Illuminating links between cis-regulators and trans-acting variants in the human prefrontal cortex

Abstract

Psychiatric disorders exact immense human and economic tolls in societies globally. Underlying many of these disorders is a complex repertoire of genomic variants that influence the expression of genes involved in pathways and processes in the brain. Identifying such variants and their associated brain functions is thus essential for understanding the molecular underpinnings of psychiatric disorders. Genome-wide association studies (GWASes) have provided many variants associated with these disorders; however, our knowledge of the precise biological mechanisms by which these contribute to disease remains limited. In connection with this, expression quantitative trait loci (eQTLs) have provided useful information linking variants to genes and functions. However, most eQTL studies on human brain have focused exclusively on cis-eQTLs. A complete understanding of disease etiology should also include trans-regulatory mechanisms. Thus, we conduct one of the first genome-wide surveys of trans-eQTLs in the dorsolateral prefrontal cortex (DLPFC) by leveraging the large datasets from the PsychENCODE consortium. We identified ~80,000 trans-eQTLs. We found that a significant number of these overlap with cis-eQTLs, thereby implicating cis-mediators as key players in trans-acting regulation. We show, furthermore, that trans-regulatory mechanisms provide novel insights into psychiatric disease. Particularly, colocalization analysis between trans-eQTLs and schizophrenia (SCZ) GWAS loci identified 90 novel SCZ risk genes and 23 GWAS loci previously uncharacterized by cis-eQTLs. Moreover, these 90 genes tend to be more central in transcriptome-wide co-expression networks and more susceptible to rare variants than SCZ-risk genes associated by cis-variation.

Rare schizophrenia risk variant burden is conserved in diverse human populations

“…In previous work, SRRM2 has been shown to play a role in the tauopathy of Alzheimer's disease [36][37][38] , and de novo mutations in this gene have been linked to developmental disorders 39 . AKAP11 was suggested as a trans-gene linking to a SCZ GWAS signal in a recent study 40 , which considered together with our results, adds to examples of convergence of common and rare variant associations on the same gene. A recent metaanalysis of SCHEMA and a bipolar disorder (BD) dataset also found exome-wide significance for AKAP11 41 , suggesting a role of this gene in the shared etiology of SCZ and BD.…”

Systematic investigation of allelic regulatory activity of schizophrenia-associated common variants

“…Colocalization analysis has been implemented to evaluate whether GWAS and eQTLs are explained by a shared set of variants. To test how many IDE variants are also identified from the colocalization analysis, we compared IDE variants with colocalization between schizophrenia GWAS and adult brain eQTLs (Liu et al, 2021) using the coloc package ( Methods ). Because colocalization does not always indicate a specific variant, we tested how often eGenes (genes detected as having an associated eQTL) linked to IDE variants were also observed from colocalization analysis.…”

“…Intersection of MPRA and eQTLs also pruned the gene list ( Figure 3B ). We initially detected 288 eGenes to be associated with schizophrenia by colocalization analysis, covering 78 loci (Liu et al, 2021). An orthogonal analysis of coordinate-level overlap between eQTLs and MPRA identified 269 eGenes, covering 80 loci.…”

“…Colocalization analysis between adult DLPFC eQTLs and schizophrenia GWAS was obtained from Liu et al (Liu et al, 2021). Same analytic pipeline was used to perform colocalization analysis between developing brain eQTLs and schizophrenia GWAS.…”

Adjusting for genetic confounders in transcriptome-wide association studies leads to reliable detection of causal genes

“…Several examples were discussed in a perspective article on TWAS [6]. Colocalization analysis has a similar problem ; for example, in a Schizophrenia study, 282 genes in 78 risk loci, showed evidence of colocalization [16]. Secondly, at a biochemical level, co-regulation of genes by the same regulatory elements is common [17].…”