Combined sequence and sequence-structure based methods for analyzing FGF23, CYP24A1 and VDR genes.
FGF23, CYP24A1 and VDR altogether play a significant role in genetic susceptibility to chronic kidney disease (CKD). Identification of possible causative mutations may serve as therapeutic targets and diagnostic markers for CKD. Thus, we adopted both sequence and sequence-structure based SNP analysis algorithm in order to overcome the limitations of both methods. We explore the functional significance towards the prediction of risky SNPs associated with CKD. We assessed the performance of four widely used pathogenicity prediction methods. We compared the performances of the programs using Mathews correlation Coefficient ranged from poor (MCC = 0.39) to reasonably good (MCC = 0.42). However, we got the best results for the combined sequence and structure based analysis method (MCC = 0.45). 4 SNPs from FGF23 gene, 8 SNPs from VDR gene and 13 SNPs from CYP24A1 gene were predicted to be the causative agents for human diseases. This study will be helpful in selecting potential SNPs for experimental study from the SNP pool and also will reduce the cost for identification of potential SNPs as a genetic marker.
Department of Bioinformatics, Alagappa University, Karaikudi 630 004, Tamilnadu, India.
Selvaraman
S
Nagamani
Department of Bioinformatics, Alagappa University, Karaikudi 630 004, Tamilnadu, India.
Kh Dhanachandra
KhD
Singh
Department of Bioinformatics, Alagappa University, Karaikudi 630 004, Tamilnadu, India.
Karthikeyan
K
Muthusamy
CYP24A1; Chronic kidney disease; Combined sequence and sequence-structure based methods; FGF23; SNP analysis; VDR