Use of High-density SNP analyses to develop a long-term strategy for conventional populations to prevent loss of diversity - review
Bibliographic record
Abstract
Article Details: Received: 2020-09-10 | Accepted: 2020-10-30 | Available online: 2020-12-31 https://doi.org/10.15414/afz.2020.23.04.236-240 The aim was to review obtained results related to molecular-genetic analyses by high-density SNP chips and mtDNA in farm and wild populations within the project APVV-17-0060 Genomic Indicators of extranuclear DNA as a source of Diversity for Animal Breeding. Continuous human activity and subsequent socioeconomic and climatic changes of the environment significantly affect the genetic diversity of livestock on both intra- and inter-population levels. Concerning the conservation of local livestock populations and thus animal genetic resources (AnGR) for future generations is, therefore, necessary to monitor and look for new âmore preciseâ tools to measure the amount of genetic diversity. The expected result will be the identification of SNP markers and spot mutations with a significant effect on the process of development resp. variability of traits. In the case of the dog, identification of regions related to fitness, health, and trainability will be the primary objective. Keywords: Genetic diversity, economically important breeds, Animal genetic resources, Slovakia References CURIK, I., FERENÄAKOVIÄ, M. and SÃLKNER, J. (2014). Inbreeding and runs of homozygosity: a possible solution to an old problem. Livestock Science, 166, 26â34. ENGELSMA, K.A., VEERKAMP, R.F., CALUS, M.P., BIJMA, P. and WINDIG, J.J. (2012). Pedigree and marker-based methods in the estimation of genetic diversity in small groups of Holstein cattle. Journal of Animal Breeding and Genetics, 129, 195â205. FERENÄAKOVIÄ, M., BANADINOVIÄ, M., MERCVAJLER, M., KHAYATZADEH, N., MÃSZÃROS, G., CUBRIC-CURIK, V., CURIK, I. and SÃLKNER, J. 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Genomic signatures of selection in cattle throught variation of allele frequencies and linkage disequilibrium. Journal of Central European Agriculture, 20(2), 576â580. MORAVÄÃKOVÃ, N., ŽIDEK, R., KASARDA, R., JAKABOVÃ, D., GENÄÃK, M., POKORÃDI, J. and FERIANCOVÃ, E. (2020b). Identification of genetic families based on mitochondrial D-loop sequence in population of the Tatra chamois (Rupicapra rupicapra tatrica). Biologia, 75(1), 121â128. TRAKOVICKÃ, A., LEHOCKÃ, K., KASARDA, R., KADLEÄÃK, O. and MORAVÄÃKOVÃ, N. (2019). Effective population size and genomic inbreeding of Slovak Spotted cattle. In Book of Abstracts of the 70th Annual Meeting of the European Federation of Animal Science. Annual meeting of the European federation of animal science. Wageningen : Wageningen Academic Publishers, pp. 112.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".