Studying the genetic loci related to the bovine leukemia virus using random forest method and genomic data

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Document Type : Research Article (Regular Paper)

Authors

1 Department of Animal Science, Faculty of Agricultural Science and Engineering, University College of Agriculture and Natural Resources (UTCAN), University of Tehran, Karaj, Iran

2 Department of Animal Science, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran

Abstract

Bovine leukemia virus (BLV) is a causative agent of bovine leukosis, which, due to its long incubation period, can spread widely within a herd before clinical symptoms appear, causing significant economic losses. This study used a supervised machine learning method called random forest to identify genomic regions associated with BLV. The non-parametric nature of this method allows for the creation of predictive models without the need for initial statistical assumptions; whereas the standard Genome-wide association studies (GWAS) methods are usually based on single-variable hypothesis tests and cannot account for correlations resulting from connectivity imbalance or the combination of multiple markers. In this study, the genotyping data of 145 Holstein cows (77 BLV-positive, 68 healthy) after quality control by using the PLINK (v 1.02), which resulted in 23,910 Single nucleotide polymorphisms (SNPs) were analyzed. Random forest analyses on the mentioned data included three hyperparameters: mtry (0.5(p/3), (p/3), 2(p/3)), ntree (2000, 3000, 4000), and nodesize (5, 10, 15), where p is equal to the total number of SNPs (23,910). To find the best SNPs, the Mean Decrease Accuracy (MDA) index (> 1.89) was used which resulted in the selection of 50 SNPs. Genomic enrichment analyses showed that genes associated with the top 50 SNPs are predominantly involved in Positive Regulation, Intracellular Signaling, Apoptosis and Cell Death, Signal Transduction, Metabolic Processes, and Cell Differentiation and Development. In total, 82 genes were identified, including hub genes such as MYC, RABIF, IRS1, TRAPPC9, MAPK8, HTT, SNX9, BCLAF1, XRN1, and LSM6.

