Academic journal article Genetics

Prospects for Association Mapping in Classical Inbred Mouse Strains

Academic journal article Genetics

Prospects for Association Mapping in Classical Inbred Mouse Strains

Article excerpt


The collection of classical inbred mouse strains displays heritable variation in a large number of complex traits. Many generations of historical recombination have contributed to the panel of classical strain genomes, raising the possibility that quantitative trait loci could be located with high resolution by correlating strain genotypes and phenotypes. Although this association mapping framework has been successful in several empirical applications, its expected performance remains unclear. We used computer simulations based on a publicly available, dense single-nucleotide polymorphism (SNP) map to measure the power and false-positive rate of association mapping on a genomic scale across 30 commonly used classical inbred strains. Expected power is (i) often low for phenotypic effect sizes that are realistic for complex traits, (ii) highly variable across the genome, and (iii) correlated with linkage disequilibrium, aspects of the allele frequency distribution, and haplotype characteristics, as predicted by theory. Simulations also demonstrate clear potential for spurious associations to be generated by unequal relatedness among the strains. These findings suggest that association mapping in the classical strains is best applied in combination with other procedures, such as QTL mapping.

(ProQuest-CSA LLC: ... denotes formulae omitted.)

CLASSICAL inbred mouse strains provide powerful model systems for dissecting the genetic basis of complex phenotypes. The collection of widely available strains displays dramatic genetic variation in many quantitative traits, and the association of phenotypes with molecular markers in controlled crosses can reveal chromosomal regions that contain the causal loci. This strategy, quantitative trait locus (QTL) mapping, provides essential information about the genetic basis of complex phenotypes, including locus positions, effect sizes, and modes of action. However, standard QTL designs involve only one generation of recombination, so that phenotypic variation is typically associated with large genomic regions. This low level of mapping resolution has left the genes underlying most mouse QTL unidentified (FLINT et al. 2005). Populations of lines formed by additional generations of recombination, including recombinant inbred lines, advanced intercross lines, and heterogeneous stocks, allow finer mapping resolution (MOTT et al. 2000; WILLIAMS et al. 2001; CHURCHILL et al. 2004; YALCIN et al. 2005; VALDAR et al. 2006), but narrowing the resulting genomic intervals to small numbers of contributing genes still constitutes a formidable challenge.

The recent ability to genotype strains at markers from across the genome and the low resolution of most crossing studies has led some investigators to pursue an alternative approach to mapping complex trait variation. In this method (originally referred to as ''in silico mapping''), genotypes and phenotypes from groups of classical inbred strains are compared to identify genomic regions that correlate with phenotypic variation (GRUPE et al. 2001; PLETCHER et al. 2004). Because the collective genomes of classical strains have experienced many generations of recombination during their histories, loci can be located with higher precision than in typical crossing designs. Additionally, in contrast to F2 or backcross experiments, where each mouse has a unique genome, large numbers of animals with the same genotype can be measured, increasing the precision and accuracy of phenotypic estimates. Finally, the classical strains need be genotyped only once, accelerating the identification of genotype-phenotype associations.

Researchers have successfully applied this approach to fine map loci underlying complex trait variation in the classical strains. Some studies have used association mapping to narrow genomic intervals previously determined to contribute to phenotypic variation (through crosses), including metastasis (PARK et al. …

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