Disentangling effects of colocalizing genomic annotations to functionally prioritize non-coding variants within complex trait loci

Abstract

Identifying genomic annotations that differentiate causal from trait-associated variants is essential to fine mapping disease loci. Although many studies have identified non-coding functional annotations that overlap disease-associated variants, these annotations often colocalize, complicating the ability to use these annotations for fine mapping causal variation. We developed a statistical approach (Genomic Annotation Shifter [GoShifter]) to assess whether enriched annotations are able to prioritize causal variation. GoShifter defines the null distribution of an annotation overlapping an allele by locally shifting annotations; this approach is less sensitive to biases arising from local genomic structure than commonly used enrichment methods that depend on SNP matching. Local shifting also allows GoShifter to identify independent causal effects from colocalizing annotations. Using GoShifter, we confirmed that variants in expression quantitative trail loci drive gene-expression changes though DNase-I hypersensitive sites (DHSs) near transcription start sites and independently through 3 0 UTR regulation. We also showed that (1) 15%-36% of trait-associated loci map to DHSs independently of other annotations; (2) loci associated with breast cancer and rheumatoid arthritis harbor potentially causal variants near the summits of histone marks rather than full peak bodies; (3) variants associated with height are highly enriched in embryonic stem cell DHSs; and (4) we can effectively prioritize causal variation at specific loci.

Which genetic variants in DNase I sensitive regions are functional?

“…S13 and Table S7). Overall, we observed enrichment for biologically relevant factors; for example, factors enriched for height-associated SNPs include the embryonic stem cell factor POU5F1, consistent with previous observations of genetic variants associated with height being enriched in embryonic stem cell DHS sites (Trynka et al 2014). We also see enrichment for the developmental regulators TBX15, FOXD3, and NKX2-5.…”

IMPACT: Genomic Annotation of Cell-State-Specific Regulatory Elements Inferred from the Epigenome of Bound Transcription Factors

“…Genetic studies of rheumatoid arthritis (RA), an autoimmune disease that attacks synovial joint tissue leading to permanent joint damage and disability, 45 have suggested a critical role by CD4 þ T cells. 7,8,11,12,[46][47][48][49][50] However, CD4 þ T cells are extremely heterogeneous: naive CD4 þ T cells may differentiate into memory T cells, and then into effector T cells including Th1, Th2, and Th17 and T regulatory cells, requiring the action of a limited number of key transcription factors (TFs): T-BET or STAT4, GATA3 or STAT6, STAT3 or RORgt, FOXP3 or STAT5, respectively. 51 As these CD4 þ T effector cell states contribute to RA risk, 7,11,49 we hypothesized that CD4 þ T cell-state-specific IMPACT regulatory element annotations would better capture RA h2 than annotations that generalize CD4 þ T cells and ignore the differential functionality of effector cell states.…”