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References

Ahlawat, S., Arora, R., Sharma, U., Sharma, A., Girdhar, Y., Sharma, R., Kumar, A., Vijh, R.K., 2021. Comparative gene expression profiling of milk somatic cells of Sahiwal cattle and Murrah buffaloes. Gene 764, 145101.
Alireza, Z., Maleeha, M., Kaikkonen, M., Fortino, V., 2024. Enhancing prediction accuracy of coronary artery disease through machine learning-driven genomic variant selection. Journal of Translational Medicine 22, 356.
Arjmand Kermani, F., Moradi Shahr Babak, H., Moradi Shahr Babak, M., Mohammadi, H., Jvan Nikkhah, M., Doosti, Y., 2024. Genome-wide association study to identify the loci related to resistance in Leukosis disease in Iranian Holstein cattle. Journal of Animal Production 26, 219-232. (In Persian)
Bhat, S.A., Elnaggar, M., Hall, T.J., McHugo, G.P., Reid, C., MacHugh, D.E., Meade, K.G., 2023. Preferential differential gene expression within the WC1.1+ γδ T cell compartment in cattle naturally infected with Mycobacterium bovis. Frontiers in Immunology 14, 1265038.
Bionaz, M., Loor, J.J., 2007. Identification of reference genes for quantitative real-time PCR in the bovine mammary gland during the lactation cycle. Physiological Genomics 29, 312-319.
Bongers, R., 2023. A genetic perspective on enzootic bovine leukosis resistance in Canadian Holstein cattle. Master of Science Thesis, University of Guelph, Guelph, Ontario, Canada.
Botta, V., Louppe, G., Geurts, P., Wehenkel, L., 2014. Exploiting SNP correlations within random forest for genome-wide association studies. PLoS One 9, e93379.
Breiman, L., 2001. Random Forests. Machine Learning, 45, 5-32.
Breiman, L., 2013. Breiman and Cutler’s Random Forests for Classification and Regression. Package ‘RandomForest’. Institute for Statistics and Mathematics, University of Economics and Business, Vienna.
Cantanhêde, L.F., Moura, M.T., Oliveira-Silva, R.L., Nascimento, P.S., Ferreira-Silva, J.C., Benko-Iseppon, A.M., Oliveira, M.A.L., 2022. MYC integrates FSH signalling networks in cumulus cells during bovine oocyte maturation. Acta Veterinaria Hungarica 70, 1-9.
Chang, J., Hwang, H.J., Kim, B., Choi, Y.G., Park, J., Park, Y., Lee, B.S., Park, H., Yoon, M.J., Woo, J.S., Kim, C., Park, M.S., Lee, J.B., Kim, Y.K., 2021. TRIM28 functions as a negative regulator of aggresome formation. Autophagy 17, 4231-4248.
Chin, C.H., Chen, S.H., Wu, H.H., Ho, C.W., Ko, M.T., Lin, C.Y., 2014. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Systems Biology 8 (Suppl 4) S11.
De León, C., Martínez, R., Rocha, J.F., Darghan, A.E., 2021. Selection of genomic regions and genes associated with adaptation and fertility traits in two Colombian creole cattle breeds. Genetics and Molecular Research 20, gmr18882.
Deng, T.X., Ma, X.Y., Duan, A., Lu, X.R., Abdel-Shafy, H., 2024. Genome-wide copy number variant analysis reveals candidate genes associated with milk production traits in water buffalo (Bubalus bubalis). Journal of Dairy Science 107, 7022-7037.
Dilweg, I.W., Savina, A., Köthe, S., Gultyaev, A.P., Bredenbeek, P.J., Olsthoorn, R.C.L., 2021. All genera of Flaviviridae host a conserved Xrn1-resistant RNA motif. RNA Biology 18, 2321-2329.
Enoma, D.O., Bishung, J., Abiodun, T., Ogunlana, O., Osamor, V.C., 2022. Machine learning approaches to genome-wide association studies. Journal of King Saud University – Science 34, 101847.
Freitas, P.H.F., Oliveira, H.R., Silva, F.F., Fleming, A., Schenkel, F.S., Miglior, F., Brito, L.F., 2020. Short communication: Time-dependent genetic parameters and single-step genome-wide association analyses for predicted milk fatty acid composition in Ayrshire and Jersey dairy cattle. Journal of Dairy Science 103, 5263-5269.
Gao, X., Sun, X., Yao, X., Wang, Y., Li, Y., Jiang, X., Han, Y., Zhong, L., Wang, L., Song, H., Xu, Y., 2022. Downregulation of the long noncoding RNA IALNCR targeting MAPK8/JNK1 promotes apoptosis and antagonizes bovine viral diarrhea virus replication in host cells. Journal of Virology 96, e0111322.
García-de-Gracia, F., Gaete-Argel, A., Riquelme-Barrios, S., Pereira-Montecinos, C., Rojas-Araya, B., Aguilera, P., Oyarzún-Arrau, A., Rojas-Fuentes, C., Acevedo, M.L., Chnaiderman, J., Valiente-Echeverría, F., Toro-Ascuy, D., Soto-Rifo, R., 2021. CBP80/20-dependent translation initiation factor (CTIF) inhibits HIV-1 Gag synthesis by targeting the function of the viral protein Rev. RNA Biology 18, 745-758.
