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Combinatorial Chemistry & High Throughput Screening

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ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

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Research Article

Importance of GWAS Risk Loci and Clinical Data in Predicting Asthma Using Machine-learning Approaches

Author(s): Zan-Mei Qin, Si-Qiao Liang, Jian-Xiong Long, Jing-Min Deng*, Xuan Wei, Mei-Ling Yang, Shao-Jie Tang and Hai-Li Li

Volume 27, Issue 3, 2024

Published on: 12 July, 2023

Page: [400 - 407] Pages: 8

DOI: 10.2174/1386207326666230602161939

Price: $65

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Abstract

Introduction: To understand the risk factors of asthma, we combined genome-wide association study (GWAS) risk loci and clinical data in predicting asthma using machine-learning approaches.

Methods: A case-control study with 123 asthmatics and 100 controls was conducted in the Zhuang population in Guangxi. GWAS risk loci were detected using polymerase chain reaction, and clinical data were collected. Machine-learning approaches were used to identify the major factors that contribute to asthma.

Results: A total of 14 GWAS risk loci with clinical data were analyzed on the basis of 10 times the 10-fold cross-validation for all machine-learning models. Using GWAS risk loci or clinical data, the best performances exhibited area under the curve (AUC) values of 64.3% and 71.4%, respectively. Combining GWAS risk loci and clinical data, the XGBoost established the best model with an AUC of 79.7%, indicating that the combination of genetics and clinical data can enable improved performance. We then sorted the importance of features and found the top six risk factors for predicting asthma to be rs3117098, rs7775228, family history, rs2305480, rs4833095, and body mass index.

Conclusion: Asthma-prediction models based on GWAS risk loci and clinical data can accurately predict asthma, and thus provide insights into the disease pathogenesis.

