Detecting Positive Selection of Korean Native Goat Populations Using Next-Generation Sequencing
Bibliographic record
Abstract
Goats (Capra hircus) are one of the oldest species of domesticated animals.Native Korean goats are a particularly interesting group, as they are indigenous to the area and were raised in the Korean peninsula almost 2,000 years ago.Although they have a small body size and produce low volumes of milk and meat, they are quite resistant to lumbar paralysis.Our study aimed to reveal the distinct genetic features and patterns of selection in native Korean goats by comparing the genomes of native Korean goat and crossbred goat populations.We sequenced the whole genome of 15 native Korean goats and 11 crossbred goats using next-generation sequencing (Illumina platform) to compare the genomes of the two populations.We found decreased nucleotide diversity in the native Korean goats compared to the crossbred goats.Genetic structural analysis demonstrated that the native Korean goat and crossbred goat populations shared a common ancestry, but were clearly distinct.Finally, to reveal the native Korean goat's selective sweep region, selective sweep signals were identified in the native Korean goat genome using cross-population extended haplotype homozygosity (XP-EHH) and a cross-population composite likelihood ratio test (XP-CLR).As a result, we were able to identify candidate genes for recent selection, such as the CCR3 gene, which is related to lumbar paralysis resistance.Combined with future studies and recent goat genome information, this study will contribute to a thorough understanding of the native Korean goat genome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".