GENOTYPING STUDIES IN THE MOUSE
Among all the model organisms, the mouse is unsurpassed as a tool for analyzing mammalian biology and human disease ( 59,60 ). The unique advantages of the mouse include a century of genetic studies, hundreds of spontaneous mutations, scores of inbred strains, practical techniques for random mutagenesis, and the generation of transgenic and knockout/knockin mice ( 61–63 ). Most importantly, high-quality genomic sequence information for the mouse is currently available in the public domain ( 64 ). There is extensive rganizational and functional similarity between the mouse and human genomes in addition to large regions of sequence similarity. More than 90% of the mouse genome is partitioned into sections of homology or synteny with the human genome, more than 40% of the human genome aligns to that of the mouse at the nucleotide level, and 80% of the mouse genes have strict orthologs in the human genome ( 64 ). Because the genes, biochemical pathways, and physiological and organ functions in mice are closely related to those of humans, the mouse is a very important and useful experimental system to study the genetics and biology of human disease
( 59 ). Amap of closely spaced genetic polymorphisms is a valuable tool to allow mouse geneticists to fully exploit inherited trait models. Toward this end, we have made a mouse SNP database
(http://mouseSNP.roche.com) available to the public. The mouse SNP database has grown from the originally published 2848 SNPs ( 65 ) to more than 70,000 unique SNPs and a total of nearly 800,000 alleles. For each SNP, allelic information is available for up to 21 commonly used inbred mouse strains. More than 95% of the SNPs in the database have been mapped to a specific chromosome and to a specific base pair on the ENSMBL mouse version 3 scaffold (http://www.ensembl.org/). The mouse SNP database is web accessible and allows real-time queries of
SNP, mouse strain, and allele information. The query results are provided through a graphical user interface. In addition, 740 SNP genotyping assays based on allele-specific kinetic PCR have been developed. The oligonucleotide primer sequences and conditions for performing the genotyping assays are also provided. This SNP database allowed us to perform genome scans in mouse genetic models of osteoporosis, emphysema, viral bronchitis, and breast cancer. Some of our work on the osteoporosis model is outlined here as an example. We undertook a large genotyping study involving eight phenotypic traits in collaboration with Robert Klein et al. of Oregon Health Sciences
University ( 14 ). His group generated a C57BL/6

DBA/2 cross, in which
994 F2 progeny were phenotyped at 16 wk of age for eight skeletal traits. These skeletal traits included bone mineral density, moment of

inertia, cortical thickness, cortical area, marrow area, weight, and femur length. In addition, the femoral cross-sectional area was analyzed separately in males and females. The most extreme F2 progeny with the top and bottom 15% phenotypic values were selected for each trait.
Equal amounts of DNA from each phenotypically extreme sample were collected to construct two pools (high and low) for each trait. For a total of 18 pools, differences in allele frequency were determined between related pairs of pools for 140 SNPs across the whole genome using the allele-specific kinetic PCR genotyping method. The results from this genome scan identified a large number of QTLs related to bone quality
(see Fig.1 for results of scan using bone mineral density). We identified candidate genes in many of these loci, some of which we validated with in vivo and in vitro experiments ( 66 ). Using these methods, one person was able to obtain the equivalent of 280,000 genotypes in 3 mo. This method accelerated SNP analysis, improved traditional QTL detection, and made92 Wang et al.SNP Discovery and Genotyping 93large-scale studies feasible. This method is easily performed using reagents and equipment that are available and affordable to small-scale and academic laboratories. Conventional genotyping can be replaced altogether by an alternative QTL mapping method. This is a computational method for predicting chromosomal regions regulating phenotypic traits ( 14 ) based on the mouse SNP database and phenotypic differences between the commonly used inbred mouse strains. The computational method calculates genotypic distances between a pair of mouse strains based on allelic differences. The genotypic distances are hen compared with phenotypic differences between the two mouse strains. This process is repeated for all strain pairs with phenotypic information available, and a correlation value is derived using linear regression on the phenotypic and genotypic distances for each genome locus. This method has been demonstrated to successfully predict several phenotypic traits (see details in ref. 14 ). Fig. 1. Comparison of
SNP-based genotyping of pooled DNA samples with microsatellite genotyping of individual DNA samples. Phenotypically extreme F2 progeny from a B6D2 intercross with the highest and lowest bone mineral density (BMD) were subjected to whole-genome scanning for association with BMD by genotyping either individual DNA samples
(from 299 mice) with 112 microsatellite markers or two pooled DNA samples (150 mice per pool) with 109 SNP markers. The significance of each allele frequency difference was calculated using the z-test and plotted as a logarithm of the odds (lod) score for all chromosomes.
Dashed line indicates a lod score of 3.3, the threshold for genome-wide significance. (Reprinted with permission from ref. 14 ) To improve the precision of the computational method, a haplotype map has been generated for 13 commonly used Mus musculus strains and is being

expanded to include 21 strains ( 67 ). The computational method has been modified to correlate phenotypic data obtained from inbred mouse strainswith haplotypic blocks. This method evaluates how well the occurrence of haplotype alleles within a block correlates with the phenotypic data. A matching score is assigned to the haplotypic block, which is then adjusted on the size and structure of the block. This process is repeated for all the haplotypic blocks until the best matching blocks are generated. With this new algorithm, our ability to effectively analyze complex traits in mouse genetic models will be greatly improved.