BackgroundMood disorders, including major depression (MD) and bipolar disorder (BD), are risk factors for Alzheimer’s disease (AD) and possibly share an overlapping genetic architecture. However, few studies have investigated the shared loci and potential pleiotropy among these disorders.MethodsWe carried out a systematic analytical pipeline using GWAS data and three complementary (genome-wide, single variant, and gene-level) statistical approaches to investigate the genetic overlap among MD, BD, and AD.ResultsGWAS summary statistics data from 679,973 individuals were analyzed herein (59,851 MD cases and 113,154 controls; 20,352 BD cases and 31,358 controls; and 71,880 AD cases and 383,378 controls). We identified a significant positive genetic correlation between MD and AD (rG = 0.162; s.e. = 0.064; p = 0.012), and between BD and AD (rG = 0.162; s.e. = 0.068; p = 0.018). We also identified two pleiotropic candidate genes for MD and AD (TMEM106B and THSD7A) and three forBD and AD (MTSS2, VAC14, and FAF1), and reported candidate biological pathways associated with all three disorders.DiscussionOur study identified genetic loci and mechanisms shared by mood disorders and AD. These findings could be relevant to better understand the higher risk for AD among individuals with mood disorders, and to propose new interventions.

Our 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=7.957x10-4) 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.

Brain-behavior relationships that could provide insight into risk-associated pathophysiology have not been thoroughly assessed in anorexia nervosa (AN). Therefore, we sought to identify grey and white matter signatures of AN symptoms and risk factors (trait anxiety, set-shifting impairment) in a sample enriched for AN vulnerability, including acute and remitted AN patients and their unaffected sisters (n = 72, aged 18 -48 years). MRI/DTI data were acquired on a 3T scanner and processed with Freesurfer and FSL TBSS. Relationships between clinical variables of interest and regional subcortical volume, vertexwise cortical surface architecture (thickness, surface area, local gyrification), and voxel-wise white matter microstructure (FA, MD) were tested with separate linear regressions, including age, BMI, lifetime AN diagnosis, and intracranial volume as covariates, where appropriate. Significance was determined using a Bonferroni-corrected threshold, p(t) ≤ 0.001. We detected distinct associations linking AN symptoms to lateral occipital cortical thickness and insular/cingulate gyrification and trait anxiety to lingual cortical thickness and superior parietal gyrification, and we detected overlapping associations linking AN symptoms and set-shifting impairment to frontoparietal gyrification. No other brain-behavior relationships emerged. Our findings suggest that variations in site-specific cortical morphology could give rise to core features of AN and shared temperamental and cognitive-behavioral risk factors for AN.