A free lunch: microhaplotype discovery in an existing amplicon panel improves parentage assignment for the highly polymorphic Pacific oyster
Bibliographic record
Abstract
Amplicon panels using genotyping by sequencing methods are now common, but have focused on characterizing SNP markers. We investigate how microhaplotype (MH) discovery within a recently developed Pacific oyster (Magallana gigas) amplicon panel could increase the statistical power for relationship assignment. Trios (offspring and two parents) from three populations in a newly established breeding program were genotyped on a 592 locus panel. After processing, 92% of retained amplicons contained polymorphic MH variants and 85% of monomorphic SNP markers contained MH variation. The increased allelic richness resulted in substantially improved power for relationship assignment with much lower estimated false positive rates. No substantive differences in assignment accuracy occurred between SNP and MH datasets, but using MHs increased the separation in log-likelihood values between true parents and highly related potential parents (aunts and uncles). A high number of Mendelian incompatibilities among trios were observed, likely due to null alleles. Further development of a MH panel, including removing loci with high rates of null alleles, would enable high-throughput genotyping by reducing panel size and therefore cost for Pacific oyster research and breeding programs.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".