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Record W2170392185 · doi:10.1002/humu.20777

Further evidence for allelic heterogeneity in Hartnup disorder

2008· article· en· W2170392185 on OpenAlexaffabout
Dimitar N. Azmanov, Sonja Kowalczuk, Helen Rodgers, Christiane Auray‐Blais, Robert Giguère, John E.J. Rasko, Stefan Bröer, Juleen A. Cavanaugh

Bibliographic record

VenueHuman Mutation · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAmino Acid Enzymes and Metabolism
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsBiologyGeneticsAllelePhenotypeAminoaciduriaGeneAllelic heterogeneityMutationGenetic heterogeneityAmino acid

Abstract

fetched live from OpenAlex

Hartnup disorder is an autosomal recessive impairment of amino acid transport in kidney and intestine. Mutations in SLC6A19 have been shown to cosegregate with the disease in the predicted recessive manner; however, in two previous studies (Seow et al., Nat Genet 2004;36:1003-1007; Kleta et al., Nat Genet 2004;36:999-1002), not all causative alleles were identified in all affected individuals, raising the possibility that other genes may contribute to Hartnup disorder. We have now investigated six newly acquired families of Australian and Canadian (Province of Quebec) origin and resequenced the entire coding region of SLC6A19 in families with only a single disease allele identified. We also studied one American family in whom no mutations had been identified in a previous study (Kleta et al., Nat Genet 2004;36:999-1002). We have identified seven novel mutations in SLC6A19 that show functional obliteration of the protein in vitro, explaining Hartnup disorder in all reported families so far. We demonstrate that Hartnup disorder is allelically heterogeneous with two mutated SLC6A19 alleles, whether identical or not, necessary for manifestation of the characteristic aminoaciduria in affected individuals. This study resolves the previous hypothesis that other genes contribute to the Hartnup phenotype.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.756
Threshold uncertainty score0.293

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 studies0.0000.000
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 head0.068
GPT teacher head0.343
Teacher spread0.276 · 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

Citations36
Published2008
Admission routes2
Has abstractyes

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