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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2022.9
- 수록면
- 830 - 841 (12page)
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초록· 키워드
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Genetic analysis has great potential as a tool to differentiate between different species andbreeds of livestock. In this study, the optimal combinations of single nucleotide polymorphism(SNP) markers for discriminating the Yeonsan Ogye chicken (Gallus gallus domesticus)breed were identified using high-density 600K SNP array data. In 3,904 individuals from 198chicken breeds, SNP markers specific to the target population were discovered through acase-control genome-wide association study (GWAS) and filtered out based on the linkagedisequilibrium blocks. Significant SNP markers were selected by feature selection applyingtwo machine learning algorithms: Random Forest (RF) and AdaBoost (AB). Using a machinelearning approach, the 38 (RF) and 43 (AB) optimal SNP marker combinations for the YeonsanOgye chicken population demonstrated 100% accuracy. Hence, the GWAS and machinelearning models used in this study can be efficiently utilized to identify the optimal combinationof markers for discriminating target populations using multiple SNP markers.
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