LAUSR

Genome-wide association studies (GWAS) associate genetic variants with traits. Neuropsychiatric traits have complex etiology, and GWAS have started to reveal their polygenic architecture, including multiple single nucleotide polymorphisms (SNPs) associated with each trait (SNP-trait association— STAs). Yet, GWAS-hits have generally small effect sizes and their biological interpretation has been challenging, since they are often noncoding, pleiotropic, and/or non-causative. The latter effect is due to nonrandom co-inheritance, linkage disequilibrium (LD), with nearby causative loci. These reasons necessitate methods for SNP-prioritization through fine mapping, and causal inference [1]. To this end, transcriptome-wide association studies (TWAS) leverage genetically regulated expression through genotypebased transcriptomic imputation (TI) to identify gene-trait associations (GTAs). TI predicts the expression of a variety of gene transcripts, including gene isoforms, based on typically cis-regulatory variants (Fig. 1a). Disease-association testing is accomplished by using: (i) raw genotype to impute first the transcriptome, and subsequently compute the GTAs; or (ii) GWAS summary statistics to directly impute GTAs from STAs (Fig. 1b). Compared to GWAS, TWAS benefit from less multiple testing burden, confer higher statistical power and deliver biological understanding of neuropsychiatric risk, given their results involve tissue/cell-type and directional specificity. Since neuropsychiatric TWAS relies on the GWAS sample size, it circumvents the need of disease-specific brain samples required to conduct well-powered differential gene expression analyses of disease. Moreover, since TI-models are trained on psychiatric controls, the resulting GTAs probe neuropsychiatric risk without confounding by reverse causality, as it is often true for brain transcriptomic studies [2]. Neuropsychiatric GTAs are often far from known GWAS-hits, and have implicated novel brain-based transcriptional mechanisms (e.g., alternative splicing), which have been validated in human studies and experimental models [3, 4]. For instance, our post-traumatic stress disorder TWAS, predicted SNRNP35 downregulation in prefrontal cortex (PFC), a region implicated in stress regulation. The function of SNRNP35 as a stress/ glucocorticoid responsive U12-splicing regulator was confirmed in cell culture, mouse PFC and blood of war-exposed marines [4]. Experimental validation steps are essential for TWAS, since associations may be confounded [2]. Firstly, TI-modeling accuracy varies, hampered by the specific characteristics of the training dataset (e.g., sample size, demographic, and clinical information) and by high LD between eQTL-regions. Secondly, accuracy of GTAs and their biological specificity can be confounded by LDbased correlated predicted expression, gene co-regulation, and shared eQTLs across tissues/cell-types. Thirdly, ancestry mismatch between the TI-training and the GWAS can lead to false-positive/ negative GTAs. To mitigate these limitations, more accurate TI is necessary, by modeling nonadditive relationships between cisand transregulation of gene expression, by distinguishing commonfrom rare-variant effects, by making TI-models focused on ancestry, sex, and life-stages, and by predicting RNA-species in brain cell types (e.g., [5]). GWAS-SNP imputation with appropriate reference panels would improve LD estimation and, consequently, GTAs. The improved GTAs in conjunction with their tissue-/cell-type-/directionalspecificity can be understood in the context of affected pathways [6], and lead to testable mechanistic hypotheses, drug-target identification, and drug repurposing (Fig. 1c), which are lagging behind in neuropsychiatry. Finally, developing fine mapping, together with polygenic risk scoring for TWAS will advance personalized medicine efforts for these disorders.