INFERNO: inferring the molecular mechanisms of noncoding genetic variants

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.

Inferring the molecular mechanisms of noncoding Alzheimer’s disease-associated genetic variants

“…INFERNO (details of the algorithm are described in [23]) was used to analyze 19 top variants from Phase 1 of the IGAP study, excluding the locus near DSG2 (tagged by rs8093731) which did not replicate in Phase 2 and the HLA-DRB5 locus (rs9271192) which is difficult to analyze due to the dense LD structure in the major histocompatibility (MHC) region caused by population-specific selective pressure [24]. INFERNO was run using P value expansion within one order of magnitude and 500 kilobases (kb) of each tagging variant, and the European population from the 1,000 Genomes Project [25] was used for LD pruning and expansion.…”

“…For both pruning and expansion, a threshold of r 2 >= 0.7 was used to define LD blocks. All downstream analyses including lncRNA correlation and pathway analysis were performed as defined in [23].…”

“…Using INFERNO's tissue categorization approach [23] that groups each functional genomics dataset into one of 32 high-level tissue categories, we identified 36 variants from nine tag regions (the CASS4, CELF1, EPHA1, FERMT2, MS4A6A, NME8, PICALM, PTK2B, and SLC24A4/RIN3 regions) that overlapped concordant FANTOM5 and Roadmap ChromHMM enhancers in a tissue category (Figure 2a). All of these regions harbored at least one variant with concordant support in the blood category, supporting the hypothesis of immune mechanisms underlying LOAD genetic signals [7][8][9].…”

“…We applied COLOC to tissue-specific eQTL signals for 884 unique genes across all 19 tag regions (median number of genes within each region = 34) for 25,435 tests of GWAS -tissue-specific eQTL co-localization ( Supplementary Figure 4a, Methods). We identified 153 co-localized GWAS/eQTL signals (P(H 4 ) >= 0.5 representing strong support for a shared causal signal [23]) representing 16 tag regions, 37 tissues and 71 target genes ( Supplementary Figure 4b). For 32 of these, COLOC identified individual variants with ABF >= 0.5, but in the majority of cases COLOC was not able to prioritize a single causal variant.…”

“…We hypothesize that noncoding LOAD GWAS signals modulate disease risk by perturbing genomic elements that regulate genes involved in pathogenesis. To explore this hypothesis, we applied our bioinformatics pipeline, INFERNO (INFERring the molecular mechanisms of NOncoding genetic variants) [23] to LOAD GWAS data from the International Genomics of Alzheimer's Project (IGAP) [6]. INFERNO characterizes noncoding GWAS signals by integrating information across diverse functional genomics data sources to identify causal noncoding variants and the regulatory mechanisms, tissue contexts, and target genes they affect (Figure 1a).…”