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Most risk variants for brain disorders identified by genome-wide association studies reside in the noncoding genome, which makes deciphering biological mechanisms difficult. A commonly used tool, multimarker analysis of genomic annotation (MAGMA), addresses this issue by aggregating single nucleotide polymorphism 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 analyzing gene regulatory relationships in the disease-relevant tissue, H-MAGMA identified 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 neurodegenerative-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 principles of brain disorders.Sey et al. report a computational tool, H-MAGMA, that extracts neurobiological insights from brain-disorder GWAS by linking risk variants to their cognate genes using chromatin interaction profiles from human brain tissue.

An outstanding goal in modern genomics is to systematically predict the functional outcome of noncoding variation associated with complex traits. To address this, we developed Hi-C-coupled multi-marker analysis of genomic annotation (H-MAGMA), which builds on traditional MAGMA—a gene-based analysis tool that assigns variants to their target genes—by incorporating 3D chromatin configuration in assigning variants to their putative target genes. Applying this approach, we identified key biological pathways implicated in a wide range of brain disorders and showed its utility in complementing other functional genomic resources such as expression quantitative trait loci–based variant annotation. Here, we provide a detailed protocol for generating the H-MAGMA variant-gene annotation file by using chromatin interaction data from the adult human brain. In addition, we provide an example of how H-MAGMA is run by using genome-wide association study summary statistics of Parkinson’s disease. Lastly, we generated variant-gene annotation files for 28 tissues and cell types, with the hope of contributing a resource for researchers studying a broad set of complex genetic disorders. H-MAGMA can be performed in <2 h for any cell type in which Hi-C data are available. Hi-C-coupled multi-marker analysis of genomic annotation extends the widely used multi-marker analysis of genomic annotation tool for assigning genetic variants to their target genes by incorporating chromatin conformation data that allow discovery of genes associated with noncoding variants.

Cigarette smoking and alcohol use are among the most prevalent substances used worldwide and account for a substantial proportion of preventable morbidity and mortality, underscoring the public health significance of understanding their etiology. Genome-wide association studies (GWAS) have successfully identified genetic variants associated with cigarette smoking and alcohol use traits. However, the vast majority of risk variants reside in non-coding regions of the genome, and their target genes and neurobiological mechanisms are unknown. Chromosomal conformation mappings can address this knowledge gap by charting the interaction profiles of risk-associated regulatory variants with target genes. To investigate the functional impact of common variants associated with cigarette smoking and alcohol use traits, we applied Hi-C coupled MAGMA (H-MAGMA) built upon cortical and newly generated midbrain dopaminergic neuronal Hi-C datasets to GWAS summary statistics of nicotine dependence, cigarettes per day, problematic alcohol use, and drinks per week. The identified risk genes mapped to key pathways associated with cigarette smoking and alcohol use traits, including drug metabolic processes and neuronal apoptosis. Risk genes were highly expressed in cortical glutamatergic, midbrain dopaminergic, GABAergic, and serotonergic neurons, suggesting them as relevant cell types in understanding the mechanisms by which genetic risk factors influence cigarette smoking and alcohol use. Lastly, we identified pleiotropic genes between cigarette smoking and alcohol use traits under the assumption that they may reveal substance-agnostic, shared neurobiological mechanisms of addiction. The number of pleiotropic genes was ~26-fold higher in dopaminergic neurons than in cortical neurons, emphasizing the critical role of ascending dopaminergic pathways in mediating general addiction phenotypes. Collectively, brain region- and neuronal subtype-specific 3D genome architecture helps refine neurobiological hypotheses for smoking, alcohol, and general addiction phenotypes by linking genetic risk factors to their target genes.

Cigarette smoking is the leading cause of preventable morbidity and mortality. Genetic variation contributes to initiation, regular smoking, nicotine dependence, and cessation. We present a Fagerström Test for Nicotine Dependence (FTND)-based genome-wide association study in 58,000 European or African ancestry smokers. We observe five genome-wide significant loci, including previously unreported loci MAGI2/GNAI1 (rs2714700) and TENM2 (rs1862416), and extend loci reported for other smoking traits to nicotine dependence. Using the heaviness of smoking index from UK Biobank ( N  = 33,791), rs2714700 is consistently associated; rs1862416 is not associated, likely reflecting nicotine dependence features not captured by the heaviness of smoking index. Both variants influence nearby gene expression (rs2714700/ MAGI2-AS3 in hippocampus; rs1862416/ TENM2 in lung), and expression of genes spanning nicotine dependence-associated variants is enriched in cerebellum. Nicotine dependence (SNP-based heritability = 8.6%) is genetically correlated with 18 other smoking traits ( r g  = 0.40–1.09) and co-morbidities. Our results highlight nicotine dependence-specific loci, emphasizing the FTND as a composite phenotype that expands genetic knowledge of smoking. There is strong genetic evidence for cigarette smoking behaviors, yet little is known on nicotine dependence (ND). Here, the authors perform a genome-wide association study on ND in 58,000 smokers, identifying five genome-wide significant loci.

Abstract Hi-C coupled multimarker analysis of genomic annotation (H-MAGMA) was initially developed to advance MAGMA by assigning non-coding SNPs to their cognate genes based on threedimensional chromatin architecture. Yurko and colleagues raised concerns that the SNP-wise mean gene-analysis model of MAGMA may allow inflation in type I errors. Accordingly, we updated MAGMA and found that the updated version (MAGMA v.1.08) effectively controls for error rate inflation. Intrigued by this result, H-MAGMA was also updated by implementing MAGMA v.1.08. As expected, H-MAGMA v.1.08 detected a smaller set of risk genes than its original version (v.1.07), but the overall statistical architecture remained largely unchanged between v.1.07 and v.1.08. H-MAGMA v.1.08 was then applied to genome-wide association studies (GWAS) of five psychiatric disorders, from which we recapitulated our previous findings that psychiatric disorder risk genes display neuronal and prenatal enrichment. Therefore, issues raised by Yurko and colleagues can be overcome by using (H-)MAGMA v.1.08. Competing Interest Statement The authors have declared no competing interest.

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 SNP- level 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 (mid- gestation) 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. Footnotes * https://github.com/thewonlab/H-MAGMA