<b>Integrated GWAS, Meta-Analysis, and Bayesian Fine Mapping Reveal Novel QTLs and Functional Candidate Genes for Vulva Traits in Large White Pigs</b>_pheno data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The reproductive performance of pigs is crucial for agricultural production, and the vulva traits of sows—such as length, width, and angle—directly impact breeding efficiency. For example, gilts with small or upward-tilted vulva are often culled, limiting the size and efficiency of breeding herds. To improve the retention rate of breeding females, we conducted this study to explore the key genes and genetic mechanisms underlying these traits using genomics. We collected data on vulva traits from 2,197 gilts across three Large White pig populations (from PIC, Topigs, and Canada) and used genome-wide association studies (GWAS) and meta-analysis techniques, combined with Bayesian fine mapping, to systematically identify genetic loci and candidate genes associated with these traits. Through these methods, we discovered several new significant loci and identified potential candidate genes such as <i>SDC2</i>, <i>MTERF3</i>, <i>VIP</i>, <i>POP1</i>, and <i>PSMA1</i> that may play important roles in regulating vulva traits. These findings provide new insights into the genetic mechanisms of reproductive traits in pigs and offer a vital molecular basis for future breeding programs. By using marker-assisted selection (MAS) or genomic selection (GS), we can more effectively improve vulva traits, thereby increasing the retention rate and productivity of breeding females. This not only enhances the economic benefits of pig farming but also improves animal welfare by reducing the culling of gilts due to reproductive issues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.033 | 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 it