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Prioritization and association analysis of murine-derived candidate genes in anxiety-spectrum disorders.


ABSTRACT: Anxiety disorders are common psychiatric conditions that are highly comorbid with each other and related phenotypes such as depression, likely due to a shared genetic basis. Fear-related behaviors in mice have long been investigated as potential models of anxiety disorders, making integration of information from both murine and human genetic data a powerful strategy for identifying potential susceptibility genes for these conditions.We combined genome-wide association analysis of fear-related behaviors with strain distribution pattern analysis in heterogeneous stock mice to identify a preliminary list of 52 novel candidate genes. We ranked these according to three complementary sources of prior anxiety-related genetic data: 1) extant linkage and knockout studies in mice, 2) a meta-analysis of human linkage scans, and 3) a preliminary human genome-wide association study. We genotyped tagging single nucleotide polymorphisms covering the nine top-ranked regions in a two-stage association study of 1316 subjects from the Virginia Adult Twin Study of Psychiatric and Substance Use Disorders chosen for high or low genetic loading for anxiety-spectrum phenotypes (anxiety disorders, neuroticism, and major depression).Multiple single nucleotide polymorphisms in the PPARGC1A gene demonstrated association in both stages that survived gene-wise correction for multiple testing.Integration of genetic data across human and murine studies suggests PPARGC1A as a potential susceptibility gene for anxiety-related disorders.

SUBMITTER: Hettema JM 

PROVIDER: S-EPMC3191234 | biostudies-literature | 2011 Nov

REPOSITORIES: biostudies-literature

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<h4>Background</h4>Anxiety disorders are common psychiatric conditions that are highly comorbid with each other and related phenotypes such as depression, likely due to a shared genetic basis. Fear-related behaviors in mice have long been investigated as potential models of anxiety disorders, making integration of information from both murine and human genetic data a powerful strategy for identifying potential susceptibility genes for these conditions.<h4>Methods</h4>We combined genome-wide asso  ...[more]

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