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A genetic interaction network model of a complex neurological disease.


ABSTRACT: Absence epilepsy (AE) is a complex, heritable disease characterized by a brief disruption of normal behavior and accompanying spike-wave discharges (SWD) on the electroencephalogram. Only a handful of genes has been definitively associated with AE in humans and rodent models. Most studies suggest that genetic interactions play a large role in the etiology and severity of AE, but mapping and understanding their architecture remains a challenge, requiring new computational approaches. Here we use combined analysis of pleiotropy and epistasis (CAPE) to detect and interpret genetic interactions in a meta-population derived from three C3H × B6J strain crosses, each of which is fixed for a different SWD-causing mutation. Although each mutation causes SWD through a different molecular mechanism, the phenotypes caused by each mutation are exacerbated on the C3H genetic background compared with B6J, suggesting common modifiers. By combining information across two phenotypic measures - SWD duration and frequency - CAPE showed a large, directed genetic network consisting of suppressive and enhancing interactions between loci on 10 chromosomes. These results illustrate the power of CAPE in identifying novel modifier loci and interactions in a complex neurological disease, toward a more comprehensive view of its underlying genetic architecture.

SUBMITTER: Tyler AL 

PROVIDER: S-EPMC4241132 | biostudies-literature | 2014 Nov

REPOSITORIES: biostudies-literature

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A genetic interaction network model of a complex neurological disease.

Tyler A L AL   McGarr T C TC   Beyer B J BJ   Frankel W N WN   Carter G W GW  

Genes, brain, and behavior 20141027 8


Absence epilepsy (AE) is a complex, heritable disease characterized by a brief disruption of normal behavior and accompanying spike-wave discharges (SWD) on the electroencephalogram. Only a handful of genes has been definitively associated with AE in humans and rodent models. Most studies suggest that genetic interactions play a large role in the etiology and severity of AE, but mapping and understanding their architecture remains a challenge, requiring new computational approaches. Here we use  ...[more]

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