Chronic Kidney Disease (CKD) Phenotype and Its Association with Single Nuclotide Polymorpisms (SNPs) using Elastic Net Method

Authors

  • Maureen Zerlina Oktaviani, Angga Aditya Permana, Analekta Tiara Perdana

Keywords:

chronic kidney disease (CKD), elastic net, single nucleotide polymorphism (SNP).

Abstract

Chronic kidney disease (CKD) is one of critical disorders that typically has no symptoms until late stages. Early intervention in its initial stages can delay this disease progression. Single nucleotide polymorphism (SNP) can be used as a genetic variation that can be observed with CKD phenotype. Association study of SNP and CKD phenotype needs assistance from a machine learning algorithm that can process data with many dimensions. In this research, Elastic Net used as a method to associate SNP with phenotype for CKD. Based on the result, it showed that the Elastic Net method can select 88 significant SNP with a low MAE score that is 0.755 with a good explained variance (R2) which is 0.999. Also, there are significant SNP that were chosen with Elastic Net method such as Rhd, F2, Col1a1, Nos3, F5, Gypc, F8, F10, Bcam, Rbp4, Plg, Thpo, Myh9, Akt1, Fgb, and F7.

Downloads

Download data is not yet available.

References

R. A. Wijayanti, M. T. Furqon, and S. Adinugroho, “Penerapan Algoritme Support Vector Machine Terhadap Klasifikasi Tingkat Risiko Pasien Gagal Ginjal,” 2018. [Online]. Available: http://j-ptiik.ub.ac.id

Yulianti, R. Amegia Saputra, M. Sukrisno Mardiyanto, and A. Rahmawati, “Optimasi Akurasi Algoritma C4.5 Berbasis Particle Swarm Optimization dengan Teknik Bagging pada Prediksi Penyakit Ginjal Kronis Optimization of C4.5 Algorithm Based On Particle Swarm Optimization with Bagging Technique on Prediction of Chronic Kidney Disease,” 2020. [Online]. Available: https://archive.ics.uci.edu/ml/

O. : Anggun, H. Safitri, ) Dinar, S. E. Dewi, and ) Abstrak, “DESKRIPSI TINGKAT HARAPAN PADA PENDERITA GAGAL GINJAL KRONIK DI RSU PROF DR. MARGONO SOEKARJO PURWOKERTO DESCRIPTION OF HOPE IN CHRONIC RENAL FAILURE PATIENTS IN RSU PROF DR. MARGONO SOEKARJO PURWOKERTO.”

“InfoDATIN,” 2006. [Online]. Available: http://emojione.com

U. N. Semarang, M. Masa, D. Pendidikan, I. Hayati, P. Pandemi, and T. N. Azhar1, “Prosiding Seminar Nasional Biologi X FMIPA.”

B. Li et al., “GPCards: An integrated database of genotype–phenotype correlations in human genetic diseases,” Comput Struct Biotechnol J, vol. 19, pp. 1603–1611, Jan. 2021, doi: 10.1016/j.csbj.2021.03.011.

Meiliana, N. M. Dewi, and A. Wijaya, “Metabolomics: An Emerging Tool for Precision Medicine,” Indonesian Biomedical Journal, vol. 13, no. 1, pp. 1–18, 2021, doi: 10.18585/inabj.v13i1.1309.

R. L. Perlman, “Mouse Models of Human Disease: An Evolutionary Perspective,” Evol Med Public Health, p. eow014, Apr. 2016, doi: 10.1093/emph/eow014.

J. P. Ly, T. Onay, and S. E. Quaggin, “Mouse models to study kidney development, function and disease,” Current Opinion in Nephrology and Hypertension, vol. 20, no. 4. pp. 382–390, Jul. 2011. doi: 10.1097/MNH.0b013e328347cd4a.

G. Research, “Help Me Understand Genetics.” [Online]. Available: https://medlineplus.gov/genetics/

Y. Dwiningsih, M. Rahmaningsih, and J. Alkahtani, “Development of Single Nucleotide Polymorphism (SNP) Markers in Tropical Crops,” Advance Sustainable Science, Engineering and Technology, vol. 2, no. 2, Jul. 2020, doi: 10.26877/asset.v2i2.6279.

