Expanding the application of haplotype-based genomic predictions to the wild: A case of antibody response against Teladorsagia circumcincta in Soay sheep
Bibliographic record
Abstract
Abstract Background Genomic prediction of breeding values (GP) has been adopted in evolutionary genomic studies to uncover microevolutionary processes of wild populations or improve captive breeding strategies. While recent evolutionary studies applied GP with individual single nucleotide polymorphism (SNP), haplotype-based GP could outperform individual SNP predictions through more capture of the linkage disequilibrium (LD) between the SNP and quantitative trait loci (QTL). This study aimed to compare the accuracy and bias of Genomic Best Linear Unbiased Prediction (GBLUP) and five Bayesian methods [BayesA, BayesB, BayesCπ, Bayesian Lasso (BayesL), and BayesR] for GP of immunoglobulin (Ig) A (IgA), IgE, and IgG against Teladorsagia circumcincta in lambs of an unmanaged sheep population (Soay breed). Genomic predictions using SNP, haplotypic pseudo-SNP from blocks with different LD thresholds (0.15, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 and 1.00), or the combinations of pseudo-SNPs and non-LD clustered SNPs were evaluated. Results Higher ranges of accuracies were observed for IgA (0.36 to 0.82), followed by IgE (0.20 to 0.60), and IgG (0.09 to 0.33). For IgA, up to 33% gain in GP accuracy was obtained using the combinations of the pseudo-SNPs with non-clustered SNPs compared to GBLUP using SNPs. For IgE and IgG, up to 17% and 22% gains in accuracy were achieved by haplotype-based GPs compared to GBLUP using SNPs, respectively. Among haplotype-based GPs of IgA, lower accuracies were obtained with higher LD thresholds, whereas a reverse trend was observed for IgE and IgG. Bayesian methods outperformed GBLUP; BayesB achieved the most accurate Genomic Estimated Breeding Values (GEBV) for IgA (0.82) and IgG (0.33) and BayesCπ for IgE (0.60). Haplotype-based GPs predicted less-biased GEBVs in most IgG scenarios with high LD thresholds compared with SNP-based GBLUP, whereas no improvement in bias was observed for other traits. Conclusions Haplotype-based methods improved GP accuracy of anti-helminthic antibody traits compared to GBLUP using individual SNP. The observed gains in the predictive performances indicate that haplotype-based methods could be advantageous for some traits in unmanaged wild animal populations.
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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 | 0.003 |
| 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 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".