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The hairless gene in androgenetic alopecia: results of a systematic mutation screening and a family-based association approach

2002· article· en· W1993371097 on OpenAlexaff
Axel M. Hillmer, Roland Kruse, Fabìo Macciardi, Uwe Heyn, Regina C. Betz, Thomas Ruzicka, Peter Propping, Markus M. Nöthen, Sven Cichon

Bibliographic record

VenueBritish Journal of Dermatology · 2002
Typearticle
Languageen
FieldMedicine
TopicHair Growth and Disorders
Canadian institutionsUniversity of Toronto
FundersDeutsche Forschungsgemeinschaft
KeywordsGeneticsMissense mutationMale-pattern baldnessHairlessBiologyExonMutationHair lossScalpSingle-nucleotide polymorphismGeneInternal medicineMedicineGenotypeDermatology

Abstract

fetched live from OpenAlex

BACKGROUND: Genetic disposition and androgen dependence are important characteristics of the common patterned loss of scalp hair known as androgenetic alopecia (AGA). The genetic factors contributing to AGA are currently unknown. The human hairless gene (HR) has recently been cloned and mutations have been reported in families with autosomal recessive universal congenital alopecia and papular atrichia. The main feature of these disorders is persistent complete absence of hair at or shortly after birth. This suggests that HR is essential and specific for the development of hair. OBJECTIVES: To test the hypothesis that HR may be involved in AGA. METHODS: We systematically screened HR for genetic variability by means of single-strand conformation analysis (SSCA) in 46 unrelated men with AGA. To test for an involvement of HR in the development of AGA, seven common variants were genotyped in 61 families with 93 affected offspring. The results were analysed with the transmission/disequilibrium test (TDT). RESULTS: SSCA showed 15 single nucleotide substitutions: eight missense mutations, four silent mutations and three mutations in exon-flanking intronic sequences. TDT results showed a marginally significant association between AGA and variants 3379-29G/T (P = 0.024) and 2611-68C/T (P = 0.047). These results, however, did not remain significant after applying the conservative Bonferroni correction for multiple testing. CONCLUSIONS: Our results do not provide evidence for a strong involvement of HR in the development of AGA, although a minor role cannot be fully excluded.

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)0.001
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 score0.207
Threshold uncertainty score0.248

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.018
GPT teacher head0.228
Teacher spread0.210 · 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

Citations18
Published2002
Admission routes1
Has abstractyes

Explore more

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