Melatonin receptor 1A (MTNR1A) gene polymorphisms in the local goat population in South Sulawesi region of Indonesia
Bibliographic record
Abstract
Abstract Some important genes are involved in controlling the reproductive trait of goats, one of which is the Melatonin Receptor 1A (MTNR1A) gene. The MTNR1A gene is known with seasonal reproductive activity and related to lambing frequency in the goat. The purpose of this research was to determine the genetic polymorphisms of the MTNR1A gene in Kacang, and Peranakan Ottawa (PE) goat population reared traditionally in South Sulawesi region of Indonesia. In total 253 heads of goat consist of 137 heads of Kacang and 116 Peranakan Ottawa from South, Sulawesi region was used as research samples for blood collection. The blood samples were collected from the jugular vein, which was then continued for DNA extracted by using a DNA extraction kit. The MTNR1A genotype was identified by PCR-RFLP technique using restriction enzyme RsaI. The result showed that there was genetic diversity in the MTNR1A gene in Kacang and PE population with the obtained of two alleles R and r. The common allele was R with frequency 0.93 in Kacang population while in PE population was 0.89. The r allele was 0.06 and 0.10 in Kacang and PE population, respectively. The most common genotype found in the population was RR (0.95), while Rr was only 0.05 and rr genotype did not found in this population. Observed heterozygosity value was 0.05. According to the Hardy-Weinberg test, this population was in equilibrium for MTNR1A gene. In conclusion, this finding indicated that there was genetic diversity exists in local goat population, and future research needed to find any association with this genetic variation with reproductive performance, the data obtained from this study could be used for the strategic program in goat breeding to increase reproductive performance of local goat especially fertility traits.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".