Ghoreishifar, M., Vahedi, S.M., Salek Ardestani, S., Khansefid, M., Pryce, J.E., 2023. Genome-wide assessment and mapping of inbreeding depression identifies candidate genes associated with semen traits in Holstein bulls. BMC Genomics 24, 230.
Goldstein, B.A., Polley, E.C., Briggs, F.B., 2011. Random forests for genetic association studies. Statistical Applications in Genetics and Molecular Biology 10, 32.
Gupta, P., Kashmiri, S.V., Erisman, M.D., Rothberg, P.G., Astrin, S.M., Ferrer, J.F., 1986. Enhanced expression of the c-myc gene in bovine leukemia virus-induced bovine tumors. Cancer Research 46, 6295-6298.
Hong, M., Choi, S., Singh, N.K., Kim, H., Yang, S., Kwak, K., Kim, J., Lee, S., 2019. Genome-wide association analysis to identify QTL for carcass traits in Hanwoo (Korean native cattle). Indian Journal of Animal Sciences 89, 57-62.
Jiang, H., Chai, Z.X., Cao, H.W., Zhang, C.F., Zhu, Y., Zhang, Q., Xin, J.W., 2022. Genome-wide identification of SNPs associated with body weight in yak. BMC Genomics 23, 833.
Jiao, P., Yuan, Y., Zhang, M., Sun, Y., Wei, C., Xie, X., Zhang, Y., Wang, S., Chen, Z., Wang, X., 2020. PRL/microRNA-183/IRS1 pathway regulates milk fat metabolism in cow mammary epithelial cells. Genes 11, 196.
Khan, M.Z., Dari, G., Khan, A., Yu, Y., 2022. Genetic polymorphisms of TRAPPC9 and CD4 genes and their association with milk production and mastitis resistance phenotypic traits in Chinese Holstein. Frontiers in Veterinary Science 9, 1008497.
Li, B., Zhang, N., Wang, Y.G., George, A.W., Reverter, A., Li, Y., 2018. Genomic prediction of breeding values using a subset of SNPs identified by three machine learning methods. Frontiers in Genetics 9, 237.
Liu, S., Yin, H., Li, C., Qin, C., Cai, W., Cao, M., Zhang, S., 2017. Genetic effects of PDGFRB and MARCH1 identified in GWAS revealing strong associations with semen production traits in Chinese Holstein bulls. BMC Genetics 18, 63.
Lv, G., Wang, J., Lian, S., Wang, H., Wu, R., 2024. The global epidemiology of bovine leukemia virus: Current trends and future implications. Animals 14, 297.
Matenchi, Y.P., Hegarty, M., Baștanlar, E.K., 2024. Genome wide association analysis revealed novel candidate genes for body measurement traits in indigenous Gudali and crossbred Simgud in Cameroon. Research Square PREPRINT (Version 1).
Mayes, M.A., Sirard, M.A., 2002. Effect of type 3 and type 4 phosphodiesterase inhibitors on the maintenance of bovine oocytes in meiotic arrest. Biology of Reproduction 66, 180-184.
Meng, Y.A., Yu, Y., Cupples, L.A., Farrer, L.A., Lunetta, K.L., 2009. Performance of random forest when SNPs are in linkage disequilibrium. BMC Bioinformatics 10, 78.
Mentis, A.F., Kararizou, E., 2010. Metabolism and cancer: an up-to-date review of a mutual connection. Asian Pacific Journal of Cancer Prevention 11, 1437-1444.
Moon, S.L., 2014. Inhibition of the host 5'-3' RNA decay pathway is a novel mechanism by which flaviviruses influence cellular gene expression. Ph.D. Thesis, Colorado State University, Fort Collins, Colorado, USA.
Nguyen, T.T., Huang, J., Wu, Q., Nguyen, T., Li, M., 2015. Genome-wide association data classification and SNPs selection using two-stage quality-based Random Forests. BMC Genomics 16 Suppl 2, S5.
Oliveira, L.J., McClellan, S., Hansen, P.J., 2010. Differentiation of the endometrial macrophage during pregnancy in the cow. PLoS One 5, e13213.
Ooi, E., Xiang, R., Chamberlain, A.J., Goddard, M.E., 2024. Archetypal clustering reveals physiological mechanisms linking milk yield and fertility in dairy cattle. Journal of Dairy Science 107, 4726-4742.
Pease, N.A., Wise-Draper, T., Privette Vinnedge, L., 2015. Dissecting the Potential Interplay of DEK Functions in Inflammation and Cancer. Journal of Oncology 2015, 106517.
Rosse, I.C., Assis, J.G., Oliveira, F.S., Leite, L.R., Araujo, F., Zerlotini, A., Volpini, A., Dominitini, A.J., Lopes, B.C., Arbex, W.A., Machado, M.A., Peixoto, M.G., Verneque, R.S., Martins, M.F., Coimbra, R.S., Silva, M.V., Oliveira, G., Carvalho, M.R., 2017. Whole genome sequencing of Guzerá cattle reveals genetic variants in candidate genes for production, disease resistance, and heat tolerance. Mammalian Genome 28, 66-80.
