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Record W3212775966 · doi:10.1073/pnas.2112032118

De novo germline mutation in the dual specificity phosphatase 10 gene accelerates autoimmune diabetes

2021· article· en· W3212775966 on OpenAlexaff
Anne‐Perrine Foray, Sophie Candon, Sara Hildebrand, Cindy Marquet, Fabrice Valette, Coralie Pecquet, Sébastien Lemoine, Francina Langa Vives, Michaël Dumas, Peipei Hu, Pere Santamaría, Sylvaine You, Stephen Lyon, Lindsay Scott, Chun Hui Bu, Tao Wang, Darui Xu, Eva Marie Y. Moresco, Claudio Scazzocchio, Jean‐François Bach, Bruce Beutler, Lucienne Chatenoud

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Tyrosine Phosphatases
Canadian institutionsUniversity of Calgary
FundersNational Institute of Allergy and Infectious Diseases
KeywordsBiologyGeneticsGermlineMissing heritability problemAutoimmune diseaseGeneEpigeneticsMutationGenotypeGenetic variantsAntibody

Abstract

fetched live from OpenAlex

Significance The vast majority of autoimmune diseases are polygenic, and causal loci uncovered by genetic-mapping studies explain only a minority of the heritable contribution to trait variation. Multiple explanations for this missing heritability include rare meaningful variants, rare copy number variations or deletions, epistasis, epigenetics, disease heterogeneity, and rare or infrequent variants that segregate within individual families (even within monozygotic twins). Here we demonstrate that experimental models of spontaneous autoimmune diseases may be invaluable tools to map rare germline variants impacting disease susceptibility traits. We identified a variant of the dual-specificity phosphatase 10 encoding gene that accelerates disease in an autoimmune type 1 diabetes model, the nonobese diabetic mouse.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.029
GPT teacher head0.292
Teacher spread0.263 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the National Academy of SciencesSame topicProtein Tyrosine PhosphatasesFrench-language works237,207