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Record W2009551866 · doi:10.1007/s00277-008-0624-3

Mutations in the paralogous human α-globin genes yielding identical hemoglobin variants

2008· article· en· W2009551866 on OpenAlexaff
Kamran Moradkhani, Claude Préhu, John Old, Shirley Henderson, Vera Balamitsa, Hongyuan Luo, Man‐Chiu Poon, David H.K. Chui, Henri Wajcman, George P. Patrinos

Bibliographic record

VenueAnnals of Hematology · 2008
Typearticle
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsCalgary Laboratory ServicesSouth Health CampusUniversity of Calgary
Fundersnot available
KeywordsGeneGeneticsBiologyGlobinMutationHemoglobin variantsHemoglobinPhenotypeBiochemistry

Abstract

fetched live from OpenAlex

The human alpha-globin genes are paralogues, sharing a high degree of DNA sequence similarity and producing an identical alpha-globin chain. Over half of the alpha-globin structural variants reported to date are only characterized at the amino acid level. It is likely that a fraction of these variants, with phenotypes differing from one observation to another, may be due to the same mutation but on a different alpha-globin gene. There have been very few previous examples of hemoglobin variants that can be found at both HBA1 and HBA2 genes. Here, we report the results of a systematic multicenter study in a large multiethnic population to identify such variants and to analyze their differences from a functional and evolutionary perspective. We identified 14 different Hb variants resulting from identical mutations on either one of the two human alpha-globin paralogue genes. We also showed that the average percentage of hemoglobin variants due to a HBA2 gene mutation (alpha2) is higher than the percentage of hemoglobin variants due to the same HBA1 gene mutation (alpha1) and that the alpha2/alpha1 ratio varied between variants. These alpha-globin chain variants have most likely occurred via recurrent mutations, gene conversion events, or both. Based on these data, we propose a nomenclature for hemoglobin variants that fall into this category.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.254
Threshold uncertainty score0.386

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 head0.070
GPT teacher head0.346
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 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

Citations44
Published2008
Admission routes1
Has abstractyes

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