Accurate runs of homozygosity estimation from low coverage genome sequences in non-model species
Bibliographic record
Abstract
Runs of homozygosity (ROH) are increasingly being analyzed 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 has been shown to perform poorly when sequencing depth is below 10X, often underestimating the true proportion of the genome in ROH, which could lead to erroneous status assessment and management decisions. We use whole genome sequences from caribou, a non-model species at risk, subsampled to sequencing depths ranging from 1X to 15X, to assess the performance of ROHan, a program developed to enable ROH estimation using lower coverage sequences but so far only optimized for human data. We use 22 individuals with varying extent of inbreeding to assess the effects of sequencing depth, input parameters, and demographic history on the inference of ROH. We found that accurate estimation of the percentage of the genome and lengths of ROH can be achieved down to depths as low as 3-5X. However, input parameters and the demographic history of the individual can have a dramatic effect on results. Using our optimized settings, we then re-analyze low coverage sequences from a small and isolated caribou population and demonstrate high levels of inbreeding which had previously been missed. We provide recommendations for thorough optimization of parameters including the need for multiple runs as well as 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 | 0.011 | 0.038 |
| 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".