Investigation of growth traits in Turkish Merino lambs using multi-locus GWAS approaches: Karacabey Merino
Bibliographic record
Abstract
BACKGROUND: Sheep have demonstrated remarkable adaptability to diverse and unproductive pastures, making them highly advantageous in the context of sustainable farming practices in the globally warming world. Despite their adaptation skills, local sheep breeds generally exhibit low performance, highlighting the need to develop desired traits. Traditional Mixed Linear Model (MLM)-based single-locus Genome-Wide Association (GWA) studies may fall short in identifying multiple loci influencing traits due to their linear genome scanning approach, rendering them less effective for detecting polygenic effects. OBJECTIVES: To identify genetic variants associated with birth weight (BW) and weaning weight (WW) in Karacabey Merino lambs using multi-locus genome-wide association study (GWAS) approaches, and to propose candidate genes for these economically important growth traits. METHODS: Five multi-locus approaches were employed, including MrMLM, FastMrMLM, ISIS EM-BLASSO, FASTmrEMMA, pLARmEB, and pKWmEB. These methods utilize a two-stage process to detect associated markers, first screening with a single locus approach at a less strict significance threshold, followed by collective evaluation using multi-locus GWA models. Gene annotation was performed to identify candidate genes based on SNP locations within intron regions or within ± 100 Kb proximity of associated SNPs. RESULTS: Using the five multi-locus approaches, 11 SNPs were detected with significant effects on birth weight and seven SNPs on weaning weight in Karacabey merino lambs. Gene annotation revealed most associated SNPs were within or very close to protein-coding genes, suggesting a functional role in trait influence. The genes GNAQ, CDKL4, PIP, SLC7A1, PBRM1, SORCS3, and NFATC1 were identified as candidate genes for birth weight, while BABAM2, LALBA, NOP14, FAM110B, SKAP1, SVIL, and ATXN1 were proposed as candidate genes for weaning weight. CONCLUSION: These insights contribute to a better understanding of the genetic makeup of birth weight and weaning weight traits, supporting efforts to refine breeding programs for improved growth performance in Karacabey merino sheep.
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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 | 0.001 | 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".