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Updated: Mar 22, 2026

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

Published on: October 5, 2018

Combined sequence and sequence-structure based methods for analyzing FGF23, CYP24A1 and VDR genes.

Selvaraman Nagamani¹, Kh Dhanachandra Singh¹, Karthikeyan Muthusamy¹

¹Department of Bioinformatics, Alagappa University, Karaikudi 630 004, Tamilnadu, India.

|April 27, 2016

Summary

Researchers identified specific genetic variations (SNPs) in FGF23, CYP24A1, and VDR genes linked to chronic kidney disease (CKD). A combined sequence and structure analysis improved prediction accuracy for these disease-causing SNPs.

Keywords:

CYP24A1 Chronic kidney disease Combined sequence and sequence-structure based methods FGF23 SNP analysis VDR

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Published on: January 25, 2019

Area of Science:

Genetics
Nephrology
Bioinformatics

Background:

Genetic factors, including the FGF23, CYP24A1, and VDR genes, are implicated in chronic kidney disease (CKD) susceptibility.
Identifying causative mutations is crucial for developing diagnostic markers and therapeutic strategies for CKD.
Existing single nucleotide polymorphism (SNP) analysis methods have limitations.

Purpose of the Study:

To develop and validate a combined sequence and sequence-structure based algorithm for predicting disease-causing SNPs associated with CKD.
To identify specific SNPs within the FGF23, CYP24A1, and VDR genes that confer genetic risk for CKD.
To enhance the accuracy of SNP pathogenicity prediction beyond traditional methods.

Main Methods:

Utilized a novel combined sequence and sequence-structure based SNP analysis algorithm.
Assessed the performance of four widely used pathogenicity prediction methods.
Compared prediction accuracy using the Mathews Correlation Coefficient (MCC).

Main Results:

The combined sequence and structure-based method achieved the highest prediction accuracy (MCC = 0.45), outperforming individual methods (MCC range: 0.39–0.42).
Identified 4 causative SNPs in the FGF23 gene, 8 in the VDR gene, and 13 in the CYP24A1 gene.
The study successfully predicted SNPs associated with human diseases, specifically in the context of CKD.

Conclusions:

A combined sequence and structure-based approach offers superior accuracy for predicting disease-causing SNPs in CKD.
This method aids in pinpointing specific genetic markers (SNPs) in FGF23, CYP24A1, and VDR genes relevant to CKD.
The findings facilitate cost-effective selection of potential SNPs for experimental validation and biomarker development in CKD research.