Brandeis, Fabiola Ceroni, Valéria Csépe, Philip S. Dale, Peter F. de Jong, John C. DeFries, Jean‐François Démonet, Ditte Demontis, Yu Feng, Scott D. Gordon, Sharon Guger, Marianna E. Hayiou‐Thomas, Juan Hernández, Jouke‐Jan Hottenga, Charles Hulme, Juha Kere, Elizabeth N. Kerr, Tanner Koomar, Karin Landerl, Gabriel Leonard, Maureen W. Lovett, Heikki Lyytinen, Nicholas G. Martin, Angela Martinelli, Urs Maurer, Jacob J. Michaelson, Kristina Moll, Anthony P. Monaco, Angela Morgan, Markus M. Nöthen, Zdenka Pausová, Craig E. Pennell, Bruce F. Pennington, Kaitlyn M. Price, Veera M. Rajagopal, Franck Ramus, Louis Richer, Nuala H. Simpson, Shelley D. Smith, Maggie Snowling, John Stein, Lisa J. Strug, Joel B. Talcott, Henning Tiemeier, Marc P. van der Schroeff, Ellen Verhoef, Kate E. Watkins, Margaret Wilkinson, Margaret J. Wright, Cathy L. Barr, Dorret I. Boomsma, Manuel Carreiras, Marie-Christine Franken, Jeffrey R. Gruen, Michelle Luciano, Bertram Müller‐Myhsok, Dianne F. Newbury, Richard K. Olson, Silvia Paracchini, Tomáš Paus, Robert Plomin, Sheena Reilly, Gerd Schulte‐Körne, J. Bruce Tomblin, Elsje van Bergen, Andrew Whitehouse, Erik G. Willcutt, Beaté St Pourcain, Clyde Francks, Simon E. Fisher

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2022
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-JustineUniversité du Québec à ChicoutimiUniversity Health NetworkUniversity of TorontoSickKids FoundationHospital for Sick ChildrenMcGill University
FundersKoninklijke Nederlandse Akademie van WetenschappenNational Institute of Child Health and Human DevelopmentNational Institute of Neurological Disorders and StrokeNational Institute on AgingEdith Cowan UniversityNational Health and Medical Research CouncilNational Center for Advancing Translational SciencesMedical Research CouncilCanadian Institutes of Health ResearchHeart And Stroke Foundation Of QuebecH. Lundbeck A/SUniversity of BristolHospital for Sick ChildrenFondazione Umberto VeronesiNational Institute on Drug AbuseGovernment of Western AustraliaNovo NordiskAvera Institute for Human GeneticsMurdoch UniversityNederlandse Organisatie voor Wetenschappelijk OnderzoekLundbeckfondenEusko JaurlaritzaNovo Nordisk FondenHeart and Stroke Foundation of CanadaUniversiteit van AmsterdamNational Institutes of HealthZonMwUniversity of Notre Dame AustraliaSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Institute on Deafness and Other Communication DisordersCurtin University of TechnologyNational Institute of Mental HealthAarhus UniversitetEuropean CommissionManton FoundationAustralian GovernmentAgence Nationale de la RechercheWellcome TrustAustrian Science FundUniversity of Notre DameNational Science FoundationMax-Planck-GesellschaftRoyal SocietyRaine Medical Research FoundationEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentSimons Foundation Autism Research InitiativeWomen and Infants Research FoundationChildren's Hospital of Philadelphia
KeywordsReading (process)PsychologyLinguisticsComputer scienceNatural language processingPhilosophy

Abstract

fetched live from OpenAlex

The use of spoken and written language is a fundamental human capacity. Individual differences in reading- and language-related skills are influenced by genetic variation, with twin-based heritability estimates of 30 to 80% depending on the trait. The genetic architecture is complex, heterogeneous, and multifactorial, but investigations of contributions of single-nucleotide polymorphisms (SNPs) were thus far underpowered. We present a multicohort genome-wide association study (GWAS) of five traits assessed individually using psychometric measures (word reading, nonword reading, spelling, phoneme awareness, and nonword repetition) in samples of 13,633 to 33,959 participants aged 5 to 26 y. We identified genome-wide significant association with word reading (rs11208009, P = 1.098 × 10 −8 ) at a locus that has not been associated with intelligence or educational attainment. All five reading-/language-related traits showed robust SNP heritability, accounting for 13 to 26% of trait variability. Genomic structural equation modeling revealed a shared genetic factor explaining most of the variation in word/nonword reading, spelling, and phoneme awareness, which only partially overlapped with genetic variation contributing to nonword repetition, intelligence, and educational attainment. A multivariate GWAS of word/nonword reading, spelling, and phoneme awareness maximized power for follow-up investigation. Genetic correlation analysis with neuroimaging traits identified an association with the surface area of the banks of the left superior temporal sulcus, a brain region linked to the processing of spoken and written language. Heritability was enriched for genomic elements regulating gene expression in the fetal brain and in chromosomal regions that are depleted of Neanderthal variants. Together, these results provide avenues for deciphering the biological underpinnings of uniquely human traits.

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)0.003
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
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 head0.060
GPT teacher head0.379
Teacher spread0.319 · 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

Citations133
Published2022
Admission routes2
Has abstractyes

Explore more

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