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Genetic overlap between Alzheimer's disease and depression mapped onto the brain

By Jennifer Monereo-Sánchez, Miranda Schram, Oleksandr Frei, Kevin S. O'Connell, Alexey A. Shadrin, Olav B. Smeland, Lars T. Westlye, Ole A. Andreassen, Tobias Kaufmann, David EJ Linden, Dennis van der Meer

Posted 26 Jan 2021
medRxiv DOI: 10.1101/2021.01.23.21250016

Background: Alzheimer's disease (AD) and depression are debilitating brain disorders that are often comorbid. Shared brain mechanisms have been implicated, yet findings are inconsistent, reflecting the complexity of the underlying pathophysiology. As both disorders are (partly) heritable, characterizing their genetic overlap may provide etiological clues. While previous studies have indicated negligible genetic correlations, this study aims to expose the genetic overlap that may remain hidden due to mixed directions of effects. Methods: We applied Gaussian mixture modelling, through MiXeR, and conjunctional false discovery rate (cFDR) analysis, through pleioFDR, to genome-wide association study (GWAS) summary statistics of AD (n=79,145) and depression (n=450,619). The effects of identified overlapping loci on AD and depression were tested in 403,029 participants of the UK Biobank (mean age 57.21 52.0% female), and mapped onto brain morphology in 30,699 individuals with brain MRI data. Results: MiXer estimated 98 causal genetic variants overlapping between the two disorders, with 0.44 concordant directions of effects. Through pleioFDR, we identified a SNP in the TMEM106B gene, which was significantly associated with AD (B=-0.002, p=9.1x10-4) and depression (B=0.007, p=3.2x10-9) in the UK Biobank. This SNP was also associated with several regions of the corpus callosum volume anterior (B>0.024, p<8.6x10-4), third ventricle volume ventricle (B=-0.025, p=5.0x10-6), and inferior temporal gyrus surface area (B=0.017, p=5.3x10-4). Discussion: Our results indicate there is substantial genetic overlap, with mixed directions of effects, between AD and depression. These findings illustrate the value of biostatistical tools that capture such overlap, providing insight into the genetic architectures of these disorders.

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