TU34. ASSOCIATION BETWEEN DEPRESSION DIAGNOSIS, POLYGENIC RISK FOR DEPRESSION AND ANTIHYPERTENSIVE MEDICATION NON-PERSISTENCE

Abstract Background MRI-derived cortical folding measures are an indicator of largely genetically driven early developmental processes. However, the effects of genetic risk for major mental disorders on early brain development are not well understood. Methods We extracted cortical complexity values from structural MRI data of 580 healthy participants using the CAT12 toolbox. Polygenic risk scores (PRS) for schizophrenia, bipolar disorder, major depression, and cross-disorder (incorporating cumulative genetic risk for depression, schizophrenia, bipolar disorder, autism spectrum disorder, and attention-deficit hyperactivity disorder) were computed and used in separate general linear models with cortical complexity as the regressand. In brain regions that showed a significant association between polygenic risk for mental disorders and cortical complexity, volume of interest (VOI)/region of interest (ROI) analyses were conducted to investigate additional changes in their volume and cortical thickness. Results The PRS for depression was associated with cortical complexity in the right orbitofrontal cortex (right hemisphere: p = 0.006). A subsequent VOI/ROI analysis showed no association between polygenic risk for depression and either grey matter volume or cortical thickness. We found no associations between cortical complexity and polygenic risk for either schizophrenia, bipolar disorder or psychiatric cross-disorder when correcting for multiple testing. Conclusions Changes in cortical complexity associated with polygenic risk for depression might facilitate well-established volume changes in orbitofrontal cortices in depression. Despite the absence of psychopathology, changed cortical complexity that parallels polygenic risk for depression might also change reward systems, which are also structurally affected in patients with depressive syndrome.

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O58. Childhood Urbanization Affects Prefrontal Cortical Responses to Trait Anxiety and Interacts With Polygenic Risk for Depression

Biological Psychiatry ◽

10.1016/j.biopsych.2019.03.323 ◽

2019 ◽

Vol 85 (10) ◽

pp. S129

Author(s):

Xiao Zhang ◽

Hao Yan ◽

Hao Yu ◽

Xin Zhao ◽

Shefali Shah ◽

...

Keyword(s):

Trait Anxiety ◽

Polygenic Risk ◽

Prefrontal Cortical ◽

Cortical Responses ◽

Risk For Depression

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Does Childhood Trauma Moderate Polygenic Risk for Depression? A Meta-analysis of 5765 Subjects From the Psychiatric Genomics Consortium

Biological Psychiatry ◽

10.1016/j.biopsych.2017.09.009 ◽

2018 ◽

Vol 84 (2) ◽

pp. 138-147 ◽

Cited By ~ 31

Author(s):

Wouter J. Peyrot ◽

Sandra Van der Auwera ◽

Yuri Milaneschi ◽

Conor V. Dolan ◽

Pamela A.F. Madden ◽

...

Keyword(s):

Childhood Trauma ◽

Meta Analysis ◽

Polygenic Risk ◽

Risk For Depression

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Polygenic risk for depression and the neural correlates of working memory in healthy subjects

Progress in Neuro-Psychopharmacology and Biological Psychiatry ◽

10.1016/j.pnpbp.2017.06.010 ◽

2017 ◽

Vol 79 ◽

pp. 67-76 ◽

Cited By ~ 9

Author(s):

Dilara Yüksel ◽

Bruno Dietsche ◽

Andreas J. Forstner ◽

Stephanie H. Witt ◽

Robert Maier ◽

...

Keyword(s):

Working Memory ◽

Healthy Subjects ◽

Neural Correlates ◽

Polygenic Risk ◽

Risk For Depression

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Faculty Opinions recommendation of Polygenic risk for depression increases risk of ischemic stroke: from the stroke genetics network study.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.732656134.793563017 ◽

2019 ◽

Author(s):

John Nurnberger

Keyword(s):

Ischemic Stroke ◽

Polygenic Risk ◽

Risk For Depression

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Novel polygenic risk score links depression-related cortical transcriptomic changes to brain morphology and symptom severity

10.1101/2021.02.28.21251786 ◽

2021 ◽

Author(s):

Amy E Miles ◽

Fernanda C Dos Santos ◽

Enda M Byrne ◽

Miguel E Renteria ◽

Andrew M McIntosh ◽

...

