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Mouse SNP Miner: an annotated database of mouse functional single nucleotide polymorphisms.

Eli Reuveni1, Vasily E Ramensky, Cornelius Gross

  • 1Mouse Biology Unit, EMBL, Via Ramarini 32, 00016 Monterotondo, Italy. reuveni@embl.it

BMC Genomics
|January 24, 2007
PubMed
Summary

This study introduces Mouse SNP Miner, a database of mouse single nucleotide polymorphisms (SNPs) predicted to alter protein function. This tool aids in identifying genetic variations linked to complex traits and diseases in mice.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Quantitative Trait Loci (QTL) analysis

Background:

  • Mapping quantitative trait loci (QTLs) in rodents has identified chromosomal regions associated with human disease phenotypes.
  • Identifying specific DNA variations within QTLs that cause these phenotypes remains a significant challenge.
  • High-density sequence variation between mouse strains complicates the identification of functional DNA sequence changes within QTLs.

Purpose of the Study:

  • To develop a database of mouse single nucleotide polymorphisms (SNPs) predicted to cause functional mutations.
  • To aid researchers in identifying phenotype-causing DNA sequence variations within QTLs.
  • To facilitate the discovery of genetic underpinnings of complex traits and diseases in mouse models.

Main Methods:

Related Experiment Videos

  • Compiled a database of mouse SNPs predicted to cause missense, nonsense, frameshift, or splice-site mutations using Ensembl genome sequence.
  • Utilized PolyPhen and PANTHER algorithms to predict the functional impact of missense mutations.
  • Integrated gene expression and functional annotations from Symatlas, Gene Ontology, and OMIM databases.

    • Main Results:

    • Developed a database of mouse SNPs with predicted functional consequences.
    • Analysis indicated that 20% of missense SNPs are likely deleterious (PolyPhen), 14% (PANTHER), and 6% by both.
    • Demonstrated utility by identifying a known SNP in Tas1r3 related to sucrose preference and a novel set of candidate mutations for morphine response.

    Conclusions:

    • The developed database and associated tool, Mouse SNP Miner, effectively aids in identifying candidate phenotype-causing mutations within QTLs.
    • The integration of functional prediction and annotation data enhances the assessment of potential causative genetic variations.
    • Mouse SNP Miner successfully identified known and novel candidate SNPs, highlighting its utility in genetic research.