Mapping the genetic landscape of psychiatric disorders with the MiXeR toolset.

Abstract

Psychiatric disorders have complex genetic architectures with substantial genetic overlap across conditions, which may partly explain their high levels of comorbidity. This presents significant challenges to research; genome-wide association studies (GWAS) have uncovered hundreds of loci associated with single disorders, yet the genetic landscape of psychiatric disorders has remained largely obscured. By moving beyond the conventional infinitesimal model, uni-, bi-, and trivariate MiXeR tools, applied to GWAS summary statistics, have enabled us to more comprehensively describe the genetic architecture of complex disorders and traits, and their overlap. Further, the GSA-MiXeR tool improves biological interpretation of GWAS findings to better understand causal mechanisms. Here, we outline the methodology underlying the MiXeR tools, together with instructions for their optimal use. We review results from studies investigating the genetic architecture of psychiatric disorders and their overlap using the MiXeR toolset. These studies have revealed generally high polygenicity and low discoverability among psychiatric disorders, particularly in contrast to somatic disorders. There is also pervasive genetic overlap across psychiatric disorders and behavioral traits, while their overlap with somatic traits is smaller, in line with differences in polygenicity. Finally, GSA-MiXeR has quantified the contribution of gene-sets to the heritability of psychiatric disorders, prioritizing small, biologically coherent gene-sets. Together, these findings have implications for our understanding of the complex relationships between psychiatric disorders and related traits. MiXeR tools have provided new insights into the genetic architecture of psychiatric disorders, generating a better understanding of their underlying biological mechanisms and potential for clinical utility.