A computational tool (H-MAGMA) for improved prediction of brain-disorder risk genes by incorporating brain chromatin interaction profiles

Abstract

Most risk variants for brain disorders identified by genome-wide association studies (GWAS) reside in non-coding genome, which makes deciphering biological mechanisms difficult. A commonly used tool, MAGMA, addresses this issue by aggregating SNP associations to nearest genes. Here, we developed a platform, Hi-C coupled MAGMA ( H-MAGMA ), that advances MAGMA by incorporating chromatin interaction profiles from human brain tissue across two developmental epochs and two brain cell types. By employing gene regulatory relationships in the disease-relevant tissue, H-MAGMA identifies neurobiologically-relevant target genes. 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. Psychiatric disorder risk genes tended to be expressed during mid-gestation and in excitatory neurons, whereas degenerative disorder risk genes showed increasing expression over time and more diverse cell-type specificities. H-MAGMA adds to existing analytic frameworks to help identify the neurobiological consequences of brain disorder genetics.

Neuronal and glial 3D chromatin architecture illustrates cellular etiology of brain disorders

“…In interneurons, GABA-SCZ genes and GABA-BD genes showed similar enrichment for parvalbumin-expressing cells (In6). Given that genetic correlation between SCZ and BD is remarkably high (rg=0.67) 44,53,54 , these findings suggest cellular substrates for molecular convergence (Ex7 and In6 are shared between two disorders) and divergence (Ex1 is BD-specific, while Ex5 is SCZ-specific) among two highly genetically correlated disorders. FDR=1.83e-06).…”

scGRNom: a computational pipeline of integrative multi-omics analyses for predicting cell-type disease genes and regulatory networks

“…Thus, incorporating GWAS data from various brain regions exposes key areas of observed phenotypes. Previous studies in the same field have demonstrated the caution that must be exercised when attempting to correlate GWAS data with clinical phenotypes, and methods such as scGRN-based analysis mitigate these effects [26]. A similar methodology as outlined could be used where common loci within each set of summary statistics are incorporated and established prior to integration into the cell type GRNs, thus linking neuronal spatial information with known mutation sites in disease case patients along with potentially cell type specific functionality.…”

“…Further enrichment analyses including gene ontology and KEGG pathways revealed potential novel cross-disease and disease-specific molecular functions, advancing knowledge on the interplays among genetic, transcriptional and epigenetic risks at the cellular resolution between neurodegenerative and neuropsychiatric diseases. Finally, we summarized our computational analyses as a general-purpose pipeline for predicting gene regulatory networks via multi-omics data.Recent analyses have also revealed that brain disease risk variants are located in non-coding regulatory elements (e.g., enhancers) and that the risk genes likely have cell-type specific effects including neuronal and non-neuronal types [25,26]. In addition, recent single-cell studies 68 TFs,…”

“…Recent analyses have also revealed that brain disease risk variants are located in non-coding regulatory elements (e.g., enhancers) and that the risk genes likely have cell-type specific effects including neuronal and non-neuronal types [25,26]. In addition, recent single-cell studies 68 TFs,…”

MOSTWAS: Multi-Omic Strategies for Transcriptome-Wide Association Studies

“…We also applied MOSTWAS to transcriptomic data on samples of prefrontal cortex, a tissue that has been used previously in studying neuropsychiatric traits and disorders with TWAS [58,59]. There has been ample evidence in brain tissue, especially the prefrontal cortex, that non-coding variants may regulate distal genes [58,60,61]; in fact, an eQTL analysis by Sng et al found that approximately 20-40% of eQTLs in the frontal cortex can be considered trans-acting [62]. Thus, the prefrontal cortex in the context of neuropsychiatric disorders provides a prime example to assess MOSTWAS.…”