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

Autor: Benxia Hu, Jessica C. McAfee, Won Mah, Schahram Akbarian, Prashanth Rajarajan, Nancy Y A Sey, Harper B. Fauni, Hyejung Won, Kristen J. Brennand
Rok vydání: 2020
Předmět:
Zdroj: Nature Neuroscience. 23:583-593
ISSN: 1546-1726
1097-6256
DOI: 10.1038/s41593-020-0603-0
Popis: 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.
Databáze: OpenAIRE
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