Optimising Genome‐Wide Detection of Runs of Homozygosity: Impacts of Reference Genome Quality and Sequencing Parameters on Inbreeding Assessment
Bibliographic record
Abstract
Inbreeding and inbreeding depression pose a critical challenge to the persistence of small and isolated populations, driving the need for precise assessment of genomic metrics. Genome-wide runs of homozygosity (ROH) have been widely used for evaluating contemporary inbreeding levels and tracing historical events, circumventing the limitations of methods based on pedigree records. However, the reliability of ROH detection is contingent upon the quality of both the reference genome and resequencing data. Here, we employed a simulation-based approach, generating an inbred population with individuals exhibiting varying inbreeding coefficients and 13 reference genomes with differing levels of contiguity. This framework enabled us to systematically investigate the effects of sequencing depth, read length, reference genome continuity and the phylogenetic divergence of reference genomes on detecting genome-wide ROH segments. We found that a sequencing depth of ≥ 15× and a reference genome with a contig N50 > 4 Mb enabled discrimination of both the recent and historical inbreeding events, and a reference genome of congeneric subspecies is an optimal choice for ROH detection if a species-specific reference genome is not available. Furthermore, we performed parameter optimisation for PLINK to enhance ROH detection accuracy under low-coverage sequencing data and imperfect reference genomes. Our findings established methodological guidance for improving ROH-based inbreeding assessments, providing critical insights for conservation genomics and breeding programmes where accurate characterisation of genomic homozygosity is paramount.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".