Genome-wide association study dissection of candidate genes for fleece traits in Inner Mongolia cashmere goats based on whole-genome resequencing data
Bibliographic record
Abstract
OBJECTIVE: Genome-wide association study (GWAS) and haplotype analysis were employed to identify molecular markers and candidate genes associated with fleece traits in Inner Mongolia cashmere goats (IMCGs). METHODS: GWASs using whole-genome resequencing data together with phenotypic data from 2,299 IMCGs, applying four models: mixed linear model, multiple locus mixed linear model, fixed and random model circulating probability unification, and Bayesian-information and linkage-disequilibrium iteratively nested keyway. We focused on the GWAS signals to conduct gene annotation and performed functional enrichment analyses to explore the biological processes underlying these signals. Additionally, haplotypes were constructed for the significant loci, and haplotype-phenotype association analyses were performed to identify molecular markers and candidate genes associated with these fleece traits in IMCGs. RESULTS: We identified 542 SNPs and 179 candidate genes linked to fleece traits through GWAS and gene annotation. Genes such as LAMA3, KCTD1, PTK7, FGFR3, LEF1, TAPT1, PTCH1, ELOVL6, and EVC have emerged as important candidates that may influence fleece traits. Furthermore, 11 haplotype blocks related to fleece traits were constructed, among which A1A1, C1C1, E2E2, F1F1, G1G1, H1H1 and K1K1 were identified as the superior haplotype combinations for fleece traits. These could serve as important molecular markers to improve the accuracy of early selection and the economic efficiency of breeding programs for fleece traits in IMCGs. CONCLUSION: This study successfully employed GWAS to identify key genetic loci significantly associated with the fleece traits of IMCGs. The genetic basis of these traits was revealed through additional gene annotation and haplotype analysis. The findings provide important theoretical and practical foundations for molecular breeding in IMCGs.
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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 | 0.001 | 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".