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Record W2986233154 · doi:10.1089/gtmb.2019.0116

Genetic Distribution of the <i>LTA</i> +252 A&gt;G and <i>TNFA</i> −308 G &gt; A Polymorphisms in the Moroccan Population

2019· article· en· W2986233154 on OpenAlexaff
Fatima Zahra Aznag, Mohamed Taha Moutaoufik, Amal Korrida, El Hassan Izaabel

Bibliographic record

VenueGenetic Testing and Molecular Biomarkers · 2019
Typearticle
Languageen
FieldImmunology and Microbiology
TopicT-cell and B-cell Immunology
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsSingle-nucleotide polymorphismLinkage disequilibriumBiologyGeneticsPopulationAlleleAllele frequencyHaplotypeGenotypeGenetic associationGeneMedicine

Abstract

fetched live from OpenAlex

Introduction: The LTA and TNFA genes encode key proinflammatory cytokines with diverse activities in the immune responses. Single nucleotide polymorphisms (SNPs) in the LTA rs909253 (+252 A > G) and TNFA rs1800629 (−308 G > A) genes have been associated with susceptibility to many complex diseases. The aim of this study was to assess the frequency for these two key polymorphisms in the Moroccan population. Materials and Methods: A total of 338 unrelated healthy Moroccan subjects were genotyped for the two alleles using a restriction fragment length polymorphism–polymerase chain reaction method. Results: The LTA (+252 A > G) and TNFA (−308 G > A) were the most common alleles with 67.9% and 74.8% frequencies, respectively. In addition to the linkage disequilibrium between the two SNPs, significant differences in allele frequencies were observed in Moroccan population compared with Mediterraneans, Europeans, Africans, South Americans, and Asians (p < 0.05). Finally, genetic proximities between Moroccan, European, and West African populations were found by means of the principal component analysis. Conclusion: The LTA +252 A>G and TNFA −308 G > A polymorphisms among Moroccan population follow the patterns commonly encountered in other Mediterranean, European, and African populations. The result of this study could contribute in developing a genetic database on the healthy Moroccan population.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.770
Threshold uncertainty score0.522

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 head0.006
GPT teacher head0.189
Teacher spread0.184 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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