Investigating the shared genetic architecture between obesity and depression: a large-scale genomewide cross-trait analysis.

Introduction: Increasing evidence suggests that individuals with obesity are at a higher risk of developing depression, and conversely, depression can contribute to the onset of obesity, creating a detrimental cycle. This study aims to investigate the potential shared biological pathways between obesity and depression by examining genetic correlations, identifying common polymorphisms, and conducting cross-trait genetic analyses.

Methods: We assessed the genetic correlation between obesity and depression using linkage disequilibrium score regression and high-density lipoprotein levels. We combined two different sources of obesity data using METAL and employed bidirectional Mendelian randomization to determine the causal relationship between obesity and depression. Additionally, we conducted multivariate trait analysis using the MTAG method to improve statistical robustness and identify novel genetic associations. Furthermore, we performed a thorough investigation of independent risk loci using GCTA-COJO, PLACO, MAGMA, POPS, and SMR, integrating different QTL information and methods to further identify risk genes and proteins.

Results: Our analysis revealed genetic correlations and bidirectional positive causal relationships between obesity and depression, highlighting shared risk SNP (rs10789340). We identified RPL31P12, NEGR1, and DCC as common risk genes for obesity and depression. Using the BLISS method, we identified SCG3 and FLRT2 as potential drug targets.

Limitation: Most of our data sources are from Europe, which may limit the generalization of our findings to other ethnic populations.

Conclusion: This study demonstrates the genetic causal relationship and common risk SNPs, genes, proteins, and pathways between obesity and depression. These findings contribute to a deeper understanding of their pathogenesis and the identification of potential therapeutic targets.