H. Suprapti, B. Farmakologi, F. Kedokteran, U. Wijaya, and K. Surabaya, “Farmakogenomik Statin: Biomarker untuk Prediksi Klinis,” Online) Jurnal Ilmiah Kedokteran Wijaya Kusuma, vol. 7, no. 1. pp. 1–14, 2018.

T. Marees et al., “A tutorial on conducting genome-wide association studies: Quality control and statistical analysis,” Int J Methods Psychiatr Res, vol. 27, no. 2, Jul. 2018, doi: 10.1002/mpr.1608.

T. Nguyen and L. Le, “Detection of SNP-SNP Interactions in Genome-wide Association Data Using Random Forests and Association Rules,” in 2018 12th International Conference on Software, Knowledge, Information Management & Applications (SKIMA), 2018, pp. 1–7. doi: 10.1109/SKIMA.2018.8631529.

Adiwijaya, U. N. Wisesty, E. Lisnawati, A. Aditsania, and D. S. Kusumo, “Dimensionality reduction using Principal Component Analysis for cancer detection based on microarray data classification,” Journal of Computer Science, vol. 14, no. 11, pp. 1521–1530, 2018, doi: 10.3844/jcssp.2018.1521.1530.

L. H. Tresnawati, W. A. Kusuma, S. H. Wijaya, and L. S. Hasibuan, “ Asosiasi Single Nucleotide Polymorphism pada Diabetes Mellitus Tipe 2 Menggunakan Random Forest Regression,” Jurnal Nasional Teknik Elektro dan Teknologi Informasi, vol. 8, no. 4, pp. 320–326, Nov. 2019, [Online]. Available: https://jurnal.ugm.ac.id/v3/JNTETI/article/view/2556

“ASOSIASI SINGLE NUCLEOTIDE POLYMORPHISM DAN FENOTIPE PADA PENYAKIT DIABETES MELLITUS TIPE 2 MENGGUNAKAN STEPWISE REGRESSION DEVY APRIANSYAH.”

R. M. Siregar, “Asosiasi single nucleotide polymorphism pada penyakit diabetes mellitus tipe 2 menggunakan support vector regression dan genetic algorithm,” IPB University.

H. F. Ramadhani, W. A. Kusuma, L. S. Hasibuan, and R. Heryanto, “Association of single nucleotide polymorphism and phenotypes in type 2 diabetes mellitus using genetic algorithm and catboost,” in 2020 International Conference on Computer Science and Its Application in Agriculture, ICOSICA 2020, Institute of Electrical and Electronics Engineers Inc., Jul. 2020. doi: 10.1109/ICOSICA49951.2020.9243208.

Fadli, L. S. Hasibuan, W. A. Kusuma, and R. Heryanto, “Single nucleotide polymorphism and type 2 diabetes mellitus phenotypes association using gradient boosting,” in 2020 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2020, Institute of Electrical and Electronics Engineers Inc., Jul. 2020, pp. 115–120. doi: 10.1109/ICACSIS51025.2020.9263142.

M. F. Romdendine, “ASOSIASI SINGLE NUCLEOTIDE POLYMORPHISM DAN FENOTIPE PADA PENYAKIT DIABETES MELLITUS TIPE 2 MENGGUNAKAN METODE ELASTIC NET,” 2022.

H. Hanum, “Perbandingan Metode Stepwise, Best Subset Regression, dan Fraksi dalam Pemilihan Model Regresi Berganda Terbaik,” Jurnal Penelitian Sains, vol. 14. p. 14201.

N. D. Maulana, B. D. Setiawan, and C. Dewi, “Implementasi Metode Support Vector Regression (SVR) Dalam Peramalan Penjualan Roti (Studi Kasus: Harum Bakery),” vol. 3, no. 3. pp. 2986–2995, 2019. [Online]. Available: http://j-ptiik.ub.ac.id

M. Tingkatkemanisan, M. Berdasarkan, and F. Warna, “Klasifikasi Support Vector Machine (SVM) Untuk,” MIND Journal | ISSN, vol. 3, no. 2, pp. 16–24, 2018, doi: 10.26760/mindjournal.

