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Fine Mapping in 94 Inbred Mouse Strains Using a High-Density Haplotype Resource

Cited 82 time in Web of Science Cited 84 time in Scopus
Authors

Kirby, Andrew; Kang, Hyun Min; Wade, Claire M.; Cotsapas, Chris; Kostem, Emrah; Han, Buhm; Furlotte, Nick; Kang, Eun Yong; Rivas, Manuel; Bogue, Molly A.; Frazer, Kelly A.; Johnson, Frank M.; Beilharz, Erica J.; Cox, David R.; Eskin, Eleazar; Daly, Mark J.

Issue Date
2010-07
Publisher
Genetics Society of America
Citation
Genetics, Vol.185 No.3, pp.1081-1095
Abstract
The genetics of phenotypic variation in inbred mice has for nearly a century provided a primary weapon in the medical research arsenal. A catalog of the genetic variation among inbred mouse strains, however, is required to enable powerful positional cloning and association techniques. A recent whole-genome resequencing study of 15 inbred mouse strains captured a significant fraction of the genetic variation among a limited number of strains, yet the common use of hundreds of inbred strains in medical research motivates the need for a high-density variation map of a larger set of strains. Here we report a dense set of genotypes from 94 inbred mouse strains containing 10.77 million genotypes over 121,433 single nucleotide polymorphisms (SNPs), dispersed at 20-kb intervals on average across the genome, with an average concordance of 99.94% with previous SNP sets. Through pairwise comparisons of the strains, we identified an average of 4.70 distinct segments over 73 classical inbred strains in each region of the genome, suggesting limited genetic diversity between the strains. Combining these data with genotypes of 7570 gap-filling SNPs, we further imputed the untyped or missing genotypes of 94 strains over 8.27 million Perlegen SNPs. The imputation accuracy among classical inbred strains is estimated at 99.7% for the genotypes imputed with high confidence. We demonstrated the utility of these data in high-resolution linkage mapping through power simulations and statistical power analysis and provide guidelines for developing such studies. We also provide a resource of in silico association mapping between the complex traits deposited in the Mouse Phenome Database with our genotypes. We expect that these resources will facilitate effective designs of both human and mouse studies for dissecting the genetic basis of complex traits.
ISSN
0016-6731
URI
https://hdl.handle.net/10371/191654
DOI
https://doi.org/10.1534/genetics.110.115014
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  • College of Medicine
  • Department of Medicine
Research Area Bioinformatics, Computational Biology, Genomics, Human Leukocyte Antigen, Statistical Genetics

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