DescriptionAnxiety and mood disorders have a heterogenous symptom presentation and are main contributors to the burden of disability worldwide. By using a multivariate factor analytical method, genomic SEM, we identified six correlated latent genetic factors (AIC=3591, SRMR=0.053) explaining 59% of the genetic variance underpinning anxiety and mood disorders, including major depressive disorder. We obtained GWAS with each latent genetic factor by regressing individual variant effects on factors in a synthetic GWAS using genomic SEM, including 3,935,493 individuals and more than 7.7M genetic variants per putative factor trait. We performed gene and gene-level association analyses with GTEx, Brainspan, and MSigDB gene sets using MAGMA. Our work revealed how genetic associations with latent constructs may uncover associations that are qualitatively and quantitatively different from associations in univariate GWAS, which in turn may yield more informative secondary GWAS analysis associations. We demonstrated this by identifying specific biological processes potentially involved in neuroticism aetiology.
|Period||26 May 2022|
|Held at||Social Genetic & Developmental Psychiatry|
|Degree of Recognition||Regional|