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Published on: November 29, 2012
An imputed genotype resource for the laboratory mouse.
Jin P Szatkiewicz1, Glen L Beane, Yueming Ding
1The Jackson Laboratory, 600 Main Street, Bar Harbor, Maine 04609, USA.
Summary
Researchers developed a high-density single nucleotide polymorphism (SNP) resource for 49 mouse strains by imputing missing data. This reliable SNP dataset aids in studying natural variation and complex traits in laboratory mice.
Area of Science:
- Genomics
- Bioinformatics
- Animal Models
Background:
- Laboratory mice are crucial models for genetic research.
- High-density genotype data is essential for understanding complex traits.
- Existing SNP resources have limitations in coverage and imputation.
Purpose of the Study:
- To create a comprehensive, high-density single nucleotide polymorphism (SNP) resource for 49 inbred mouse strains.
- To improve genotype data accuracy and availability for genetic studies.
- To facilitate research on natural variation and complex traits in mice.
Main Methods:
- Combined genotype data from public databases.
- Employed a hidden Markov model for imputing missing single nucleotide polymorphism (SNP) genotypes.
- Validated imputed genotypes using eight independent SNP resources.
Main Results:
- Generated a resource of 7.87 million polymorphic single nucleotide polymorphism (SNP) loci.
- Demonstrated high reliability of imputed genotypes across most inbred strains.
- Confirmed strong linkage disequilibrium supports accurate imputation.
Conclusions:
- The high-density SNP resource is a valuable tool for mouse genetics research.
- Facilitates association study designs by providing dense SNP genotypes.
- The resource is publicly available and expected to expand with new data.
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