Incorporating regulatory interactions into gene-set analyses for GWAS data: A controlled analysis with the MAGMA tool
Fig 8
Identifying genes and regulatory elements that carry gains from particular augmentations for selected gene sets.
(A) Ten top-gaining genes from the schizophrenia-associated gene set, post-synaptic chemical transmission (officially, go_chemical_synaptic_transmission_postsynaptic), which gained from augmentation of the baseline model with the pc-HiC of brain dataset of regulatory interactions, were inspected to relate regulatory elements to schizophrenia risk. Left panel: illustrating genes in their genomic context to relate their regulatory elements to their gains (right panel), and in turn, to the gain at the level of the gene set itself. Regulatory elements that overlap with strong SNV-level associations for a phenotype (note, SNV-level associations from the GWAS dataset are depicted on the red midline) are of particular interest (note that the legend specifies the most significant SNV-level association represented by a given shade of red). Regulatory interactions involving interesting regulatory elements may be independently supported by eQTL data (GTEx version 8 for the European population, covering all tissues and cell types) for the same gene (purple dots). Right panel: gene scores (as used in gene-set analysis, before multiple-testing correction) for selected mappings. Bigger, positive scores imply a stronger phenotype association. For random augmentation, gene scores and error bars represent the mean and standard deviation, respectively (based on 20 independent permutations of EPVP). (B) Ten top-gaining genes from the type-2 diabetes associated gene-set, endocrine system development (officially, go_endocrine_system_development), which gained from augmentation with the cMap of Pa-Islet Cells dataset of regulatory interactions, were likewise inspected. Mapping abbreviations: Pa-Islet Cells (pancreatic-islet cells).