Whole genome linkage disequilibrium and effective population size in a coho salmon ( <i>Oncorhynchus kisutch</i> ) breeding population
Bibliographic record
Abstract
Abstract The estimation of linkage disequilibrium between molecular markers within a population is critical when establishing the minimum number of markers required for association studies, genomic selection and for inferring historical events influencing different populations. This work aimed to evaluate the extent and decay of linkage disequilibrium in a coho salmon breeding population using ddRAD genomic markers. Linkage disequilibrium was estimated between a total of 7,505 SNPs found in 62 individuals (33 dams and 29 sires) from the breeding population. The makers encompass all 30 coho salmon chromosomes and comprise 1,655.19 Mb of the genome. The average density of markers per chromosome ranged from 3.45 to 6.11 per 1 Mbp. The minor allele frequency averaged 0.20 (with a range from 0.08 to 0.50). The overall average linkage disequilibrium among SNPs pairs measured as r 2 was 0.054. The Average r 2 value decreased with increasing physical distance, with values ranging from 0.37 to 0.054 at distances lower than 1 kb and up to 10 Mb, respectively. An r 2 threshold of 0.1 was reached at distance of approximately 1.3 Mb. Chromosomes Okis05, Okis15 and Okis28 showed high levels of linkage disequilibrium (> 0.20 at distances lower than 1 Mb). Average r 2 values were lower than 0.1 for all chromosomes at distances greater than 4 Mb. Linkage disequilibrium values suggest that whole genome association and selection studies could be performed using about 75,000 SNPs in aquaculture populations (depending on the trait under investigation). From the identified SNPs, an effective population size of 100 was estimated for the population 10 generation ago, and 1,000, for 139 generations ago. Based on the extent of r 2 decay, we suggest that at least 75,000 SNPs would be necessary for an association mapping study. Over 100,000 SNPs would be necessary for a high power study, in the current coho salmon population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".