Annotating genetic variants to target genes using H-MAGMA.
Nancy Y A SeyBrandon M PrattHyejung WonPublished in: Nature protocols (2022)
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.
Keyphrases
- genome wide
- copy number
- dna methylation
- rna seq
- single cell
- genome wide association study
- electronic health record
- gene expression
- poor prognosis
- randomized controlled trial
- genome wide identification
- high resolution
- white matter
- transcription factor
- big data
- machine learning
- bone marrow
- oxidative stress
- brain injury
- mesenchymal stem cells
- data analysis
- childhood cancer