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[1]
Global Initiative for Asthma (GINA). The global strategy for asthma management and prevention. 2019. Available From: http://www.ginasthma.org
[2]
Los, H.; Koppelman, G.H.; Postma, D.S. The importance of genetic influences in asthma. Eur. Respir. J., 1999, 14(5), 1210-1227.
[http://dx.doi.org/10.1183/09031936.99.14512109] [PMID: 10596715]
[3]
Kim, K.W.; Ober, C. Lessons learned from GWAS of asthma. Allergy Asthma Immunol. Res., 2019, 11(2), 170-187.
[http://dx.doi.org/10.4168/aair.2019.11.2.170] [PMID: 30661310]
[4]
Bønnelykke, K.; Sleiman, P.; Nielsen, K.; Kreiner-Møller, E.; Mercader, J.M.; Belgrave, D.; den Dekker, H.T.; Husby, A.; Sevelsted, A.; Faura-Tellez, G.; Mortensen, L.J.; Paternoster, L.; Flaaten, R.; Mølgaard, A.; Smart, D.E.; Thomsen, P.F.; Rasmussen, M.A.; Bonàs-Guarch, S.; Holst, C.; Nohr, E.A.; Yadav, R.; March, M.E.; Blicher, T.; Lackie, P.M.; Jaddoe, V.W.V.; Simpson, A.; Holloway, J.W.; Duijts, L.; Custovic, A.; Davies, D.E.; Torrents, D.; Gupta, R.; Hollegaard, M.V.; Hougaard, D.M.; Hakonarson, H.; Bisgaard, H. A genome-wide association study identifies CDHR3 as a susceptibility locus for early childhood asthma with severe exacerbations. Nat. Genet., 2014, 46(1), 51-55.
[http://dx.doi.org/10.1038/ng.2830] [PMID: 24241537]
[5]
Ferreira, M.A.R.; Matheson, M.C.; Tang, C.S.; Granell, R.; Ang, W.; Hui, J.; Kiefer, A.K.; Duffy, D.L.; Baltic, S.; Danoy, P.; Bui, M.; Price, L.; Sly, P.D.; Eriksson, N.; Madden, P.A.; Abramson, M.J.; Holt, P.G.; Heath, A.C.; Hunter, M.; Musk, B.; Robertson, C.F.; Le Souëf, P.; Montgomery, G.W.; Henderson, A.J.; Tung, J.Y.; Dharmage, S.C.; Brown, M.A.; James, A.; Thompson, P.J.; Pennell, C.; Martin, N.G.; Evans, D.M.; Hinds, D.A.; Hopper, J.L. Genome-wide association analysis identifies 11 risk variants associated with the asthma with hay fever phenotype. J. Allergy Clin. Immunol., 2014, 133(6), 1564-1571.
[http://dx.doi.org/10.1016/j.jaci.2013.10.030] [PMID: 24388013]
[6]
Moffatt, M.F.; Gut, I.G.; Demenais, F.; Strachan, D.P.; Bouzigon, E.; Heath, S.; von Mutius, E.; Farrall, M.; Lathrop, M.; Cookson, W.O.C.M. A large-scale, consortium-based genomewide association study of asthma. N. Engl. J. Med., 2010, 363(13), 1211-1221.
[http://dx.doi.org/10.1056/NEJMoa0906312] [PMID: 20860503]
[7]
Gudbjartsson, D.F.; Bjornsdottir, U.S.; Halapi, E.; Helgadottir, A.; Sulem, P.; Jonsdottir, G.M.; Thorleifsson, G.; Helgadottir, H.; Steinthorsdottir, V.; Stefansson, H.; Williams, C.; Hui, J.; Beilby, J.; Warrington, N.M.; James, A.; Palmer, L.J.; Koppelman, G.H.; Heinzmann, A.; Krueger, M.; Boezen, H.M.; Wheatley, A.; Altmuller, J.; Shin, H.D.; Uh, S.T.; Cheong, H.S.; Jonsdottir, B.; Gislason, D.; Park, C.S.; Rasmussen, L.M.; Porsbjerg, C.; Hansen, J.W.; Backer, V.; Werge, T.; Janson, C.; Jönsson, U.B.; Ng, M.C.Y.; Chan, J.; So, W.Y.; Ma, R.; Shah, S.H.; Granger, C.B.; Quyyumi, A.A.; Levey, A.I.; Vaccarino, V.; Reilly, M.P.; Rader, D.J.; Williams, M.J.A.; van Rij, A.M.; Jones, G.T.; Trabetti, E.; Malerba, G.; Pignatti, P.F.; Boner, A.; Pescollderungg, L.; Girelli, D.; Olivieri, O.; Martinelli, N.; Ludviksson, B.R.; Ludviksdottir, D.; Eyjolfsson, G.I.; Arnar, D.; Thorgeirsson, G.; Deichmann, K.; Thompson, P.J.; Wjst, M.; Hall, I.P.; Postma, D.S.; Gislason, T.; Gulcher, J.; Kong, A.; Jonsdottir, I.; Thorsteinsdottir, U.; Stefansson, K. Sequence variants affecting eosinophil numbers associate with asthma and myocardial infarction. Nat. Genet., 2009, 41(3), 342-347.
[http://dx.doi.org/10.1038/ng.323] [PMID: 19198610]
[8]
