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.
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 (rg = 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.
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.
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 ...
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 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 refines neurobiological hypotheses for smoking, alcohol, and general addiction phenotypes by linking genetic risk factors to their target genes.
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