Connecting gene regulatory relationships to neurobiological mechanisms of brain disorders

Abstract

Despite being clinically distinguishable, many neuropsychiatric disorders display a remarked level of genetic correlation and overlapping symptoms. Deciphering neurobiological mechanisms underlying potential shared genetic etiology is challenging because (1) most common risk variants reside in the non-coding region of the genome, and (2) a genome-wide framework is required to compare genome-wide association studies (GWAS) having different power. To address these challenges, we developed a platform, Hi-C coupled MAGMA (H-MAGMA), that converts SNPlevel summary statistics into gene-level association statistics by assigning non-coding SNPs to their cognate genes based on chromatin interactions. We applied H-MAGMA to five psychiatric disorders and four neurodegenerative disorders to interrogate biological pathways, developmental windows, and cell types implicated for each disorder. We found that neuropsychiatric disorder-associated genes coalesce at the level of developmental windows (midgestation) and cell-type specificity (excitatory neurons). On the contrary, neurodegenerative disorder-associated genes show more diverse cell type specific, and increasing expression over time, consistent with the age-associated elevated risk of developing neurodegenerative disorders.Genes associated with Alzheimer's disease were not only highly expressed in microglia, but also subject to microglia and oligodendrocyte-specific dysregulation, highlighting the importance of understanding the cellular context in which risk variants exert their effects. We also obtained a set of pleiotropic genes that are shared across multiple psychiatric disorders and may form the basis for common neurobiological susceptibility. Pleiotropic genes are associated with neural activity and gene regulation, with selective expression in corticothalamic projection neurons.These results show how H-MAGMA adds to existing frameworks to help identify the neurobiological basis of shared and distinct genetic architecture of brain disorders.It is becoming increasingly recognized that long range (>10kb) regulatory interactions are related to 3D chromatin structure, whereby distal enhancers are brought into contact with the gene promoter 6,12 . Hi-C identifies genome-wide chromatin configuration, which provides a framework for assigning non-coding variants to genes. We therefore modified MAGMA approach to create Hi-C coupled MAGMA or H-MAGMA, that leverages Hi-C datasets to assign non-coding SNPs to their cognate genes. We applied this framework to generate gene-level summary statistics for five neuropsychiatric disorders (Attention deficit hyperactivity disorders, ADHD; Autism spectrum disorders, ASD; Schizophrenia, SCZ; Bipolar disorder, BD; Major depressive disorders, MDD) and four neurodegenerative disorders (Amyotrophic lateral sclerosis, ALS, Multiple sclerosis, MS; Alzheimer's disease, AD, and Parkinson's disease, PD, Figure 1). H-MAGMA identified more significantly associated genes than conventional MAGMA by incorporating non-coding SNPs during the conversion ...

Non-additive effects of schizophrenia risk genes reflect convergent downstream function

“…Moreover, recent studies of loss-of-function autism spectrum disorder genes, albeit perturbations unlikely to be inherited in combination, also reported convergence, whether evaluated in vitro in human neural progenitor cells 48 and brain organoids 49 , or in vivo in fetal mouse brains 50 and Xenopus tropicalis 51 . Further exploration of cross-disorder convergence of risk is certainly warranted: the common and rare risk variants for SCZ 1,7-12,52-54 , autism spectrum disorder [55][56][57] and more broadly across the neuropsychiatric disorder spectrum [58][59][60][61][62] are all highly enriched for genes involved in synaptic biology and gene regulation. Altogether, while individual CRISPR-mediated genetic perturbation of SCZ eGenes in iGLUTs reveal their causal impacts on gene expression, neurodevelopment, and neuronal activity 32,63-65 , sub-additive and convergent effects suggests a model to explain non-additive interactions and argue for the prioritization of convergent genes as potential therapeutic targets.…”

eMAGMA: An eQTL-informed method to identify risk genes using genome-wide association study summary statistics

“…Furthermore, the framework can be modified to include any type of annotation that maps SNPs to genes. For example, recent work to integrate chromatin interaction data from relevant tissues using the MAGMA framework increased power to identify putative risk genes and biological pathways for a range of neuropsychiatric traits [18]. With the availability of tissue-specific multi-omic (transcriptome, chromatin, Hi-C, DNA methylation) datasets through projects such as GTEx…”

Interpretation of psychiatric genome-wide association studies with multispecies heterogeneous functional genomic data integration

“…Therefore, the common approach of associating SNPs to nearby downstream and upstream genes can elicit false positives [55] and therefore it is necessary to use data from gene eQTLs, epigenetics, and 3D genomics to assess the relationships among regulatory variants and their distal targets. Although most prior variant-to-gene annotation efforts have relied on positional approaches, i.e., assigning SNPs to genes based solely on physical proximity (e.g., MAGMA software [56]), modern approaches in humans rely on extensively curated functional and regulatory mapping from 'omics data (e.g., S-PrediXcan software [57], TWAS [58], Hi-C coupled MAGMA or H-MAGMA software [59]).…”