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Identification of functional SNPs in BARD1 gene and in silico analysis of damaging SNPs: based on data procured from

Ali A Alshatwi1, Tarique N Hasan, Naveed A Syed

  • 1Molecular Cancer Biology Research Laboratory, Department of Food Science and Nutrition, King Saud University, Riyadh, Saudi Arabia. alialshatwi@gmail.com

Plos One
|October 12, 2012
PubMed
Abstract

Insights

This study identified four deleterious single nucleotide polymorphisms (SNPs) in the BARD1 gene, rs4986841, rs111367604, rs13389423, and rs139785364, which reduce protein stability and are promising for future female cancer research.

Area of Science:

  • Genetics
  • Bioinformatics
  • Cancer Research

Background:

  • The BARD1 gene is crucial for the BRCA1-associated RING domain protein.
  • Mutations in BARD1 are linked to sporadic breast, ovarian, and uterine cancers.
  • Single nucleotide polymorphisms (SNPs) in BARD1 may influence female cancer development.

Purpose of the Study:

  • To identify functional non-synonymous SNPs (nsSNPs) in the BARD1 gene.
  • To analyze the potential impact of these nsSNPs on BARD1 protein function and stability.
  • To provide data for larger population-based studies on female cancers.

Main Methods:

  • Utilized the dbSNP database for SNP identification.
  • Employed bioinformatics tools including SIFT, PolyPhen, FastSNP, PDBSum, I-Mutant, and ExPASy-PROSIT.
  • Analyzed SNPs in coding and non-coding regions (5' UTR, 3' UTR, intronic).

Main Results:

  • Identified 50 nsSNPs in BARD1, with 11 predicted as not tolerable and 27 as damaging by SIFT and PolyPhen.
  • rs58253676 (3' UTR) may affect splicing; rs17489363 and rs17426219 (5' UTR) may alter transcriptional binding.
  • Four nsSNPs (rs4986841, rs111367604, rs13389423, rs139785364) were identified as deleterious, decreasing BARD1 protein stability.

Conclusions:

  • Four specific nsSNPs in BARD1 (rs4986841, rs111367604, rs13389423, rs139785364) are predicted to be deleterious and reduce protein stability.
  • These identified nsSNPs are valuable candidates for future large-scale population studies on female cancers.
  • Bioinformatic analysis provides a robust method for prioritizing functional SNPs in cancer-related genes.

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