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Record W4386046143 · doi:10.1111/acel.13962

Targeted sequencing of the 9p21.3 region reveals association with reduced disease risks in Ashkenazi Jewish centenarians

2023· article· en· W4386046143 on OpenAlexfundno aff
Yizhou Zhu, Seungjin Ryu, Archana Tare, Nir Barzilai, Gil Atzmon, Yousin Suh

Bibliographic record

VenueAging Cell · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsnot available
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute of General Medical SciencesNational Institute on AgingNational Institutes of HealthBuck Institute for Research on AgingEcho FoundationSimons Foundation
KeywordsCentenarianBiologyLocus (genetics)Genome-wide association studyGeneticsAlleleGenotypeDiseaseLongevityGenetic associationCohortSingle-nucleotide polymorphismInternal medicineGeneMedicine

Abstract

fetched live from OpenAlex

Genome-wide association studies (GWAS) have pinpointed the chromosomal locus 9p21.3 as a genetic hotspot for various age-related disorders. Common genetic variants in this locus are linked to multiple traits, including coronary artery diseases, cancers, and diabetes. Centenarians are known for their reduced risk and delayed onset of these conditions. To investigate whether this evasion of disease risks involves diminished genetic risks in the 9p21.3 locus, we sequenced this region in an Ashkenazi Jewish centenarian cohort (centenarians: n = 450, healthy controls: n = 500). Risk alleles associated with cancers, glaucoma, CAD, and T2D showed a significant depletion in centenarians. Furthermore, the risk and non-risk genotypes are linked to two distinct low-frequency variant profiles, enriched in controls and centenarians, respectively. Our findings provide evidence that the extreme longevity cohort is associated with collectively lower risks of multiple age-related diseases in the 9p21.3 locus.

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 score0.122
Threshold uncertainty score0.295

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 science0.0000.000
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 head0.022
GPT teacher head0.256
Teacher spread0.234 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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