Identification of Tyrosinase Gene Polymorphisms Associated with Albinism in Swamp and Riverine Types of Water Buffaloes (Bubalus bubalis Linn.) in the Philippines
Bibliographic record
Abstract
Background: Albinism is a genetic condition marked by a lack of melanin in the skin, hair, and eyes, leading to increased sensitivity to light and susceptibility to skin cancer. Oculocutaneous albinism in buffaloes is caused by a G>A mutation in the tyrosinase gene, which introduces a premature stop codon, rendering the enzyme inactive. Despite efforts to prevent genetic defects, albinism persists because it is an autosomal recessive trait. This study used capillary sequencing to analyze the tyrosinase gene in local buffaloes. Methods: One hundred forty-eight (148) buffaloes were sampled for genomic DNA extraction, followed by PCR amplification of the reported region of the tyrosinase gene with G>A mutation associated with albinism. The PCR products were subjected to Sanger chain termination sequencing. Genotypic frequencies were computed manually, and phenotypic association was done descriptively. Results: The proportion of phenotypically albino-looking buffaloes was 4.76% of the sampled animals and are homozygous for the A allele of the G>A mutation at position 1494 of the tyrosinase gene. These were all riverine-type buffaloes. Phenotypically white but with pigmented irises were all swamp buffaloes and comprised 4.17% of the sampled animals. All swamp buffaloes sampled, including the phenotypically white with pigmented irises, were homozygous for the G allele of the G>A mutation at position 1494, suggesting these are not similar cases of oculocutaneous albinism. Conclusions: The study established baseline data on the prevalence of albinism and identified new mutations in the tyrosinase gene for further research on their effects on color phenotypes and production potential.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".