Hirota, T.; Takahashi, A.; Kubo, M.; Tsunoda, T.; Tomita, K.; Doi, S.; Fujita, K.; Miyatake, A.; Enomoto, T.; Miyagawa, T.; Adachi, M.; Tanaka, H.; Niimi, A.; Matsumoto, H.; Ito, I.; Masuko, H.; Sakamoto, T.; Hizawa, N.; Taniguchi, M.; Lima, J.J.; Irvin, C.G.; Peters, S.P.; Himes, B.E.; Litonjua, A.A.; Tantisira, K.G.; Weiss, S.T.; Kamatani, N.; Nakamura, Y.; Tamari, M. Genome-wide association study identifies three new susceptibility loci for adult asthma in the Japanese population. Nat. Genet., 2011, 43(9), 893-896.
[http://dx.doi.org/10.1038/ng.887] [PMID: 21804548]
[9]
Yucesoy, B.; Kaufman, K.M.; Lummus, Z.L.; Weirauch, M.T.; Zhang, G.; Cartier, A.; Boulet, L.P.; Sastre, J.; Quirce, S.; Tarlo, S.M.; Cruz, M.J.; Munoz, X.; Harley, J.B.; Bernstein, D.I. Genome-wide association study identifies novel loci associated with diisocyanate-induced occupational asthma. Toxicol. Sci., 2015, 146(1), 192-201.
[http://dx.doi.org/10.1093/toxsci/kfv084] [PMID: 25918132]
[10]
Ramasamy, A.; Kuokkanen, M.; Vedantam, S.; Gajdos, Z.K.; Couto Alves, A.; Lyon, H.N.; Ferreira, M.A.R.; Strachan, D.P.; Zhao, J.H.; Abramson, M.J.; Brown, M.A.; Coin, L.; Dharmage, S.C.; Duffy, D.L.; Haahtela, T.; Heath, A.C.; Janson, C.; Kähönen, M.; Khaw, K.T.; Laitinen, J.; Le Souef, P.; Lehtimäki, T.; Madden, P.A.F.; Marks, G.B.; Martin, N.G.; Matheson, M.C.; Palmer, C.D.; Palotie, A.; Pouta, A.; Robertson, C.F.; Viikari, J.; Widen, E.; Wjst, M.; Jarvis, D.L.; Montgomery, G.W.; Thompson, P.J.; Wareham, N.; Eriksson, J.; Jousilahti, P.; Laitinen, T.; Pekkanen, J.; Raitakari, O.T.; O’Connor, G.T.; Salomaa, V.; Jarvelin, M.R.; Hirschhorn, J.N. Genome-wide association studies of asthma in population-based cohorts confirm known and suggested loci and identify an additional association near HLA. PLoS One, 2012, 7(9), e44008.
[http://dx.doi.org/10.1371/journal.pone.0044008] [PMID: 23028483]
[11]
Ober, C.; Nicolae, D.L.; Chiu, G.Y.; Gauderman, W.J.; Gignoux, C.R.; Graves, P.E.; Himes, B.E.; Levin, A.M.; Mathias, R.A.; Hancock, D.B.; Baurley, J.W.; Eng, C.; Stern, D.A.; Celedón, J.C.; Rafaels, N.; Capurso, D.; Conti, D.V.; Roth, L.A.; Soto-Quiros, M.; Togias, A.; Li, X.; Myers, R.A.; Romieu, I.; Van Den Berg, D.J.; Hu, D.; Hansel, N.N.; Hernandez, R.D.; Israel, E.; Salam, M.T.; Galanter, J.; Avila, P.C.; Avila, L.; Rodriquez-Santana, J.R.; Chapela, R.; Rodriguez-Cintron, W.; Diette, G.B.; Adkinson, N.F.; Abel, R.A.; Ross, K.D.; Shi, M.; Faruque, M.U.; Dunston, G.M.; Watson, H.R.; Mantese, V.J.; Ezurum, S.C.; Liang, L.; Ruczinski, I.; Ford, J.G.; Huntsman, S.; Chung, K.F.; Vora, H.; Li, X.; Calhoun, W.J.; Castro, M.; Sienra-Monge, J.J.; del Rio-Navarro, B.; Deichmann, K.A.; Heinzmann, A.; Wenzel, S.E.; Busse, W.W.; Gern, J.E.; Lemanske, R.F., Jr; Beaty, T.H.; Bleecker, E.R.; Raby, B.A.; Meyers, D.A.; London, S.J.; Gilliland, F.D.; Burchard, E.G.; Martinez, F.D.; Weiss, S.T.; Williams, L.K.; Barnes, K.C.; Ober, C.; Nicolae, D.L. Meta-analysis of genome-wide association studies of asthma in ethnically diverse North American populations. Nat. Genet., 2011, 43(9), 887-892.
[http://dx.doi.org/10.1038/ng.888] [PMID: 21804549]
[12]
Leung, T.F.; Tang, M.F.; Leung, A.S.Y.; Kong, A.P.S.; Liu, T.C.; Chan, R.W.Y.; Ma, R.C.W.; Sy, H.Y.; Chan, J.C.N.; Wong, G.W.K. Cadherin‐related family member 3 gene impacts childhood asthma in Chinese children. Pediatr. Allergy Immunol., 2020, 31(2), 133-142.
[http://dx.doi.org/10.1111/pai.13138] [PMID: 31610042]
[13]
Chen, J.; Zhang, J.; Hu, H.; Jin, Y.; Xue, M. Polymorphisms of RAD50, IL33 and IL1RL1 are associated with atopic asthma in Chinese population. Tissue Antigens, 2015, 86(6), 443-447.
[http://dx.doi.org/10.1111/tan.12688] [PMID: 26493291]
