Integrative Post-Genome-Wide Association Study Analyses Relevant to Psychiatric Disorders: Imputing Transcriptome and Proteome Signals

Abstract

Background The genome-wide association study (GWAS) is a common tool to identify genetic variants associated with complex traits, including psychiatric disorders (PDs). However, post-GWAS analyses are needed to extend the statistical inference to biologically relevant entities, e.g., genes, proteins and pathways. To achieve this goal, researchers developed methods that incorporate biologically relevant intermediate molecular phenotypes, such as gene expression and protein abundance, which are posited to mediate the variant-trait association. Transcriptome-wide association study (TWAS) and proteome-wide association study (PWAS) are commonly used methods to test the association between these molecular mediators and the trait. Summary In this review, we discuss the most recent developments in TWAS and PWAS. These methods integrate existing 'omic' information with the GWAS summary statistics for trait(s) of interest. Specifically, they impute transcript/protein data and test the association between imputed gene expression/protein level with phenotype of interest by using i) GWAS summary statistics and ii) reference transcriptomic/proteomic/genomic data sets. TWAS and PWAS are suitable as analysis tools for i) primary association scan and ii) fine-mapping to identify potentially causal genes for PDs. Key Messages As post-GWAS analyses, TWAS and PWAS have the potential to highlight causal genes in PDs. These prioritized genes could indicate targets for the development of novel drug therapies. For researchers attempting such analyses, we recommend Mendelian randomization (MR) tools that use GWAS statistics for both trait and reference data sets, e.g., SMR. We base our recommendation on i) being able to use the same tool for both TWAS and PWAS, ii) not requiring the pre-computed weights (and thus easier to update for larger reference data sets) and iii) most larger transcriptome reference data sets are publicly available and easy to transform into a compatible format for SMR analysis.