Understanding the genetics of neuropsychiatric disorders: the potential role of genomic regulatory blocks

Abstract

Recent genome-wide association studies have identified numerous loci associated with neuropsychiatric disorders. The majority of these are in non-coding regions, and are commonly assigned to the nearest gene along the genome. However, this approach neglects the three-dimensional organisation of the genome, and the fact that the genome contains arrays of extremely conserved non-coding elements termed genomic regulatory blocks (GRBs), which can be utilized to detect genes under long-range developmental regulation. Here we review a GRB-based approach to assign loci in non-coding regions to potential target genes, and apply it to reanalyse the results of one of the largest schizophrenia GWAS (SWG PGC, 2014). We further apply this approach to GWAS data from two related neuropsychiatric disorders-autism spectrum disorder and bipolar disorder-to show that it is applicable to developmental disorders in general. We find that disease-associated SNPs are overrepresented in GRBs and that the GRB model is a powerful tool for linking these SNPs to their correct target genes under long-range regulation. Our analysis identifies novel genes not previously implicated in schizophrenia and corroborates a number of predicted targets from the original study. The results are available as an online resource in which the genomic context and the strength of enhancer-promoter associations can be browsed for each schizophrenia-associated SNP.

Fig. 1

Long-range gene regulation. a An enhancer can target a gene over large genomic distances. b A genomic regulatory block (GRB) spanning a 1.9 Mb region of the human genome. This region displays high levels of non-coding conservation between the human and four vertebrate genomes, reflecting evolution over ~435 million years. The non-coding conservation peaks around IRX3, the predicted target gene in this GRB. The black vertical line and blue rectangle mark the genomic position of the obesity-associated SNPs (rs1421085 and rs9939609) from the Ragvin et al. study, and the linkage disequilibrium block around it, respectively. Each SNP falls within an enhancer that is capable of activating the expression of the IRX3 gene, shown in red. c The GRB model: conserved non-coding elements (shown in green) within a GRB contact the target gene’s promoter (shown in red) through looping and regulate its transcriptional activity in multiple contexts. SNPs in the linkage disequilibrium with regulatory elements involved in long-range gene regulation are often erroneously assigned to the nearest bystander gene, associating the wrong gene to the disease phenotype

Fig. 2

Long-range regulation in neurodevelopmental GWAS loci. a LD blocks that do not overlap GRBs are shown in grey. Loci in which the predicted GRB target gene was identified as schizophrenia associated in the original GWAS are shown in light red, and the loci in which the GRB model provides novel target gene predictions are shown in dark red. b The distribution of overlaps with the genomic regulatory blocks of the same number of random regions. P-value calculated based on N = 10,000 randomisations

Fig. 3

A schematic view of four genomic regulatory blocks. Arrows represent genes, with their orientation indicated by the direction of the arrow. The lower plot in each example is a matrix of log expression values (in TPM) for each enhancer–promoter pair in that GRB. The distribution of expression in tissues where the enhancer is inactive or active is shown in grey and pink, respectively, and the number of samples where each enhancer is (in)active is given under the enhancer label. The black line represents the median expression for each distribution. Significant differences between the medians of the two categories are marked with *p < 0.05, **p < 0.01 and ***p < 0.001 (permutation test, see Supplementary methods). Note, the schematics are not to scale and merely represent gene order (for to-scale plots, visit http://scz.genereg.net/)