Transcriptome‐wide association analysis offers novel opportunities for clinical translation of genetic discoveries on mental disorders
Authors: Eske M. Derks, Eric R. Gamazon
Journal: World Psychiatry (2020)

Large-scale genetic studies have identified hundreds of robust statistical associations between genetic variants and risk for mental disorders, including depression1 and schizophrenia2. These findings provide clear evidence for a polygenic architecture of those disorders, i.e., disease risk is influenced by thousands of genetic variants with individually small effects. Due to the polygenic nature of mental disorders, the actual gene mechanisms underlying the pathophysiological pathways remain largely unknown. A key challenge is to elucidate the downstream molecular consequences underlying the genetic associations. Here we illustrate the value of integrating genetics and transcriptomics (i.e., the study of the level and regulation of gene expression in human cells) and how this approach has improved our understanding of the biological mechanisms underlying psychiatric disease risk. We further discuss how this methodology may contribute to developing improved therapeutics for mental disorders. Determining the downstream molecular consequences of genetic risk factors for psychiatric disease is challenging for two reasons. First, the majority of disease-associated genetic risk factors are located in non-coding regions of the genome, suggesting that these genetic variants act through the regulation of gene expression rather than by directly altering the protein product. Second, due to extensive linkage disequilibrium (LD) in the genome, genetic studies alone are unable to distinguish causal variants from correlated non-functional variants within an LD block. Indeed, the genetic variant may only be statistically correlated with disease risk and may therefore not provide useful information on the causal disease mechanisms. Therefore, it is important to extend genetic studies by integrating functionally relevant intermediate measures reflecting molecular disease mechanisms (i.e., gene expression). To this end, Gamazon et al3 have developed a novel method that integrates genetic and transcriptomic information. This approach, referred to as PrediXcan, is the first transcriptome-wide association study (TWAS) methodology and allows researchers to identify genes whose expression is significantly associated with disease risk. It utilizes genotype and gene expression data from a reference panel to determine the regulatory effects of genetic variants and to identify which genes are differentially expressed in patients compared to healthy controls. An example of a widely used reference panel is the GTEx resource4, which links genotype data to gene expression levels in 48 tissues from 714 donors, including 13 brain regions from 216 donors. TWAS methods, such as PrediXcan, have multiple advantages. First, use of genetically-determined gene expression from a psychiatrically healthy reference panel ensures that significant associations are not influenced by potential confounders. For example, disease-associated variables (such as the use of psychotropic drugs) may lead to differences in gene expression that are a consequence rather than a cause of psychiatric disease. Second, TWAS methods do not only identify differentially expressed genes between patients and controls, but also provide information on the direction of effect, by showing whether the expression of a gene is upregulated or downregulated in patients compared to controls. Third, the effect of a genetic variant on gene expression may be tissue-specific. TWAS allows researchers to investigate the tissue-specificity of genetic effects. For example, we have previously shown that the strongest enrichment of genetic effects on gene expression was observed in disease-relevant tissues (e.g., aortic artery for systolic blood pressure and hippocampus for Alzheimer's disease)5. We have recently integrated genetic and transcriptomic data for four mental disorders (schizophrenia, bipolar disorder, major depression and attention-deficit/hyperactivity disorder) to gain pathophysiological insights into the role of the brain, adrenal gland (neuroendocrine factors), and colon (gastrointestinal systems)6. We found novel genetic mechanisms underlying disease risk and identified putative causal genes. Interestingly, our analysis detected 70 genes that were not identified in the original genetic analyses, illustrating improved power of TWAS compared to genome wide association studies (GWAS). Our findings highlighted the importance of analyzing gene expression data collected in multiple tissues (beyond easily accessible whole blood samples), as 76% of the putative causal genes were detected only in difficult-to-acquire tissues such as the brain. Integration of genetic and transcriptomic data will provide excellent opportunities to produce improved therapeutics for mental disorders. Genetic discoveries may accelerate drug repositioning by identifying genes that are targeted by existing pharmaceutical compounds, an approach known as drug repurposing7, 8. The development of a new drug takes on average 13-15 years and costs $2.5-3.5 billion, with only a ~10% chance that a new therapy will be successfully approved by government regulatory agencies. In contrast, drug repurposing allows increased efficiency and lower costs, because candidates have already established safety profiles from Phase I clinical trials, with time to approval estimated at 6.5 years at an average cost of $350 million. Drugs that have been linked to disorders through genetic studies are reported to be twice as likely to be clinically approved compared to drugs with no such links7. TWAS adds value beyond genetics as it provides essential information on whether a drug is predicted to reverse patterns of gene expression in patients and may therefore be able to reverse the disease phenotype. For example, if a disease-associated gene shows downregulated levels of gene expression in patients, we might aim to identify a drug that increases the expression of that gene, while drugs that decrease the expression may not be beneficial or even worsen disease symptoms. So et al9, using PrediXcan, identified a number of repurposing candidates, many of which were relevant to mental disorders. We expect that future studies exploring drugs with different mechanisms of action will reveal drug candidates that are not yet prescribed for the treatment of psychiatric disease (e.g., immune response drugs). In conclusion, large-scale genetic studies provide a wealth of information of direct clinical relevance. Integration with other types of -omics data will be essential to elucidating biological mechanisms, identification of novel drugs, and translation of findings into the clinic.