Comber and P. Harris, “Geographically weighted elastic net logistic regression,” J Geogr Syst, vol. 20, no. 4, pp. 317–341, Jul. 2018, doi: 10.1007/s10109-018-0280-7.

H. Yang et al., “Subspecific origin and haplotype diversity in the laboratory mouse,” in Nature Genetics, Jul. 2011, pp. 648–655. doi: 10.1038/ng.847.

P. Sinke et al., “Genetic analysis of mouse strains with variable serum sodium concentrations identifies the Nalcn sodium channel as a novel player in osmoregulation,” Physiol Genomics, vol. 43, pp. 265–270, 2011, doi: 10.1152/physiolgenomics.00188.2010.-In.

J. Van De Wouw and J. A. Joles, “Albumin is an interface between blood plasma and cell membrane, and not just a sponge,” Clinical Kidney Journal, vol. 15, no. 4. Oxford University Press, pp. 624–634, Apr. 01, 2022. doi: 10.1093/ckj/sfab194.

Ilhan and G. Tezel, “How to select tag SNPs in genetic association studies? the CLONTagger method with parameter optimization,” OMICS, vol. 17, no. 7, pp. 368–383, Jul. 2013, doi: 10.1089/omi.2012.0100.

H. Zou and T. Hastie, “Erratum: Regularization and variable selection via the elastic net (Journal of the Royal Statistical Society. Series B: Statistical Methodology (2005) 67 (301-320)),” J R Stat Soc Series B Stat Methodol, vol. 67, no. 5, p. 768, 2005, doi: 10.1111/j.1467-9868.2005.00527.x.

Technology University of Oradea. Faculty of Electrical Engineering and Information, IEEE Romania Section. CAS/CA Chapter, B. Association of Romanian Electrical and Electronics Engineers, O. Association of Integrated Engineering and Industrial Management, and Institute of Electrical and Electronics Engineers, 2017 14th International Conference on Engineering of Modern Electric Systems (EMES) : Oradea, România, June 01-02, 2017.

Sukmana, “Koefisien Determinasi R^2 pada Model Regresi Linear,” 1996.

C. Cameron and F. A. G. Windmeijer, “An R-squared measure of goodness of fit for some common nonlinear regression models.”

N. Rungroj et al., “Association between Human Prothrombin Variant (T165M) and Kidney Stone Disease,” PLoS One, vol. 7, no. 9, Sep. 2012, doi: 10.1371/journal.pone.0045533.

M. C. Menon et al., “Intronic locus determines SHROOM3 expression and potentiates renal allograft fibrosis,” Journal of Clinical Investigation, vol. 125, no. 1, pp. 208–221, Jan. 2015, doi: 10.1172/JCI76902.

F. Persson et al., “Endothelial dysfunction and inflammation predict development of diabetic nephropathy in the Irbesartan in Patients with Type 2 Diabetes and Microalbuminuria (IRMA 2) study,” Scand J Clin Lab Invest, vol. 68, no. 8, pp. 731–738, Dec. 2008, doi: 10.1080/00365510802187226.

M. Medina et al., “NOS3 Polymorphisms and Chronic Kidney Disease,” Jornal brasileiro de nefrologia : ’orgao oficial de Sociedades Brasileira e Latino-Americana de Nefrologia, vol. 40, no. 3. NLM (Medline), pp. 273–277, Jul. 01, 2018. doi: 10.1590/2175-8239-JBN-3824.

Q. Ying et al., “The rs13347 Polymorphism of the CD44 Gene Is Associated with the Risk of Kidney Stones Disease in the Chinese Han Population of Northeast Sichuan, China,” Comput Math Methods Med, vol. 2022, 2022, doi: 10.1155/2022/6481260.

polymorphisms with chronic kidney disease in Japanese individuals,” Int J Mol Med, vol. 24, no. 4, pp. 539–547, 2009, doi: 10.3892/ijmm_00000263.

