Neuropsychiatric and substance use disorders (NPSUD) have a complex etiology that includes environmental and polygenic risk factors with significant cross-trait rG. Genome Wide Association Studies (GWAS) of NPSUD yield numerous association signals. However, for most of these regions, we do not yet have a firm understanding of either the specific risk variants or the effects of these variants. Post-GWAS methods allow researchers to use GWAS summary statistics and functional genomics data to infer the likely molecular mediators (transcript, protein and methylation abundances) for the effect of variants on disorders. One group of post-GWAS approaches is commonly referred to as transcriptome/proteome/methylome wide association studies, which are abbreviated as T/P/MWAS (or collectively as XWAS). Since these approaches use biological mediators, the multiple testing burden is reduced to the number of genes (~20,000) instead of millions GWAS SNPs leading to increased signal detection. In this work, our aim is to uncover likely risk genes for NPSUD by performing XWAS analyses in two tissues: blood and brain. Firstly, XWAS using the Summary-data based Mendelian Randomization (SMR), which takes GWAS summary statistics, reference xQTL data and a reference LD panel as inputs, was conducted to identify putative causal risk genes. Second, given the large comorbidities among NPSUD and the shared cis-xQTLs between blood and brain, we improved XWAS signal detection in NPSUD for underpowered analyses by performing joint concordance analyses between XWAS results i) across the two tissues and ii) across NPSUD. All XWAS signals i) were adjusted for HEIDI (non-causality) p-values and ii) used to test for pathway enrichment. The results suggest that there were widely shared gene/protein signals within the Major Histocompatibility (MHC) region on chromosome 6 (BTN3A2 and C4A) and elsewhere in the genome (RERE, FURIN, ZDHHC5 and NEK4). The identification of putative molecular genes and pathways underlying risk may offer new targets for therapeutic development. Some of our analyses' more immediate actionable signals might relate to vitamins, i.e., i) in KYAT3 (a part of the kynurenine pathway with vitamin B6 as a cofactor) for post-traumatic stress disorder and ii) omega-3 and vitamin D pathways for bipolar disorder.

Neuropsychiatric and substance use disorders (NPSUDs) have a complex etiology that includes environmental and polygenic risk factors with significant cross-trait genetic correlations. Genome-wide association studies (GWAS) of NPSUDs yield numerous association signals. However, for most of these regions, we do not yet have a firm understanding of either the specific risk variants or the effects of these variants. Post-GWAS methods allow researchers to use GWAS summary statistics and molecular mediators (transcript, protein, and methylation abundances) infer the effect of these mediators on risk for disorders. One group of post-GWAS approaches is commonly referred to as transcriptome/proteome/methylome-wide association studies, which are abbreviated as T/P/MWAS (or collectively as XWAS). Since these approaches use biological mediators, the multiple testing burden is reduced to the number of genes (∼20,000) instead of millions of GWAS SNPs, which leads to increased signal detection. In this work, our aim is to uncover likely risk genes for NPSUDs by performing XWAS analyses in two tissues—blood and brain. First, to identify putative causal risk genes, we performed an XWAS using the Summary-data-based Mendelian randomization, which uses GWAS summary statistics, reference xQTL data, and a reference LD panel. Second, given the large comorbidities among NPSUDs and the shared cis-xQTLs between blood and the brain, we improved XWAS signal detection for underpowered analyses by performing joint concordance analyses between XWAS results i) across the two tissues and ii) across NPSUDs. All XWAS signals i) were adjusted for heterogeneity in dependent instruments (HEIDI) (non-causality) p-values and ii) used to test for pathway enrichment. The results suggest that there were widely shared gene/protein signals within the major histocompatibility complex region on chromosome 6 (BTN3A2 and C4A) and elsewhere in the genome (FURIN, NEK4, RERE, and ZDHHC5). The identification of putative molecular genes and pathways underlying risk may offer new targets for therapeutic development. Our study revealed an enrichment of XWAS signals in vitamin D and omega-3 gene sets. So, including vitamin D and omega-3 in treatment plans may have a modest but beneficial effect on patients with bipolar disorder.

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.

No abstract