Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types

Abstract

Genetics can provide a systematic approach to discovering the tissues and cell types relevant for a complex disease or trait. Identifying these tissues and cell types is critical for following up on non-coding allelic function, developing ex-vivo models, and identifying therapeutic targets. Here, we analyze gene expression data from several sources, including the GTEx and PsychENCODE consortia, together with genome-wide association study (GWAS) summary statistics for 48 diseases and traits with an average sample size of 169,331, to identify disease-relevant tissues and cell types. We develop and apply an approach that uses stratified LD score regression to test whether disease heritability is enriched in regions surrounding genes with the highest specific expression in a given tissue. We detect tissue-specific enrichments at FDR < 5% for 34 diseases and traits across a broad range of tissues that recapitulate known biology. In our analysis of traits with observed central nervous system enrichment, we detect an enrichment of neurons over other brain cell types for several brain-related traits, enrichment of inhibitory over excitatory neurons for bipolar disorder but excitatory over inhibitory neurons for schizophrenia and body mass index, and enrichments in the cortex for schizophrenia and in the striatum for migraine. In our analysis of traits with observed immunological enrichment, we identify enrichments of T cells for asthma and eczema, B cells for primary biliary cirrhosis, and myeloid cells for Alzheimer's disease, which we validated with

Low-frequency variant functional architectures reveal strength of negative selection across coding and non-coding annotations

“…. Finally, at the trait level, we observed that CTS annotations 7,48 with significant CVE tend to have similar LFVE (or larger LFVE for brain-related annotations and traits; see below),…”

“…reflecting regulatory activity in a given cell type) with excess contributions to common variant architectures. For each of the 40 UK Biobank traits, we selected the subset of 396 CTS Roadmap annotations 7 with statistically significant common variant enrichment after conditioning on (non-CTS annotations in) the baseline-LD model 5,48 (see Online Methods). We selected a total of 637 trait-annotation pairs, with at least one CTS annotation for 36 of 40 traits (25 of 27 independent traits) (Table S9); the 637 CTS annotations contained 2.7% of common variants and 3.0% of low-frequency variants on average (Table S10).…”

“…In particular, H3K4me1 in regulatory T-cells Figure 4b and Table S10). Thus, CTS annotations often dominate low-frequency architectures, analogous to common variant architectures 5,48 .…”

“…For CTS analyses, we analyzed the 396 Roadmap 7 annotations constructed in Finucane et al 48 from narrow peaks in six chromatin marks (DNase hypersensitivity, H3K27ac, H3K4me3, H3K4me1, H3K9ac, and H3K36me3) in a subset of a set of 88 primary cell types/tissues. We selected CTS annotations for which common variants are disease relevant following Finucane et al 48 guidelines. First, we analyzed each CTS annotation in turn using default S-LDSC (i.e.…”

Using three-dimensional regulatory chromatin interactions from adult and fetal cortex to interpret genetic results for psychiatric disorders and cognitive traits

“…We compared the eHi-C readouts to multiple sets of human genetic results selected due to the availability of large GWAS (Table S2). We evaluated the connection between Hi-C readouts and these GWAS using partitioned LD score regression 90,91 . In effect, we estimate the degree to which the SNPheritability of a trait is enriched in a set of genomic features (e.g., FIREs).…”