Computational Modeling of Genomic Inbreeding and Homozygosity Islands in Populations with Extremely Small Effective Sizes : Discerning Genomic Signals of Selection from Inbreeding: A Simulation-Based Approach Using Labrador Retrievers as a Case Study
Bibliographic record
Abstract
In populations with extremely small effective population sizes with low genetic diversity and high levels of inbreeding, such as companion animal breeds, distinguishing between genomic signals of selection and inbreeding is particularly challenging. This difficulty arises from the overlapping genomic signatures of homozygous regions such as Runs of Homozygosity (ROH) and ROH-hotspots (ROH islands). While ROH represents large homozygous regions often associated with inbreeding, ROH hotspots may indicate regions under selection pressure. The interplay between these factors complicates the detection of selection signals, as both phenomena reduce genetic diversity. This project aimed to develop a computational pipeline to differentiate between signals of selection and inbreeding signals in such populations, using a publicly available Labrador Retriever dataset as a case study due to the extreme inbreeding levels and low genetic diversity found in modern dog breeds. Using the breeding program AlphaSimR, simulations of both neutral and selection models were conducted, employing hyperparameter optimization of population history parameters to align the simulation models with the empirical Labrador Retriever data. The pipeline identified two candidate regions for selection and estimated the selection strength of these candidate regions based on simulations of different selection scenarios. This work demonstrates the potential of simulation-based approaches to detect selection in populations where inbreeding complicates genetic analyses and offers a framework that could be applied to other breeds or species with similar population histories.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".