Accurate Runs of Homozygosity Estimation From Low Coverage Genome Sequences in Non‐Model Species
Bibliographic record
Abstract
Runs of homozygosity (ROH) are increasingly being analysed using whole genome sequences in non-model species as a measure of inbreeding and to assess demographic history, thus providing useful information for conservation. However, most studies have used Plink for ROH inference which performs poorly when sequencing depth is below 10×, often underestimating ROH. This can lead to erroneous status assessment and poor management decisions. We assessed the performance of ROHan, a program developed for ROH and heterozygosity estimation using lower coverage sequences that have so far only been optimised for human data. Using high coverage whole genomes from 22 caribou, a non-model species at risk presenting varying levels of inbreeding, we assessed the effects of sequencing depth (1-15×), the input parameter 'rohmu' that determines the heterozygosity rate that is tolerated within ROH regions, and demographic history on the ROH inference and heterozygosity. Accurate estimation of the percentage of the genome and lengths of ROH could be achieved at depths as low as 3-5×. However, the rohmu parameter and individual demographic history had a significant effect on the results. Heterozygosity was also overestimated at low depth. Using our optimised rohmu parameter, we re-analysed low coverage sequences from a small and isolated caribou population and demonstrated high inbreeding levels that had previously been missed. We provide recommendations for optimisation of the rohmu parameter and demonstrate the need for careful interpretation of outputs to enable robust ROH inference using low coverage whole genome sequences in wildlife species.
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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 | 0.015 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".