Identifying Clinical and Genomic Features Associated With Chronic Kidney Disease

M Megan Moreno; Travaughn C Bain; Melissa S Moreno; Katherine C Carroll; Emily R Cunningham; Zoe Ashton; Roby Poteau; Ersoy Subasi; Michael Lipkowitz; Munevver Mine Subasi

doi:10.3389/fdata.2020.528828

Identifying Clinical and Genomic Features Associated With Chronic Kidney Disease

Front Big Data. 2021 Jan 14:3:528828. doi: 10.3389/fdata.2020.528828. eCollection 2020.

Authors

M Megan Moreno¹, Travaughn C Bain¹, Melissa S Moreno¹, Katherine C Carroll^{1

2

3}, Emily R Cunningham^{1

2

4}, Zoe Ashton¹, Roby Poteau¹, Ersoy Subasi⁵, Michael Lipkowitz⁶, Munevver Mine Subasi¹

Affiliations

¹ Department of Mathematical Sciences, Florida Institute of Technology, Melbourne, FL, United States.
² Department of Biomedical and Chemical Engineering and Sciences, Melbourne, FL, United States.
³ Department of Biology, University of Florida, Gainesville, FL, United States.
⁴ Department of Mathematics, SUNY Potsdam, Potsdam, NY, United States.
⁵ Department of Computer Engineering and Sciences, Florida Institute of Technology, Melbourne, FL, United States.
⁶ Department of Medicine, Georgetown University Medical Center, Washington, DC, United States.

Abstract

We apply a pattern-based classification method to identify clinical and genomic features associated with the progression of Chronic Kidney disease (CKD). We analyze the African-American Study of Chronic Kidney disease with Hypertension dataset and construct a decision-tree classification model, consisting 15 combinatorial patterns of clinical features and single nucleotide polymorphisms (SNPs), seven of which are associated with slow progression and eight with rapid progression of renal disease among African-American Study of Chronic Kidney patients. We identify four clinical features and two SNPs that can accurately predict CKD progression. Clinical and genomic features identified in our experiments may be used in a future study to develop new therapeutic interventions for CKD patients.

Keywords: AASK; chronic kidney disease; classification; decision trees; genomic analysis.