Genome-wide association study and genomic selection for growth-related traits in Eastern oyster (Crassostrea virginica)
Bibliographic record
Abstract
Understanding the genetic architecture of economically important traits is essential for the design and implementation of efficient breeding programs. Here, we analysed 3,653 Eastern oysters (Crassostrea virginica) from 62 crossing groups and 307 full-sib families, all genotyped with a 200 K SNP array, to (i) estimate heritability and genetic correlations for six growth-related traits, (ii) detect quantitative trait loci (QTL) and candidate genes, and (iii) compare the accuracy of pedigree- (PBLUP) versus genomic-based (GBLUP) predictions across marker densities, selection strategies (physical distance (PD) versus linkage disequilibrium (LD) and phenotype-based genotyping strategies. SNP-based Heritabilities ranged from 0.33 to 0.56, and genetic correlations among traits exceeded 0.61, indicating strong pleiotropy. GWAS revealed a polygenic architecture with a shared QTL on chromosome 2; the top SNP explained ≤ 1.1% of phenotypic variance. Except for the 0.5 K LD panel-where PBLUP was more accurate-and the 1 K LD panel-where both methods performed similarly-GBLUP outperformed PBLUP in all scenarios, with the greatest advantage observed for physically spaced (PD) panels. The highest accuracy (0.80) was achieved with the full 100 K SNP set. GBLUP models required panels containing ≥ 0.5 K PD (accuracy ≥ 0.55) or ≥ 2 K LD (accuracy ≥ 0.56) to significatively surpass PBLUP predictions. Training sets built from extreme phenotypes boosted accuracy in small to intermediate sample sizes (e.g., 0.74 at n = 1,500 with 5 K PD SNPs versus 0.53 under random sampling). These results provide novel insights into not only the genomic regions controlling growth in this species but also about the utility of PD-based SNP panels and balanced sampling designs for enhancing GS accuracy in C. virginica.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".