Effects of polygenic risk for suicide attempt and risky behavior on brain structure in young people with familial risk of bipolar disorder: Influence Statistics

Effects of polygenic risk for suicide attempt and risky behavior on brain structure in young people with familial risk of bipolar disorder

Abstract

. Abstract Aims Bipolar Disorder (BD) is associated with a 20-30 fold increased suicide risk compared to the general population. First-degree relatives of BD patients show inflated rates of psychopathology including suicidal behaviors. As reliable biomarkers of suicide attempts (SA) are lacking, we examined associations between suicide-related polygenic risk scores (PRS) – a quantitative index of genomic risk – and variability in brain structures implicated in SA. Methods Participants (n=206; aged 12-30 years) were unrelated individuals of European ancestry and comprised three groups: 41 BD cases, 96 BD relatives (‘high-risk’), and 69 controls. Genotyping employed PsychArray, followed by imputation. Three PRS were computed using genome-wide association data for SA in BD (SA-in-BD), SA in Major Depressive Disorder (SA-in-MDD) [Mullins et al., 2019], and risky behavior [Karlsson Linnér et al., 2019]. Structural MRI processing employed FreeSurfer v5.3.0. General linear models were constructed using 32 regions-of-interest identified from suicide neuroimaging literature, with false-discovery-rate correction. Results SA-in-MDD and SA-in-BD PRS negatively predicted parahippocampal thickness, with the latter association modified by group membership. SA-in-BD and Risky Behavior PRS inversely predicted rostral and caudal anterior cingulate structure, respectively, with the latter effect driven by the ‘high-risk’ group. SA-in-MDD and SA-in-BD PRS positively predicted cuneus structure, irrespective of group. Conclusions This study demonstrated associations between PRS for suicide-related phenotypes and structural variability in brain regions implicated in SA. Future exploration of extended PRS, in conjunction with a range of biological, phenotypic, environmental and experiential data in high-risk populations, may inform predictive models for suicidal behaviors.