Polygenic risk and white matter integrity in individuals at high risk of mood disorder.
Polygenic risk and white matter integrity in individuals at high risk of mood disorder.
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DOI:
10.1016/j.biopsych.2013.01.027
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发表时间:
2013-08-15
影响因子:
10.6
通讯作者:
McIntosh, Andrew M.
中科院分区:
文献类型:
--
作者:
Whalley, Heather C.;Sprooten, Emma;Hackett, Suzanna;Hall, Lynsey;Blackwood, Douglas H.;Glahn, David C.;Bastin, Mark;Hall, Jeremy;Lawrie, Stephen M.;Sussmann, Jessika E.;McIntosh, Andrew M.
关键词:
Bipolar disorder (BD) and major depressive disorder (MDD) are highly heritable and genetically overlapping conditions characterised by episodic elevation and/or depression of mood. Both demonstrate abnormalities in white matter integrity, measured using diffusion tensor magnetic resonance imaging (MRI), that are also heritable. However it is unclear how these abnormalities relate to the underlying genetic architecture of each disorder. Genome-wide association studies (GWAS) have demonstrated a significant polygenic contribution to BD and MDD, where risk is attributed to the summation of many alleles of small effect. Determining the effects of an overall polygenic risk profile score on neuroimaging abnormalities may help to identify proxy measures of genetic susceptibility and thereby inform models of risk prediction. In the current study we determined the extent to which common genetic variation underlying risk to mood disorders (BD and MDD) was related to fractional anisotropy, an index of white matter integrity. This was conducted in unaffected individuals at familial risk of mood disorder (n=70) and comparison subjects (n=62). Polygenic risk scores were calculated separately for BD and MDD based on GWAS data from the Psychiatric Genome Consortia. We report that a higher polygenic risk allele load for MDD was significantly associated with decreased white matter integrity across both groups in a large cluster with a peak in the right-sided superior longitudinal fasciculus. These findings suggest that the polygenic approach to examining brain imaging data may be a useful means of identifying traits linked to the genetic risk of mood disorders.
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