Genetic Basis of Growth Traits in Shrimp Based on QTL and GWAS Studies
Bibliographic record
Abstract
Shrimps are an important part of global aquaculture, especially South American white shrimp and other varieties dominate the global aquatic supply, and their output accounts for more than half of the world's total crustacean production. Growth traits (such as body length, weight, etc.) are directly related to breeding yield and economic benefits, and are one of the core goals of aquatic breeding. In recent years, molecular genetic technologies such as quantitative trait locus (QTL) localization and genome-wide association analysis (GWAS) have made progress in the field of aquatic products and have been gradually applied to shrimp genetic breeding research. This study reviews the main growth traits of shrimp and their biological mechanisms, reviews the current application status of QTL localization and GWAS in the study of growth traits of shrimp, summarizes the research progress of key molecular markers and candidate genes, and discusses the combination of traditional breeding and molecular assistive means using Chinese shrimp as an example. Finally, future strategies and international cooperation in shrimp molecular breeding are expected.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".