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. 2018 Sep 28;46(17):8740-8753.
doi: 10.1093/nar/gky686.

INFERNO: inferring the molecular mechanisms of noncoding genetic variants

Alexandre Amlie-Wolf  1   2 , Mitchell Tang  2 , Elisabeth E Mlynarski  2 , Pavel P Kuksa  2 , Otto Valladares  2 , Zivadin Katanic  2 , Debby Tsuang  3 , Christopher D Brown  1   2   4 , Gerard D Schellenberg  1   2   4 , Li-San Wang  1   2   4
Affiliations

INFERNO: inferring the molecular mechanisms of noncoding genetic variants

Alexandre Amlie-Wolf et al. Nucleic Acids Res. .

Abstract

The majority of variants identified by genome-wide association studies (GWAS) reside in the noncoding genome, affecting regulatory elements including transcriptional enhancers. However, characterizing their effects requires the integration of GWAS results with context-specific regulatory activity and linkage disequilibrium annotations to identify causal variants underlying noncoding association signals and the regulatory elements, tissue contexts, and target genes they affect. We propose INFERNO, a novel method which integrates hundreds of functional genomics datasets spanning enhancer activity, transcription factor binding sites, and expression quantitative trait loci with GWAS summary statistics. INFERNO includes novel statistical methods to quantify empirical enrichments of tissue-specific enhancer overlap and to identify co-regulatory networks of dysregulated long noncoding RNAs (lncRNAs). We applied INFERNO to two large GWAS studies. For schizophrenia (36,989 cases, 113,075 controls), INFERNO identified putatively causal variants affecting brain enhancers for known schizophrenia-related genes. For inflammatory bowel disease (IBD) (12,882 cases, 21,770 controls), INFERNO found enrichments of immune and digestive enhancers and lncRNAs involved in regulation of the adaptive immune response. In summary, INFERNO comprehensively infers the molecular mechanisms of causal noncoding variants, providing a sensitive hypothesis generation method for post-GWAS analysis. The software is available as an open source pipeline and a web server.

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Figures

Figure 1.
Figure 1.
Outline of INFERNO pipeline approach.
Figure 2.
Figure 2.
Characteristics of expanded variant sets for schizophrenia analysis. (A) Number of variants after LD expansion. (B) Genomic partitions of expanded set variants across tag regions. (C) Summary of tissue category FANTOM5 and Roadmap enhancer overlaps across tag regions. (D) Distribution of ΔPWM scores for variants overlapping HOMER TFBSs. (E) Empirical enrichment of variants overlapping enhancers from FANTOM5 and/or Roadmap in specific tissue categories.
Figure 3.
Figure 3.
Results of GTEx co-localization analysis with schizophrenia GWAS. (A) Top results from co-localization analysis integrated with annotation overlaps. Counts in barplots refer to individual variants underlying an eQTL signal in a given tag region, including all variants in the ABF-expanded sets. (B) UCSC Genome Browser view of locus around rs4766428. In ChromHMM tracks, yellow = enhancer, green-yellow = genic enhancer, green = transcription, red = active transcription start site. Track highlighted with red box is dorsolateral prefrontal cortex.
Figure 4.
Figure 4.
Pathway enrichments for tissue-specific lncRNA targets in schizophrenia. Results are split by the tissue category of the lncRNA eQTL signal and pathway annotation. Red arrows denote brain and blood schizophrenia-related pathways discussed in the main text.
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