Keyword(s):

Gene Expression ◽

Risk Score ◽

Symptom Severity ◽

Self Report ◽

Brain Morphology ◽

Polygenic Risk Score ◽

Depression Diagnosis ◽

Polygenic Risk ◽

Depression Risk ◽

Common Genetic Variants

ABSTRACTOur group developed a transcriptome-based polygenic risk score (T-PRS) that uses common genetic variants to capture ‘depression-like’ shifts in cortical gene expression. Here, we mapped T-PRS onto diagnosis and symptom severity in major depressive disorder (MDD) cases and controls from the Psychiatric Genomics Consortium (PGC). To evaluate potential mechanisms, we further mapped T-PRS onto discrete measures of brain morphology and broad depression risk in healthy young adults. Genetic, self-report, and/or neuroimaging data were available in 29,340 PGC participants (59% women; 12,923 MDD cases, 16,417 controls) and 482 participants in the Duke Neurogenetics Study (DNS: 53% women; aged 19.8±1.2 years). T-PRS was computed from SNP data using PrediXcan to impute cortical expression levels of MDD-related genes from a previous post-mortem transcriptome meta-analysis. Sex-specific regressions were used to test effects of T-PRS on depression diagnosis, symptom severity, and Freesurfer-derived subcortical volume, cortical thickness, surface area, and local gyrification index in the PGC and DNS samples, respectively. T-PRS did not predict depression diagnosis (OR=1.007, 95%CI=[0.997-1.018]); however, it correlated with symptom severity in men (rho=0.175, p=7.957×10−4) in one large PGC cohort (N=762, 48% men). In DNS, T-PRS was associated with smaller amygdala volume in women (β=-0.186, t=-3.478, p=.001) and less prefrontal gyrification (max≤-2.970, p≤.006) in both sexes. In men, prefrontal gyrification mediated an indirect effect of T-PRS on broad depression risk (b=.005, p=.029), indexed using self-reported family history of depression. Depression-like shifts in cortical gene expression predict symptom severity in men and may contribute to disease vulnerability through their effect on cortical gyrification.

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Air pollution interacts with genetic risk to influence cortical networks implicated in depression

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2109310118 ◽

2021 ◽

Vol 118 (46) ◽

pp. e2109310118

Author(s):

Zhi Li ◽

Hao Yan ◽

Xiao Zhang ◽

Shefali Shah ◽

Guang Yang ◽

...

Keyword(s):

Air Pollution ◽

Genetic Risk ◽

Information Transfer ◽

Depressive Disorders ◽

Effective Connectivity ◽

Brain Network ◽

Brain Atlas ◽

Polygenic Risk ◽

Increased Risk ◽

Risk For Depression

Air pollution is a reversible cause of significant global mortality and morbidity. Epidemiological evidence suggests associations between air pollution exposure and impaired cognition and increased risk for major depressive disorders. However, the neural bases of these associations have been unclear. Here, in healthy human subjects exposed to relatively high air pollution and controlling for socioeconomic, genomic, and other confounders, we examine across multiple levels of brain network function the extent to which particulate matter (PM2.5) exposure influences putative genetic risk mechanisms associated with depression. Increased ambient PM2.5 exposure was associated with poorer reasoning and problem solving and higher-trait anxiety/depression. Working memory and stress-related information transfer (effective connectivity) across cortical and subcortical brain networks were influenced by PM2.5 exposure to differing extents depending on the polygenic risk for depression in gene-by-environment interactions. Effective connectivity patterns from individuals with higher polygenic risk for depression and higher exposures with PM2.5, but not from those with lower genetic risk or lower exposures, correlated spatially with the coexpression of depression-associated genes across corresponding brain regions in the Allen Brain Atlas. These converging data suggest that PM2.5 exposure affects brain network functions implicated in the genetic mechanisms of depression.

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