[14]
Marinho, S.; Custovic, A.; Marsden, P.; Smith, J.A.; Simpson, A. 17q12-21 Variants are associated with asthma and interact with active smoking in an adult population from the United Kingdom. Ann. Allergy Asthma Immunol., 2012, 108(6), 402-411.e9.
[http://dx.doi.org/10.1016/j.anai.2012.03.002] [PMID: 22626592]
[15]
Yu, J.; Kang, M.J.; Kim, B.J.; Kwon, J.W.; Song, Y.H.; Choi, W.A.; Shin, Y.J.; Hong, S.J. Polymorphisms in GSDMA and GSDMB are associated with asthma susceptibility, atopy and BHR. Pediatr. Pulmonol., 2011, 46(7), 701-708.
[http://dx.doi.org/10.1002/ppul.21424] [PMID: 21337730]
[16]
Žavbi, M.; Korošec, P.; Fležar, M.; Škrgat Kristan, S.; Marc Malovrh, M.; Rijavec, M. Polymorphisms and haplotypes of the chromosome locus 17q12-17q21.1 contribute to adult asthma susceptibility in Slovenian patients. Hum. Immunol., 2016, 77(6), 527-534.
[http://dx.doi.org/10.1016/j.humimm.2016.05.003] [PMID: 27163155]
[17]
Ullemar, V.; Magnusson, P.K.E.; Lundholm, C.; Zettergren, A.; Melén, E.; Lichtenstein, P.; Almqvist, C. Heritability and confirmation of genetic association studies for childhood asthma in twins. Allergy, 2016, 71(2), 230-238.
[http://dx.doi.org/10.1111/all.12783] [PMID: 26786172]
[18]
Sun, Y.; Wei, X.; Deng, J.; Zhang, J.; He, Z.; Yang, M.; Liang, S.; Chen, Z.; Qin, H. Association of IL1RL1 rs3771180 and TSLP rs1837253 variants with asthma in the Guangxi Zhuang population in China. J. Clin. Lab. Anal., 2019, 33(6), e22905.
[http://dx.doi.org/10.1002/jcla.22905] [PMID: 31066119]
[19]
Liang, S.Q.; Deng, J.M.; Wei, X.; Chen, Z.R.; Yang, M.L.; Qin, H.; Zhang, J.; He, Z. Association of GWAS‐supported noncoding area loci rs404860, rs3117098, and rs7775228 with asthma in Chinese Zhuang population. J. Clin. Lab. Anal., 2020, 34(2), e23066.
[http://dx.doi.org/10.1002/jcla.23066] [PMID: 31605414]
[20]
Granell, R.; Henderson, A.J.; Evans, D.M.; Smith, G.D.; Ness, A.R.; Lewis, S.; Palmer, T.M.; Sterne, J.A.C. Effects of BMI, fat mass, and lean mass on asthma in childhood: A Mendelian randomization study. PLoS Med., 2014, 11(7), e1001669.
[http://dx.doi.org/10.1371/journal.pmed.1001669] [PMID: 24983943]
[21]
Skaaby, T.; Taylor, A.E.; Jacobsen, R.K.; Paternoster, L.; Thuesen, B.H.; Ahluwalia, T.S.; Larsen, S.C.; Zhou, A.; Wong, A.; Gabrielsen, M.E.; Bjørngaard, J.H.; Flexeder, C.; Männistö, S.; Hardy, R.; Kuh, D.; Barry, S.J.; Tang Møllehave, L.; Cerqueira, C.; Friedrich, N.; Bonten, T.N.; Noordam, R.; Mook-Kanamori, D.O.; Taube, C.; Jessen, L.E.; McConnachie, A.; Sattar, N.; Upton, M.N.; McSharry, C.; Bønnelykke, K.; Bisgaard, H.; Schulz, H.; Strauch, K.; Meitinger, T.; Peters, A.; Grallert, H.; Nohr, E.A.; Kivimaki, M.; Kumari, M.; Völker, U.; Nauck, M.; Völzke, H.; Power, C.; Hyppönen, E.; Hansen, T.; Jørgensen, T.; Pedersen, O.; Salomaa, V.; Grarup, N.; Langhammer, A.; Romundstad, P.R.; Skorpen, F.; Kaprio, J.; R., Munafò M.; Linneberg, A. Investigating the causal effect of smoking on hay fever and asthma: A Mendelian randomization meta-analysis in the CARTA consortium. Sci. Rep., 2017, 7(1), 2224.
[http://dx.doi.org/10.1038/s41598-017-01977-w] [PMID: 28533558]
[22]
Zhao, J.V.; Schooling, C.M. The role of linoleic acid in asthma and inflammatory markers: A Mendelian randomization study. Am. J. Clin. Nutr., 2019, 110(3), 685-690.
[http://dx.doi.org/10.1093/ajcn/nqz130] [PMID: 31287141]
[23]
Nuzzo, R. Scientific method: Statistical errors. Nature, 2014, 506(7487), 150-152.
[http://dx.doi.org/10.1038/506150a] [PMID: 24522584]
[24]
