Runs of homozygosity assessment using reduced representation sequencing highlight the evidence of random mating in emu (<i>Dromaius novaehollandiae</i>)
Bibliographic record
Abstract
The domestication of emu ( Dromaius novaehollandiae) began in the 1970s, but their productive characteristics have not undergone significant genetic enhancement. This study investigated the inbreeding and genetic diversity of 50 emus from various farms in Japan using Double digest restriction-site associated DNA sequencing (ddRAD-seq) markers. Single nucleotide polymorphism (SNP) calling revealed 171 975 high-quality SNPs while runs of homozygosity (ROH) analysis identified 1843 homozygous segments, with an average of 36.86 ROH per individual and a mean genome length of 27 Mb under ROH. The majority (86%) of ROH were short (0.5–1 Mb), indicating ancient or remote inbreeding. The average genomic inbreeding coefficient ( FROH) was 0.0228, suggesting nearly no inbreeding. Overlapping ROH regions were identified, with top consensus regions found on chromosomes 8 and Z. Seven candidate genes related to egg production, feather development, and energy metabolism were annotated in these regions. The findings highlight the prevalence of genetic diversity and low inbreeding levels in the studied emu population. This research highlights the potentiality of random mating in genetic management and conservation of emus. Further studies should focus on enhancing productive traits through selective breeding while preserving genetic diversity to ensure the sustainable growth of the emu farming.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".