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Record W4416038162 · doi:10.1038/s10038-025-01430-1

Whole-genome sequencing of 3135 individuals representing the genetic diversity of the Japanese population

2025· article· en· W4416038162 on OpenAlexaff
Koichiro Higasa, Takahisa Kawaguchi, Shuji Kawaguchi, Saori Sakaue, Ta‐Yu Yang, Yukinori Okada, Yukihide Momozawa, Izumi Yamaguchi, Dominic Nelson, Simon Gravel, Yoshinori Murakami, Ryo Yamada, Keitaro Matsuo, Yoshihisa Yamano, Changhoon Kim, Jeong‐Sun Seo, Michiaki Kubo, Fumihiko Matsuda

Bibliographic record

VenueJournal of Human Genetics · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsMcGill University and Génome Québec Innovation CentreMcGill Genome Centre
FundersJapan Society for the Promotion of ScienceJapan Agency for Medical Research and Development
KeywordsImputation (statistics)Genetic diversityIdentity by descentGenetic variantsHaplotypeGenetic variationDiversity (politics)Population geneticsHuman genetic variationMinor allele frequency

Abstract

fetched live from OpenAlex

Whole-genome sequence information currently available for large-scale sequencing studies is biased toward European descent populations. Such bias causes difficulties in identifying disease-associated genetic variations in non-European populations, including the Japanese. Here, to comprehensively identify genetic variants, we sequenced 3135 individuals representing the genetic diversity of the Japanese population. Of the 44,757,785 identified variants, 31.0% exhibiting a minor allele frequency of <1% were novel. Using these variants, we constructed a reference haplotype and graph-structured reference sequence to facilitate accurate imputation and variant characterization. Our findings suggest that integrating genetic variations from ethnically diverse populations into the prevailing catalogs is essential to achieve precision medicine for all populations.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.255
Threshold uncertainty score0.272

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.025
GPT teacher head0.285
Teacher spread0.260 · 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 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

Citations1
Published2025
Admission routes1
Has abstractyes

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