Journal of Animal Breeding and Genomics (J Anim Breed Genom)
WoonYoung Jeong1, YeongKuk Kim2, Yoonsik Kim1, Yoonji Chung1, Dong Jae Lee1, Ji Min Kang1, Dooho Lee1, Yang Mo Koo3, Seung Hwan Lee1,*
1Division of Animal & Dairy Science, Chungnam National University, Daejeon, 34134, South Korea
2Quantomic research & solution, Daejeon, 34134, South Korea
3Korea Animal Improvement Association, 88 Myeongdal-ro, Seoul, South Korea
Correspondence to Seung Hwan Lee, E-mail: slee46@cnu.ac.kr
Volume 7, Number 4, Pages 167-178, December 2023.
Journal of Animal Breeding and Genomics 2023, 7(4), 167-178. https://doi.org/10.12972/jabng.20230018
Received on 14 November, 2023, Revised on 26 December, 2023, Accepted on 26 December, 2023, Published on 31 December, 2023.
Copyright © 2023 Korean Society of Animal Breeding and Genetics.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0).
This research used the simulation program to identify individuals resistant to foot-and-mouth disease (FMD) using the QMSim program. The simulation program was utilized to generate genetic and phenotypic data for individuals with and without FMD immunity. Subsequently, based on the simulated data, a genome-wide association study (GWAS) was performed to detect quantitative trait loci (QTL) associated with FMD immunity. Additionally, the QTLs identified by GWAS were compared with Random Forest (RF) and XGBoost. Out of the 41,461 SNPs, which included QTLs generated from the simulation, a total of 20 markers were found to be associated with FMD immunity. When comparing the performance of GWAS, RF, and XGBoost, RF identified the highest number of QTLs (7), followed by GWAS (6) and XGBoost (3). Furthermore, GBLUP, RF, and XGBoost were employed to classify individuals as either having or lacking FMD immunity. The classification accuracy, sensitivity, and specificity were evaluated using a confusion matrix, and the results were compared. The overall accuracy of the classification was as follows: XGBoost 0.53, RF 0.52, GBLUP 0.51. Sensitivity values were RF 0.98, XGBoost 0.97, GBLUP 0.19, and specificity values were GBLUP 0.83, XGboost 0.08, RF 0.05. XGBoost consistently outperformed the other methods in the overall accuracy and sensitivity, while GBLUP exhibited the lowest performance. Therefore, the research suggests that combining various methods in an ensemble approach, rather than relying solely on GBLUP, can lead to better predictions of FMD-resistant individuals. This approach has the potential to help mitigate the damages caused by future FMD outbreaks.
FMD resistance, GBLUP, RandomForest, XGBoost
This research was financially supported by the Ministry of Small and Medium-sized Enterprises(SMEs) and Startups(MSS), Korea, under the “Regional Specialized Industry Development Program+(R&D, S3370836)” supervised by the Korea Technology and Information Promotion Agency for SMEs(TIPA).
This work was partly supported by the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries (IPET) funded by the Ministry of Agriculture, Food and Rural Affairs (MAFRA) (321082-3).
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