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Updated: May 14, 2025

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
Published on: July 27, 2021
Transdiagnostic and Disorder-Level Genome-Wide Association Studies Enhance Precision of Substance Use and Psychiatric
Yousef Khan1, Christal N Davis2, Zeal Jinwala2
1Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania.
Background:
Substance use disorders (SUDs) and psychiatric disorders frequently co-occur, and their etiology likely reflects both transdiagnostic (i.e., common/shared) and disorder-level (i.e., independent/nonshared) genetic influences. Understanding the genetic influences that are shared and those that operate independently of the shared risk could enhance precision in diagnosis, prevention, and treatment, but this remains underexplored, particularly in non-European ancestry groups.
Methods:
We applied genomic structural equation modeling to examine the common and independent genetic architecture among SUDs and psychotic, mood, and anxiety disorders using summary statistics from genome-wide association studies (GWASs) conducted in European ancestry (EUR) and African ancestry (AFR) individuals. To characterize the biological and phenotypic associations, we used FUMA, conducted genetic correlations, and performed phenome-wide association studies (PheWASs).
Results:
In EUR individuals, transdiagnostic genetic factors represented SUDs, psychotic disorders, and mood/anxiety disorders, with a GWAS identifying 2 novel lead single nucleotide polymorphisms (SNPs) for the mood factor. In AFR individuals, genetic factors represented SUDs and psychiatric disorders, and a GWAS identified 1 novel lead SNP for the SUD factor. In EUR individuals, second-order factor models showed phenotypic and genotypic associations with a broad range of physical and mental health traits. Finally, genetic correlations and PheWASs highlighted how common and independent genetic factors for SUDs and psychotic disorders were differentially associated with psychiatric, sociodemographic, and medical phenotypes.
Conclusions:
Combining transdiagnostic and disorder-level genetic approaches can improve our understanding of co-occurring conditions and increase the specificity of genetic discovery, which is critical for identifying more effective prevention and treatment strategies to reduce the burden of these disorders.
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