Parentage assignment in black soldier fly (Hermetia illucens) using genotyping-by-sequencing
Bibliographic record
Abstract
Genetic selection to optimize economically important traits in black soldier flies (BSF), a major species in the insects as food and feed industry, continues to gain interest. Tracking pedigrees is a prerequisite for generating genetic progress while conserving the genetic variability of traits under selection. However, this is not currently feasible in mass reared insects like BSF. As an alternative, this study identified SNPs informative for parentage assignment (PA) in a commercial and laboratory colony of BSF using genotyping-by-sequencing (GBS). We first established an experimental population of 12 BSF families per colony by randomly mating flies within each family over three generations. DNA was then sequenced from mated pairs and two larvae per pair per generation (n = 288 samples). After SNP calling and filtering, we generated four high-quality SNP subsets containing 192, 118, 72, and 51 SNPs, respectively. PA was conducted using a likelihood-based method across simulated inbreeding rates from 0% to 100%. Compared to known parents, PA accuracy reached 100% across all SNP subsets and inbreeding rates. However, assignment confidence as measured by the log-likelihood (LOD) score decreased significantly as the number of SNPs decreased, though inbreeding had no significant effect on LOD scores. High-confidence assignments to either male or female parents required all 192 SNPs, whereas high-confidence assignments to parent pairs were possible with 118 or 192 SNPs. The identified SNPs provide a valuable resource for developing low-density panels to implement pedigree-based selection and to manage genetic diversity, thereby supporting the development of breeding programs in BSF.
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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 | 0.001 |
| 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".