Use of a Dense Single Nucleotide Polymorphism Map for In Silico Mapping in the Mouse

Mathew T. Pletcher(Genomics Institute of the Novartis Research Foundation), Philip McClurg(Genomics Institute of the Novartis Research Foundation), Serge Batalov(Genomics Institute of the Novartis Research Foundation), Andrew I. Su(Genomics Institute of the Novartis Research Foundation), S. Whitney Barnes(Genomics Institute of the Novartis Research Foundation), Erica A. Lagler(Genomics Institute of the Novartis Research Foundation), Ron Korstanje(Jackson Laboratory), Xiao‐Song Wang(Jackson Laboratory), Deborah Nusskern, Molly A. Bogue(Jackson Laboratory), Richard Mural, Beverly Paigen(Jackson Laboratory), Tim Wiltshire(Genomics Institute of the Novartis Research Foundation)
PLoS Biology
November 5, 2004
Cited by 226Open Access
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Abstract

Rapid expansion of available data, both phenotypic and genotypic, for multiple strains of mice has enabled the development of new methods to interrogate the mouse genome for functional genetic perturbations. In silico mapping provides an expedient way to associate the natural diversity of phenotypic traits with ancestrally inherited polymorphisms for the purpose of dissecting genetic traits. In mouse, the current single nucleotide polymorphism (SNP) data have lacked the density across the genome and coverage of enough strains to properly achieve this goal. To remedy this, 470,407 allele calls were produced for 10,990 evenly spaced SNP loci across 48 inbred mouse strains. Use of the SNP set with statistical models that considered unique patterns within blocks of three SNPs as an inferred haplotype could successfully map known single gene traits and a cloned quantitative trait gene. Application of this method to high-density lipoprotein and gallstone phenotypes reproduced previously characterized quantitative trait loci (QTL). The inferred haplotype data also facilitates the refinement of QTL regions such that candidate genes can be more easily identified and characterized as shown for adenylate cyclase 7.


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