A major SNP resource for dissection of phenotypic and genetic variation in Pacific white shrimp (<i>Litopenaeus vannamei</i>)
Bibliographic record
Abstract
Bioinformatics and re-sequencing approaches were used for the discovery of sequence polymorphisms in Litopenaeus vannamei. A total of 1221 putative single nucleotide polymorphisms (SNPs) were identified in a pool of individuals from various commercial populations. A set of 211 SNPs were selected for further molecular validation and 88% showed variation in 637 samples representing three commercial breeding lines. An association analysis was performed between these markers and several traits of economic importance for shrimp producers including resistance to three major viral diseases. A small number of SNPs showed associations with test weekly gain, grow-out survival and resistance to Taura Syndrome Virus. Very low levels of linkage disequilibrium were revealed between most SNP pairs, with only 11% of SNPs showing an r(2)-value above 0.10 with at least one other SNP. Comparison of allele frequencies showed small changes over three generations of the breeding programme in one of the commercial breeding populations. This unique SNP resource has the potential to catalyse future studies of genetic dissection of complex traits, tracing relationships in breeding programmes, and monitoring genetic diversity in commercial and wild populations of L. vannamei.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".