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Combined sequence and sequence-structure-based methods for analyzing RAAS gene SNPs: a computational approach.

Kh Dhanachandra Singh1, Muthusamy Karthikeyan

  • 1Department of Bioinformatics, Alagappa University , Karaikudi, Tamil Nadu , India.

Journal of Receptor and Signal Transduction Research
|June 1, 2014
PubMed
Summary

Genetic mutations in the renin-angiotensin-aldosterone system (RAAS) can influence blood pressure. This study identifies potentially harmful genetic variations (SNPs) in RAAS genes, aiding hypertension research and drug development.

Keywords:
HypertensionRAASSNPmutation

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Area of Science:

  • Genetics
  • Cardiovascular Physiology
  • Bioinformatics

Background:

  • The renin-angiotensin-aldosterone system (RAAS) is crucial for blood pressure regulation.
  • Genetic variations (SNPs) in RAAS genes are linked to hypertension susceptibility.
  • Analyzing functional SNPs from large datasets is computationally challenging.

Purpose of the Study:

  • To computationally identify deleterious single nucleotide polymorphisms (SNPs) within coding regions of RAAS genes.
  • To provide insights into the functional significance of genetic mutations in hypertension.
  • To identify potential therapeutic targets and diagnostic markers for hypertension.

Main Methods:

  • Utilized a combined sequence and sequence-structure-based SNP analysis algorithm.
  • Screened 3864 SNPs from dbSNP, focusing on missense SNPs in coding regions.
  • Applied multiple prediction tools and structural analysis (RMSD) to identify deleterious SNPs.

Main Results:

  • Identified 108 missense SNPs in RAAS genes.
  • Reported deleterious SNPs in REN (2), AGT (8), ACE (3), AT1R (2), CYP11B2 (3), and CMA1 (3) genes.
  • High RMSD values correlated with predicted deleterious effects.

Conclusions:

  • The computational approach effectively identifies potentially harmful SNPs in RAAS genes.
  • This method reduces time and cost for prioritizing SNPs for experimental validation.
  • Identified deleterious SNPs can serve as diagnostic markers and potential therapeutic targets for hypertension.