J. Huang et al., “Lutheran/basal cell adhesion molecule accelerates progression of crescentic glomerulonephritis in mice,” Kidney Int, vol. 85, no. 5, pp. 1123–1136, 2014, doi: 10.1038/ki.2013.522.

Z. Xiong et al., “RNA sequencing reveals upregulation of RUNX1-RUNX1T1 gene signatures in clear cell renal cell carcinoma,” Biomed Res Int, vol. 2014, 2014, doi: 10.1155/2014/450621.

S. Li et al., “Integrin β3 Induction Promotes Tubular Cell Senescence and Kidney Fibrosis,” Front Cell Dev Biol, vol. 9, Nov. 2021, doi: 10.3389/fcell.2021.733831.

Bruel et al., “Hemolytic uremic syndrome in pregnancy and postpartum,” Clinical Journal of the American Society of Nephrology, vol. 12, no. 8, pp. 1237–1247, 2017, doi: 10.2215/CJN.00280117.

[B. Manickavasagar et al., “Hypervitaminosis A is prevalent in children with CKD and contributes to hypercalcemia,” Pediatric Nephrology, vol. 30, no. 2, pp. 317–325, Aug. 2014, doi: 10.1007/s00467-014-2916-2.

G. S. Di Marco et al., “Soluble Flt-1 links microvascular disease with heart failure in CKD,” Basic Res Cardiol, vol. 110, no. 3, Apr. 2015, doi: 10.1007/s00395-015-0487-4.

H. A. Tzanatos, P. P. Tseke, C. Pipili, K. Retsa, G. Skoutelis, and E. Grapsa, “Cardiovascular risk factors in non-diabetic hemodialysis patients: A comparative study,” Ren Fail, vol. 31, no. 2, pp. 91–97, Feb. 2009, doi: 10.1080/08860220802595484.

T. Matsumoto et al., “Renal Biopsy-induced Hematoma and Infection in a Patient with Asymptomatic May-Hegglin Anomaly,” Journal of Nippon Medical School, vol. 88, no. 6, pp. 579–584, 2021, doi: 10.1272/JNMS.JNMS.2021_88-609.

S. Kwon et al., “Apolipoprotein B is a risk factor for end-stage renal disease,” Clin Kidney J, vol. 14, no. 2, pp. 617–623, Feb. 2021, doi: 10.1093/ckj/sfz186.

Y. Momoi et al., “Heterogenous expression of endoglin marks advanced renal cancer with distinct tumor microenvironment fitness,” Cancer Sci, vol. 112, no. 8, pp. 3136–3149, Aug. 2021, doi: 10.1111/cas.15007.

J. Korczynska, A. Czumaj, M. Chmielewski, J. Swierczynski, and T. Sledzinski, “The causes and potential injurious effects of elevated serum leptin levels in chronic kidney disease patients,” International Journal of Molecular Sciences, vol. 22, no. 9. MDPI, May 01, 2021. doi: 10.3390/ijms22094685.

N. L. Kozlovskaya et al., “Clinicomorphological characteristics of renal disorders in patients with genetic thrombophilia,” Ter Arkh, vol. 81, no. 8, pp. 30–36, 2009, [Online]. Available: https://ter-arkhiv.ru/0040-3660/article/view/30483

H. Y. H. Lin et al., “Tubular mitochondrial AKT1 is activated during ischemia reperfusion injury and has a critical role in predisposition to chronic kidney disease,” Kidney Int, vol. 99, no. 4, pp. 870–884, Apr. 2021, doi: 10.1016/j.kint.2020.10.038.

Downloads

Published

24.03.2024

How to Cite

Angga Aditya Permana, Analekta Tiara Perdana, M. Z. O. (2024). Chronic Kidney Disease (CKD) Phenotype and Its Association with Single Nuclotide Polymorpisms (SNPs) using Elastic Net Method. International Journal of Intelligent Systems and Applications in Engineering, 12(3), 1996–2004. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5665

Issue

Vol. 12 No. 3 (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.