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Record W4317889762 · doi:10.1101/2023.01.23.525133

Exploring the genetic diversity of the Japanese Population: Insights from a Large-Scale Whole Genome Sequencing Analysis

2023· preprint· en· W4317889762 on OpenAlexfundno aff
Yosuke Kawai, Yusuke Watanabe, Yosuke Omae, Reiko Miyahara, Seik‐Soon Khor, Eisei Noiri, Koji Kitajima, Hideyuki Shimanuki, Hiroyuki Gatanaga, Kenichiro Hata, Kotaro Hattori, Aritoshi Iida, Hatsue Ishibashi-Ueda, Tadashi Kaname, Tatsuya Kanto, Ryo Matsumura, Kengo Miyo, Michio Noguchi, Kouichi Ozaki, Masaya Sugiyama, Ayako Takahashi, Haruhiko Tokuda, Tsutomu Tomita, Akihiro Umezawa, Hiroshi Watanabe, Sumiko Yoshida, Yu‐ichi Goto, Yutaka Maruoka, Yoichi Matsubara, Shumpei Niida, Masashi Mizokami, Katsushi Tokunaga

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2023
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicForensic and Genetic Research
Canadian institutionsnot available
FundersInstitute of GeneticsNational Center of Neurology and PsychiatryNational Center for Child Health and DevelopmentMinistry of Education, Culture, Sports, Science and TechnologyNational Center for Geriatrics and GerontologyNational Cerebral and Cardiovascular CenterNational Center for Global Health and MedicineJapan Agency for Medical Research and Development
KeywordsArchipelagoPopulationBiologyGenetic diversityGeographyDemographic historyShotgun sequencingPopulation genomicsEvolutionary biologyDemographyGenomeEcologyGenomicsGeneticsGene

Abstract

fetched live from OpenAlex

Abstract The Japanese archipelago is a terminal location for human migration, and the contemporary Japanese people represent a unique population whose genomic diversity has been shaped by multiple migrations from Eurasia. Through high-coverage whole-genome sequencing (WGS) analysis of 9,850 samples from the National Center Biobank Network, we analyzed the genomic characteristics that define the genetic makeup of the modern Japanese population from a population genetics perspective. The dataset comprised populations from the Ryukyu Islands and other parts of the Japanese archipelago (Hondo). Low frequency detrimental or pathogenic variants were found in these populations. The Hondo population underwent two episodes of population decline during the Jomon period, corresponding to the Late Neolithic, and the Edo period, corresponding to the Early Modern era, while the Ryukyu population experienced a population decline during the shell midden period of the Late Neolithic in this region. Genes related to alcohol and lipid metabolism were affected by positive natural selection. Two genes related to alcohol metabolism were found to be 12,500 years out of phase with the time when they began to be affected by positive natural selection; this finding indicates that the genomic diversity of Japanese people has been shaped by events closely related to agriculture and food production. Author summary The human population in the Japanese archipelago exhibits significant genetic diversity, with the Ryukyu Islands and other parts of the archipelago (Hondo) having undergone distinct evolutionary paths that have contributed to the genetic divergence of the populations in each region. In this study, whole genome sequencing of healthy individuals from national research hospital biobanks was utilized to investigate the genetic diversity of the Japanese population. Haplotypes were inferred from the genomic data, and a thorough population genetic analysis was conducted. The results indicated not only genetic differentiation between Hondo and the Ryukyu Islands, but also marked differences in past population size. In addition, gene genealogies were inferred from the haplotypes, and the patterns were scrutinized for evidence of natural selection. This analysis revealed unique traces of natural selection in East Asian populations, many of which were believed to be linked to dietary changes brought about by agriculture and food production.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.772
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.003
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 head0.040
GPT teacher head0.234
Teacher spread0.194 · 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.

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

Citations5
Published2023
Admission routes1
Has abstractyes

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