Saadat, H.B., Torshizi, R.V., Manafiazar, G., Masoudi, A.A., Ehsani, A. and Shahinfar, S., 2024. Comparing machine learning algorithms and linear model for detecting significant SNPs for genomic evaluation of growth traits in F 2 chickens. Journal of Agricultural Science & Technology 26(6), 1261-1274.
Salvucci, M., Crawford, N., Stott, K., Bullman, S., Longley, D.B., Prehn, J.H.M., 2022. Patients with mesenchymal tumours and high Fusobacteriales prevalence have worse prognosis in colorectal cancer (CRC). Gut 71, 1600-1612.
Sasaki, Y., Nagai, K., Nagata, Y., Doronbekov, K., Nishimura, S., Yoshioka, S., Fujita, T., Shiga, K., Miyake, T., Taniguchi, Y., Yamada, T., 2006. Exploration of genes showing intramuscular fat deposition-associated expression changes in musculus longissimus muscle. Animal Genetics 37, 40-46.
Schiavo, G., Bertolini, F., Bovo, S., Galimberti, G., Muñoz, M., Bozzi, R., Čandek-Potokar, M., Óvilo, C., Fontanesi, L., 2024. Identification of population-informative markers from high-density genotyping data through combined feature selection and machine learning algorithms: Application to European autochthonous and cosmopolitan pig breeds. Animal Genetics 55, 193-205.
Schiavo, G., Bertolini, F., Galimberti, G., Bovo, S., Dall'Olio, S., Nanni Costa, L., Gallo, M., Fontanesi, L., 2020. A machine learning approach for the identification of population-informative markers from high-throughput genotyping data: application to several pig breeds. Animal 14, 223-232.
Schwarz, K.R., Pires, P.R., Mesquita, L.G., Chiaratti, M.R., Leal, C.L., 2014. Effect of nitric oxide on the cyclic guanosine monophosphate (cGMP) pathway during meiosis resumption in bovine oocytes. Theriogenology 81, 556-564.
Sheet, S., Jang, S.S., Kim, J.H., Park, W., Kim, D., 2024. A transcriptomic analysis of skeletal muscle tissues reveals promising candidate genes and pathways accountable for different daily weight gain in Hanwoo cattle. Scientific Reports 14, 315.
Siebert, L.J., 2017. Identifying genome associations with unique mastitis phenotypes in response to intramammary Streptococcus uberis challenge. Ph.D. Thesis, University of Tennessee, Knoxville, Tennessee, USA.
Silva, P.P., Gaudillo, J.D., Vilela, J.A., Roxas-Villanueva, R.M.L., Tiangco, B.J., Domingo, M.R., Albia, J.R., 2022. A machine learning-based SNP-set analysis approach for identifying disease-associated susceptibility loci. Scientific Reports 12, 15817.
Solodneva, E.V., Kuznetsov, S.B., Velieva, A.E., Stolpovsky, Yu.A., 2022. Molecular-genetic bases of mammary gland development using the example of cattle and other animal species: I. Embryonic and pubertal developmental stage. Russian Journal of Genetics 58, 899-914.
Uffelmann, E., Huang, Q.Q., Munung, N.S., de Vries, J., Okada, Y., Martin, A.R., Martin, H.C., Lappalainen, T., Posthuma, D., 2021. Genome-wide association studies. Nature Reviews Methods Primers 1, 1-21.
Wakayu, E.G., 2021. Machine learning analysis of single nucleotide polymorphism (SNP) data to predict bone mineral density in African American women. Master's Thesis, University of Nevada, Las Vegas, Nevada, USA.
Wang, D., Ma, S., Yan, M., Dong, M., Zhang, M., Zhang, T., Zhang, T., Zhang, X., Xu, L., Huang, X., 2024. DNA methylation patterns in the peripheral blood of Xinjiang brown cattle with variable somatic cell counts. Frontiers in Genetics 15, 1405478.
Yang, L., Xu, L., Zhu, B., Niu, H., Zhang, W., Miao, J., Shi, X., Zhang, M., Chen, Y., Zhang, L., Gao, X., Gao, H., Li, L., Liu, G.E., Li, J., 2017. Genome-wide analysis reveals differential selection involved with copy number variation in diverse Chinese Cattle. Scientific Reports 7, 14299.
Yousuf, S., Malik, W.A., Feng, H., Liu, T., Xie, L., Miao, X., 2023. Genome wide identification and characterization of fertility associated novel CircRNAs as ceRNA reveal their regulatory roles in sheep fecundity. Journal of Ovarian Research 16, 115.
Yu, Z., Zhu, J., Wang, H., Li, H., Jin, X., 2022. Function of BCLAF1 in human disease. Oncology Letters 23, 58.
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Articles in Press, Accepted Manuscript
Available Online from 13 July 2025

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