한국수산과학회

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kfas, vol. 54, no. 4, pp.489-497, August, 2021
DOI. https://doi.org/10.5657/KFAS.2021.0489

Machine Learning SNP for Classification of Korean Abalone Species (Genus Haliotis) 전복류(Genus Haliotis)의 분류를 위한 단일염기변이 기반 기계학습분석

노은수*·김주원·김동균
국립수산과학원 양식산업연구부 생명공학과

Climate change is affecting the evolutionary trajectories of individual species and ecological communities, partly through the creation of new species groups. As population shift geographically and temporally as a result of climate change, reproductive interactions between previously isolated species are inevitable and it could potentially lead to invasion, speciation, or even extinction. Four species of abalone, genus Haliotis are present along the Korean coastline and these species are important for commercial and fisheries resources management. In this study, genetic markers for fisheries resources management were discovered based on genomic information, as part of the management of endemic species in response to climate change. Two thousand one hundred and sixty one single nucleotide polymorphisms (SNPs) were discovered using genotyping-by-sequencing (GBS) method. Forty-one SNPs were selected based on their features for species classification. Machine learning analysis using these SNPs makes it possible to differentiate four Haliotis species and hybrids. In conclusion, the proposed machine learning method has potentials for species classification of the genus Haliotis. Our results will provide valuable data for biodiversity conservation and management of abalone population in Korea.

Keyword : Machine learning, Single nucleotide polymorphism, Abalone, Fisheries resource management

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