Unexpected complexity in the haplotypes of commonly used inbred strains of laboratory mice

Yalcin, Binnaz; Fullerton, Janice M.; Miller, Steve; Keays, David A.; Brady, Shannon C.; Bhomra, Amarjit; Jefferson, Ann; Volpi, Emanuela V.; Copley, Richard R.; Flint, Jonathan; Mott, Richard

doi:10.1073/pnas.0401189101

Cited by 101 publications

(118 citation statements)

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“…1b). A key feature of the computational algorithm used to define block boundaries was the inclusion of a parameter C (the cost of making a transition from one block to the next), with low C values favoring transitions (20). Thus, C controls the stringency of haplotype block boundary detection, with robust boundaries detectable even at high C values (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…It is therefore possible to identify sites where crossovers occurred during the derivation of the strains from ancestral Mus musculus subspecies (so-called historical crossovers, Fig. 1) by comparing SNPs in different inbred strains (18,20).In humans, it is estimated that Ͼ80% of crossovers occur at recombination hotspots (5,6,8,21). Therefore, we hypothesized that most of the detectable historical crossovers in the mouse also occurred within hotspots and that many of these hotspots should be currently active in laboratory strains.…”

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Meiotic crossover hotspots contained in haplotype block boundaries of the mouse genome

Kauppi

Jasin

Keeney

2007

Proc. Natl. Acad. Sci. U.S.A.

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Fertility requires successful chromosome segregation in meiosis, which in most sexual organisms depends on the formation of appropriately placed crossovers. The nonrandom genome-wide distributions of meiotic recombination events have been examined at the molecular level experimentally in yeast and by inference from linkage disequilibrium patterns in humans. Thus far, no method has existed for pinpointing sites of crossing-over on a genome-wide scale in an experimentally tractable animal whose genome size and complexity models that of humans. Here, we present a genomic approach to identify mouse crossover hotspots, based on targeting haplotype block boundaries. This represents a previously undescribed method potentially applicable to largescale mouse hotspot identification. Using this method, we have successfully predicted the location of two previously uncharacterized crossover hotspots in male mice. As increasing amounts of single-nucleotide polymorphism data emerge, this approach will be useful for investigating the recombination landscape of the mouse genome.meiosis ͉ mouse strain ͉ recombination ͉ single-nucleotide polymorphism ͉ allele-specified PCR M uch of the human genome consists of discrete chromosomal segments within which single-nucleotide polymorphisms (SNPs) are strongly associated (1-5). These segments, known as haplotype blocks, or linkage disequilibrium (LD) blocks, are 7-to 16-kb long on average, depending on the population (5). Haplotype block structure in humans is largely generated by the presence of preferred sites (''hotspots'') for meiotic crossing-over, flanked by recombinationally inert DNA (6-9). The handful of human recombination hotspots analyzed at high resolution vary in their overall cross-over activity, but share the feature of having crossovers clustered within narrow, 1-to 2-kb regions (6, 7). High-resolution analysis of two hotspots in the mouse, initially identified from pedigree data, revealed similar properties (10, 11).Extrapolating from extensive studies in yeast, it is likely that mammalian crossover hotspots are preferred sites for formation of the DNA double-strand breaks that initiate meiotic recombination (reviewed in refs. 12-14). However, although the existence of hotspots is well documented, the molecular mechanisms that control their activity are poorly understood. Availability of an experimentally tractable mammalian system for characterizing and manipulating hotspots is thus important.Breakdown of LD, that is, disruption of haplotype block structure, reports on recombination that occurred during the history of the population. Thus, LD analysis is the current method for studying genome-wide fine-scale patterns of recombination in humans (reviewed in ref. 13). However, LD analysis is less suited for this purpose in mice, because of two features of the short and unusual population history of inbred laboratory strains. First, it has been argued that there is strong selection pressure to eliminate allelic combinations that cannot be tolerated in homozygous form (15)...

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Section: Resultsmentioning

confidence: 99%

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Meiotic crossover hotspots contained in haplotype block boundaries of the mouse genome

Kauppi

Jasin

Keeney

2007

Proc. Natl. Acad. Sci. U.S.A.

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“…Despite the potential power of LD analysis, the emerging body of studies suggests major challenges to its application in mice (Frazer et al 2004;Yalcin et al 2004a). Complete resequencing has revealed greater detail than the whole genome shotgun reads used initially.…”

Section: Detection and Localizationmentioning

confidence: 99%

“…There is still hope for in silico strategies, though. While the structure of the mouse genome is more complex than originally thought, there are still blocks of sequence with clear phylogenetic relationships among the inbred strains (Frazer et al 2004;Yalcin et al 2004a). This information still allows for association tests that use a small set of SNPs to represent a given region.…”

Section: Detection and Localizationmentioning

confidence: 99%

Genomics of the future: Identification of quantitative trait loci in the mouse

Flaherty

Herron²,

Symula³

2005

Genome Res.

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Positional cloning of quantitative trait loci in rodents is a common approach to identify genes involved in complex phenotypes, including genes important to human disease. However, cloning the causative genes has proved to be more difficult than determining their positions. New tools such as genomic sequence, clone libraries, and new genomic-based methods offer new approaches to identify these genes. Here we review how these new tools and approaches will improve our ability to discover the genes important in complex traits.

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