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Record W4210468311 · doi:10.1126/science.abe8457

Discovery of genomic loci of the human cerebral cortex using genetically informed brain atlases

2022· article· en· W4210468311 on OpenAlexfundno aff
Carolina Makowski, Dennis van der Meer, Weixiu Dong, Hao Wang, Yan Wu, Jingjing Zou, Cin Liu, Donald J. Hagler, Chun Chieh Fan, William S. Kremen, Ole A. Andreassen, Terry L. Jernigan, Anders M. Dale, Kun Zhang, Peter M. Visscher, Jian Yang, Chi‐Hua Chen

Bibliographic record

VenueScience · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBioinformatics and Genomic Networks
Canadian institutionsnot available
FundersNational Institute on AgingNational Health and Medical Research CouncilNational Institute of Mental HealthMedical Research CouncilFonds de Recherche du Québec - SantéUniversity of California, San DiegoNational Institutes of HealthCanadian Institutes of Health ResearchNational Institute on Drug AbuseNorges Forskningsråd
KeywordsBiologyCerebral cortexComputational biologyHuman brainNeuroscienceGenetically modified organismNeuroimagingGeneticsGene

Abstract

fetched live from OpenAlex

To determine the impact of genetic variants on the brain, we used genetically informed brain atlases in genome-wide association studies of regional cortical surface area and thickness in 39,898 adults and 9136 children. We uncovered 440 genome-wide significant loci in the discovery cohort and 800 from a post hoc combined meta-analysis. Loci in adulthood were largely captured in childhood, showing signatures of negative selection, and were linked to early neurodevelopment and pathways associated with neuropsychiatric risk. Opposing gradations of decreased surface area and increased thickness were associated with common inversion polymorphisms. Inferior frontal regions, encompassing Broca's area, which is important for speech, were enriched for human-specific genomic elements. Thus, a mixed genetic landscape of conserved and human-specific features is concordant with brain hierarchy and morphogenetic gradients.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.193
Threshold uncertainty score0.255

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.011
GPT teacher head0.254
Teacher spread0.243 · 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 designBench or experimental
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

Citations72
Published2022
Admission routes1
Has abstractyes

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