Antonucci, L.A.; Pergola, G.; Pigoni, A.; Dwyer, D.; Kambeitz-Ilankovic, L.; Penzel, N.; Romano, R.; Gelao, B.; Torretta, S.; Rampino, A.; Trojano, M.; Caforio, G.; Falkai, P.; Blasi, G.; Koutsouleris, N.; Bertolino, A. A pattern of cognitive deficits stratified for genetic and environmental risk reliably classifies patients with schizophrenia from healthy control subjects. Biol. Psychiatry, 2020, 87(8), 697-707.
[http://dx.doi.org/10.1016/j.biopsych.2019.11.007] [PMID: 31948640]
[25]
Li, C.; Sun, D.; Liu, J.; Li, M.; Zhang, B.; Liu, Y.; Wang, Z.; Wen, S.; Zhou, J. A prediction model of essential hypertension based on genetic and environmental risk factors in northern han chinese. Int. J. Med. Sci., 2019, 16(6), 793-799.
[26]
Guido, S.; Müller, A.C. Introduction to machine learning with Python: A guide for Data Scientists; O'Reilly Media, Inc.: Sebastopol, 2016, pp. 123-145.
[27]
Chen, T.; Guestrin, C. C: XGBoost: A scalable tree boosting system. arXiv:1603.02754, 2016.
[http://dx.doi.org/10.1145/2939672.2939785]
[28]
Li, L.; Zhang, X. Study of Data Mining Algorithm Based on Decision Tree. In: 2010 International Conference On Computer Design and Applications, IEEE 2010.
[29]
Cortes, C.; Vapnik, V. Support-vector networks. Mach. Learn., 1995, 20, 273-297.
[30]
Ho, T.K. Random decision forests. Proceedings of the 3rd International Conference on Document Analysis and Recognition 278, 1995, p. 282.
[31]
Gaudillo, J.; Rodriguez, J.J.R.; Nazareno, A.; Baltazar, L.R.; Vilela, J.; Bulalacao, R.; Domingo, M.; Albia, J. Machine learning approach to single nucleotide polymorphism-based asthma prediction. PLoS One, 2019, 14(12), e0225574.
[http://dx.doi.org/10.1371/journal.pone.0225574] [PMID: 31800601]
[32]
Los, H.; Postmus, P.E.; Boomsma, D.I. Asthma genetics and intermediate phenotypes: A review from twin studies. Twin Res., 2001, 4(2), 81-93.
[http://dx.doi.org/10.1375/1369052012191] [PMID: 11665340]
[33]
AlSaad, R.; Malluhi, Q.; Janahi, I.; Boughorbel, S. Interpreting patient-Specific risk prediction using contextual decomposition of BiLSTMs: Application to children with asthma. BMC Med. Inform. Decis. Mak., 2019, 19(1), 214.
[http://dx.doi.org/10.1186/s12911-019-0951-4] [PMID: 31703676]
[34]
Ogunleye, A; Wang, QG XGBoost model for chronic kidney disease diagnosis. IEEE/ACM Trans Comput Biol Bioinform., 2020, 17(6), 2131-2140.
[http://dx.doi.org/10.1109/TCBB.2019.2911071]
[35]
Yu, D.; Liu, Z.; Su, C.; Han, Y.; Duan, X.; Zhang, R.; Liu, X.; Yang, Y.; Xu, S. Copy number variation in plasma as a tool for lung cancer prediction using Extreme Gradient Boosting (XGBoost) classifier. Thorac. Cancer, 2020, 11(1), 95-102.
[http://dx.doi.org/10.1111/1759-7714.13204] [PMID: 31694073]
[36]
Liu, L.; Yu, Y.; Fei, Z.; Li, M.; Wu, F.X.; Li, H.D.; Pan, Y.; Wang, J. An interpretable boosting model to predict side effects of analgesics for osteoarthritis. BMC Syst. Biol., 2018, 12(S6)(Suppl. 6), 105.
[http://dx.doi.org/10.1186/s12918-018-0624-4] [PMID: 30463545]
[37]
Ji, X.; Tong, W.; Liu, Z.; Shi, T. Five-feature model for developing the classifier for synergistic vs. antagonistic drug combinations built by XGBoost. Front. Genet., 2019, 10, 600.
[http://dx.doi.org/10.3389/fgene.2019.00600] [PMID: 31338106]
[38]
Ding, W.; Chen, G.; Shi, T. Integrative analysis identifies potential DNA methylation biomarkers for pan-cancer diagnosis and prognosis. Epigenetics, 2019, 14(1), 67-80.
[http://dx.doi.org/10.1080/15592294.2019.1568178] [PMID: 30696380]
[39]
Fu, B.; Liu, P.; Lin, J.; Deng, L.; Hu, K.; Zheng, H. Predicting invasive disease-free survival for early-stage breast cancer patients using follow-up clinical data. IEEE Trans. Biomed. Eng., 2018.
[PMID: 30475709]

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