Distribution of Runs of Homozygosity and Their Relationship with Candidate Genes for Productivity in Kazakh Meat–Wool Sheep Breed
Bibliographic record
Abstract
Increasing the fertility of sheep remains one of the crucial issues of modern sheep breeding. The Kazakh meat–wool sheep is an excellent breed with high meat and wool productivity and well adapted to harsh conditions. Nowadays, runs of homozygosity (ROHs) are considered a suitable approach for studying the genetic characteristics of farm animals. The aims of the study were to analyze the distribution of ROHs, describe autozygosity, and detect genomic regions with high ROH islands. In this study, we genotyped a total of 281 Kazakh meat–wool sheep using the Illumina iScan® system (EquipNet, Canton, MA, USA) via Ovine SNP50 BeadChip array. As a results, a total of 15,069 ROHs were found in the three Kazakh meat–wool sheep populations. The mean number of ROH per animal across populations varied from 40.3 (POP1) to 42.2 (POP2) in the category 1+ Mb. Furthermore, the number of ROH per animal in ROH1–2 Mb were much higher than ROH2–4 Mb and ROH8–16 Mb in the three sheep populations. Most of individuals had small number of ROH>16 Mb. The highest and lowest genomic inbreeding coefficient values were observed in POP2 and POP3, respectively. The estimated FROH presented the impact that recent inbreeding has had in all sheep populations. Furthermore, a set of interesting candidate genes (BMP2, BMPR2, BMPRIB, CLOCK, KDM2B, TIAM1, TASP1, MYBPC1, MYOM1, and CACNA2D1), which are related to the productive traits, were found. Collectively, these findings will contribute to the breeding and conservation strategies of the Kazakh meat–wool sheep breed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".