Whole‐genome resequencing reveals loci under selection during silkworm improvement
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
Breeding or genetic improvement refers to the process of artificial selection following domestication; as such, it has had a major influence on modern agriculture and animal production. Improvement generally focuses on traits that greatly affect the economic performance. Therefore, understanding the genetic basis underlying improvement will contribute to the identification of genes controlling economic traits and will facilitate future crop and animal breeding. However, genome-wide study of the molecular basis underlying improvement remains rare. The silkworm is a unique, entirely domesticated economically important invertebrate; genetic improvement has had a huge effect on the silkworm regarding silk-related traits. Herein, we performed whole-genomic sequencing on local and genetically improved silkworm lines to identify the genomic regions under strong selection in silkworm breeding/improvement. By genomic-wide selective sweeping analysis, we identified 24 genomic regions with strong selection signals, eight of which contained 13 candidate genes underlying silkworm breeding. Interestingly, six of these genes were annotated with functions related to neural signal response. Among the six genes, BGIBMGA004050 encodes silkworm CREB-regulated_transcription_coactivator_1 (BmCRTC1), which was reported to be involved in energy-sensing pathways. These results suggested that improvement may have affected the nervous system of the silkworm. This research will provide new insights into the genetic basis underlying the genetic improvement of silkworms and possibly of other species.
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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 it