Bibliographic record
Abstract
In domestic animals, genomic studies of inbreeding and selection have mainly focused on autosomes, neglecting the X chromosome. This neglect is significant as the X chromosome influences many important traits and has unique characteristics that may lead to more pronounced effects of inbreeding and positive selection. In addition, in its small part, PAR, inbreeding avoidance might occur. Furthermore, hemizygous haplotypes on nonPAR in males clearly reveal haplotype structure, enabling detection of positive selection signals and investigation of phylogenetic relationships. Therefore, the main objectives of this dissertation were to evaluate and compare F on the X chromosome and autosomes in domestic animal populations with special focus on PAR and to develop a new method for identifying positive selection signals based on the difference in haplotype richness of nonPAR in males. Each population used was represented by high density Illumina genotypes with an adequate number of both males and females. Five different inbreeding coefficients were used on two distinct populations for cattle (Croatian cattle breeds and Nellore), dogs (Labrador Retriever and Patagonian Sheepdog) and sheep (Croatian sheep breeds and Soay). Conversely, a new method called Haplotype Richness Drop (HRiD) was established and tested alongside classical methods (eROHi, iHS, and nSL) in metapopulation of native Croatian sheep breeds. Each identified signal underwent functional characterization, gene annotation and MJN. Higher inbreeding was found on the X chromosome compared to autosomes in all populations using FROH_SVS and FROH_RZooROH (most reliable), while no differences were found using FLH1, FVR1 and FYA2, with greater variability observed using all five coefficients. No difference in F between sexes at PAR or compared to autosomes was found. Using HRiD, four signals were identified and consistently validated, with the same most significant signal across all four methods (from 13.04 to 13.62 Mb). Overall, 14 positive selection signals (12 regions) were identified with 34 genes, with high concordance (86%) with other studies of sheep. The results demonstrate the high accuracy and reliability of HRiD and show that HRiD can be used comprehensively or in scenarios where only male genotypes are available, which is common in livestock where genomic breeding values are predominantly performed for males. Moreover, MJN is shown to provide useful additional information when analysing haplotypes identified as selection signals (derived versus ancestral haplotype or control for population structure-induced disorders). In general, the results emphasize the importance of including the X chromosome in inbreeding estimation and selection identification in domestic animal populations, while the new HRiD method opens up new possibilities in identifying signals using heterogametic sex haplotypes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".