A genome-wide panel of congenic mice reveals widespread epistasis of behavior quantitative trait loci

Gale, Greg D.; Yazdi, R D; Ah, Khan; Lusis, Aldons J.; Davis, Richard C.; Smith, Desmond J.

doi:10.1038/mp.2008.4

Cited by 34 publications

(31 citation statements)

References 53 publications

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“…To identify QTL genes, traditional techniques have relied upon genetic methods such as further mapping through the creation of congenic strains ( 53,54 ). However, with the availability of dense SNPs for most of the common inbred strains ( 37,38 ), fi ne mapping QTL should be easier since many of the polymorphic regions in any cross will be known.…”

Section: Discussionmentioning

confidence: 99%

Four additional mouse crosses improve the lipid QTL landscape and identify Lipg as a QTL gene

Ishimori

Chen

et al. 2009

Journal of Lipid Research

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Section: Discussionmentioning

confidence: 99%

Four additional mouse crosses improve the lipid QTL landscape and identify Lipg as a QTL gene

Ishimori

Chen

et al. 2009

Journal of Lipid Research

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“…Finally, although neither rearing nor grooming served as reliable indicators of learning here, these behaviors should be considered when selecting a scoring method for fear conditioning experiments. A number of automated scoring approaches, including beambreak, motion detectors, and tracking software, have been used to assess fear conditioned freezing (e.g., Bolivar et al 2001;Fitch et al 2002;Gale et al 2009;Lattal and Maughan 2012). Given that each uses slightly different criteria for assessing movement, it is important to consider the ability of any scoring system to distinguish between freezing and other nonambulatory behaviors (such as grooming or rearing).…”

Section: Rearingmentioning

confidence: 99%

“…The C57BL/6 (B6) and DBA/2 (D2) strains, which show significant differences in multiple learning paradigms (Paylor et al 1996;Dellu et al 2000;Schimanski and Nguyen 2004;Restivo et al 2006;Graybeal et al 2014), have been used by several groups to identify genetic and molecular contributions to fear conditioned learning, including long-standing hypotheses regarding the role of the hippocampus and amygdala (Schimanski and Nguyen 2004;Yang et al 2008;Andre et al 2012). However, the behavioral findings for fear conditioning in these strains are highly variable across labs, particularly with regard to differences in CS responses (e.g., AmmassariTeule et al 2001;Balogh et al 2002;Gale et al 2009). The lack of consensus regarding strain differences at the behavioral level makes drawing conclusions about the underlying neural mechanisms difficult.…”

mentioning

confidence: 99%

Delay and trace fear conditioning in C57BL/6 and DBA/2 mice: issues of measurement and performance

et al. 2014

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Strain comparison studies have been critical to the identification of novel genetic and molecular mechanisms in learning and memory. However, even within a single learning paradigm, the behavioral data for the same strain can vary greatly, making it difficult to form meaningful conclusions at both the behavioral and cellular level. In fear conditioning, there is a high level of variability across reports, especially regarding responses to the conditioned stimulus (CS). Here, we compare C57BL/6 and DBA/2 mice using delay fear conditioning, trace fear conditioning, and a nonassociative condition. Our data highlight both the significant strain differences apparent in these fear conditioning paradigms and the significant differences in conditioning type within each strain. We then compare our data to an extensive literature review of delay and trace fear conditioning in these two strains. Finally, we apply a number of commonly used baseline normalization approaches to compare how they alter the reported differences. Our findings highlight three major sources of variability in the fear conditioning literature: CS duration, number of CS presentations, and data normalization to baseline measures.

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“…Thus, like the CSS, GTM represent a considerable reduction in genetic complexity from a cross between two inbred strains, and the relative contribution of each genetic variant is magnified. Smith and colleagues mapped 97 loci for a variety of behavioral traits, including hyperactivity, anxiety, prepulse inhibition, avoidance, and conditioned freezing (Gale et al 2008). There was evidence for epistasis at about 60% of the QTLs they identified.…”

Section: Genetic Architecture: Context-dependent Effectsmentioning

confidence: 99%

Genetic architecture of quantitative traits in mice, flies, and humans

Flint¹,

Mackay²

2009

Genome Res.

407

364

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We compare and contrast the genetic architecture of quantitative phenotypes in two genetically well-characterized model organisms, the laboratory mouse, Mus musculus, and the fruit fly, Drosophila melanogaster, with that found in our own species from recent successes in genome-wide association studies. We show that the current model of large numbers of loci, each of small effect, is true for all species examined, and that discrepancies can be largely explained by differences in the experimental designs used. We argue that the distribution of effect size of common variants is the same for all phenotypes regardless of species, and we discuss the importance of epistasis, pleiotropy, and gene by environment interactions. Despite substantial advances in mapping quantitative trait loci, the identification of the quantitative trait genes and ultimately the sequence variants has proved more difficult, so that our information on the molecular basis of quantitative variation remains limited. Nevertheless, available data indicate that many variants lie outside genes, presumably in regulatory regions of the genome, where they act by altering gene expression. As yet there are very few instances where homologous quantitative trait loci, or quantitative trait genes, have been identified in multiple species, but the availability of high-resolution mapping data will soon make it possible to test the degree of overlap between species.Recent successes in mapping quantitative trait loci (QTLs) that contribute to phenotypic variation in humans and model organisms make it possible to address important questions about the genetic architecture of quantitative phenotypes, such as the likely number of loci, their mode of genetic action, and interaction with each other and with the environment. An understanding of the genetic architecture of quantitative traits is not only important in its own right, it will also inform studies that seek to proceed to the identification of the quantitative trait genes (QTGs) and the quantitative trait nucleotide (QTN) variants, the functional changes in DNA sequence that contribute to trait variation.In this work we use examples from two genetically wellcharacterized model organisms, the laboratory mouse, Mus musculus, and the fruit fly, Drosophila melanogaster, to discuss how an understanding of the genetic architecture of quantitative phenotypes in model organisms informs mapping experiments in human genetics. In our discussion of the latter, we draw upon the recent successful application of genome-wide association studies (GWAS) to both quantitative phenotypes (such as height and weight) and disease phenotypes (assuming that the genetic architecture of common disease is similar, if not identical, to that of quantitative phenotypes).Genetic architecture has been the subject of a number of recent reviews, most of which deal with information from a single model organism (Mackay 2004) or review a small number of phenotypes (Kendler and Greenspan 2006). Here, our purpose is different. We tackle three relate...

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A genome-wide panel of congenic mice reveals widespread epistasis of behavior quantitative trait loci

Cited by 34 publications

References 53 publications

Four additional mouse crosses improve the lipid QTL landscape and identify Lipg as a QTL gene

Four additional mouse crosses improve the lipid QTL landscape and identify Lipg as a QTL gene

Delay and trace fear conditioning in C57BL/6 and DBA/2 mice: issues of measurement and performance

Genetic architecture of quantitative traits in mice, flies, and humans

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