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Related Experiment Videos

Large-scale analysis of non-synonymous coding region single nucleotide polymorphisms.

Robert J Clifford1, Michael N Edmonson, Cu Nguyen

  • 1Laboratory of Population Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.

Bioinformatics (Oxford, England)
|January 31, 2004
PubMed
Summary
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Single nucleotide polymorphisms (SNPs) that alter amino acids can impact protein function and disease susceptibility. A new method using HMMER E-value changes effectively predicts deleterious variants among coding SNPs.

Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Single nucleotide polymorphisms (SNPs) are common genetic variations in humans.
  • SNPs causing amino acid substitutions are key candidates for complex disease susceptibility loci (e.g., diabetes, hypertension).
  • Distinguishing neutral from deleterious variants is crucial for efficient SNP screening in disease association studies.

Purpose of the Study:

  • To develop and validate a computational method for predicting the functional impact of amino acid substitutions in conserved protein domains.
  • To assess the utility of HMMER E-value changes as a predictor of deleterious non-synonymous SNPs.
  • To provide accessible tools for exploring genomewide SNP data.

Main Methods:

  • Utilized Pfam protein motif models and the HMMER program to analyze amino acid changes.

Related Experiment Videos

  • Evaluated the correlation between the magnitude of HMMER E-value change and the deleteriousness of amino acid substitutions.
  • Developed internet-accessible display tools for a genomewide collection of SNPs.

    • Main Results:

    • The change in HMMER E-value is a reliable predictor of whether an amino acid substitution is deleterious.
    • Identified 7391 distinct non-synonymous coding region SNPs across 2683 genes.
    • Developed publicly accessible tools for viewing and analyzing these SNPs.

    Conclusions:

    • The HMMER E-value change effectively predicts the functional impact of amino acid substitutions in conserved protein domains.
    • This approach facilitates the distinction between neutral and deleterious SNPs, aiding disease association studies.
    • The provided tools offer valuable resources for researchers investigating the role of genetic variation in complex diseases.