GARFIELD classifies disease-relevant genomic features through integration of functional annotations with association signals

Abstract

Loci discovered by genome-wide association studies (GWAS) predominantly map outside protein-coding genes. The interpretation of the functional consequences of non-coding variants can be greatly enhanced by catalogues of regulatory genomic regions in cell lines and primary tissues. However, robust and readily applicable methods to systematically evaluate the contribution of these regions to genetic variation implicated in diseases or quantitative traits are still lacking. Here we propose a novel approach that leverages GWAS findings with regulatory or functional annotations to classify features relevant to a phenotype of interest. Within our framework, we account for major sources of confounding that current methods do not offer. We further assess enrichment for 29 GWAS traits within ENCODE and Roadmap derived regulatory regions. We characterize unique enrichment patterns for traits and annotations, driving novel biological insights. The method is implemented in standalone software and an R package to facilitate its application by the research community.

Neonatal genetics of gene expression reveal the origins of autoimmune and allergic disease risk

“…Using a two-step conditional analysis (Methods), PHA-stimulated T cells had the largest number of eGenes (6.3%; Table S5) with multiple independent eQTL signals. GARFIELD enrichment analysis 24 showed that the cis-eSNPs were enriched in 3' untranslated regions (UTR), 5' UTR, and exon regions ( Figure S2), consistent with known mechanisms of cis-eQTLs.…”

“…We performed enrichment analyses using GARFIELD (version 2) to investigate the enrichment patterns of cis-eQTLs using predefined features ("annotation data") such as genic annotations from ENCODE, GENCODE, and Roadmap Epigenomics project provided by this tool 24 . GARFIELD evaluates enrichment using generalised linear regression models that account for allele frequency, distance to the nearest gene TSS, and LD.…”

Genomic analysis reveals a functional role for myocardial trabeculae in adults

“…We analysed all genetic variants of these association results for enrichment in regulatory and functional annotations, using GARFIELD. 46 GARFIELD accounts for LD structure and local gene density and derives the statistical significance of the functional enrichment (odds ratios) by fitting a logistic regression model. The strongest associations of the genetic loci were to open-chromatin regions in fetal heart tissue, particularly in the mid and apical regions ( Figure 3, Figure 14 in the Supplement).…”

“…We used GARFIELD version 2 46 for functional enrichment analyses of genetic variants with multi-trait GWAS p-value < 10 −6 . The GARFIELD software package and pre-computed data for samples of Caucasian ancestry (LD and annotation data, minor allele frequencies of genetic variants and their distances to nearest transcription start site) were downloaded from https://www.ebi.ac.uk/birney-srv/GARFIELD.…”

Predicting cell-type-specific non-coding RNA transcription from genome sequence

“…These findings implicate ncRNAs in the mechanisms driving complex traits. GWAS has allowed inference of trait-relevant 10 tissues or cell-types (Finucane et al, 2015(Finucane et al, , 2018, biological pathways (Iotchkova et al, 2019;Lamparter et al, 2016), and therapeutic drugs (Terao et al, 2016); therefore, determining the influence of non-coding causal variants from GWAS on ncRNA expression in relevant cell-types is a promising approach to deepen our understanding of complex trait mechanisms ( Figure 1A). For mRNAs, such analyses have already 15 improved understanding of the genetic architecture of complex traits; mRNA expression quantitative trait loci (eQTL) information, resolved to the tissue-or cell-type-level, has been linked to GWAS results to implicate the cell types and tissues involved in complex traits (Ardlie et al, 2015;Ishigaki et al, 2017).…”