Intronic elements associated with insomnia and restless legs syndrome exhibit cell type-specific epigenetic features contributing to MEIS1 regulation

A highly evolutionarily conserved MEIS1 intronic region is strongly associated with restless legs syndrome (RLS) and insomnia. To understand its regulatory function, we dissected the region by analyzing chromatin accessibility, enhancer-promoter contacts, DNA methylation, and eQTLs in different human neural cell types and tissues. We observed specific activity with respect to cell type and developmental maturation, indicating a prominent role for distinct highly conserved intronic elements in forebrain inhibitory neuron differentiation. Two elements were hypomethylated in neural cells with higher MEIS1 expression, suggesting a role of enhancer demethylation in gene regulation. MEIS1 eQTLs showed a striking modular chromosomal distribution, with forebrain eQTLs clustering in intron 8/9. CRISPR interference targeting of individual elements in this region attenuated MEIS1 expression, revealing a complex regulatory interplay of distinct elements. In summary, we found that MEIS1 regulation is organized in a modular pattern. Disease-associated intronic regulatory elements control MEIS1 expression with cell type and maturation stage specificity, particularly in the inhibitory neuron lineage. The precise spatiotemporal activity of these elements likely contributes to the pathogenesis of insomnia and RLS.

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3DFAACTS-SNP: Using regulatory T cell-specific epigenomics data to uncover candidate mechanisms of Type-1 Diabetes (T1D) risk

10.1101/2020.09.04.279554 ◽

2020 ◽

Cited By ~ 1

Author(s):

Ning Liu ◽

Timothy Sadlon ◽

Ying Ying Wong ◽

Stephen Pederson ◽

James Breen ◽

...

Keyword(s):

Type 1 Diabetes ◽

Immune Tolerance ◽

Functional Annotation ◽

Target Genes ◽

Regulatory Elements ◽

Regulatory Function ◽

Cell Type ◽

Candidate Target ◽

Cell Type Specific

AbstractBackgroundGenome-wide association and fine-mapping studies have enabled the discovery of single nucleotide polymorphisms (SNPs) and other variants that are significantly associated with many autoimmune diseases including type 1 diabetes (T1D). However, many of the SNPs lie in non-coding regions, limiting the identification of mechanisms that contribute to autoimmune disease progression.MethodsAutoimmunity results from a failure of immune tolerance, suggesting that regulatory T cells (Treg) are likely a significant point of impact for this genetic risk, as Treg are critical for immune tolerance. Focusing on T1D as a model of defective function of Treg in autoimmunity, we designed a SNPs filtering workflow called 3 Dimensional Functional Annotation of Accessible Cell Type Specific SNPs (3DFAACTS-SNP) that utilises overlapping profiles of Treg-specific epigenomic data (ATAC-seq, Hi-C and FOXP3-ChIP) to identify regulatory elements potentially driving the effect of variants associated with T1D, and the gene(s) that they control.ResultsUsing 3DFAACTS-SNP we identified 36 SNPs with plausible Treg-specific mechanisms of action contributing to T1D from 1,228 T1D fine-mapped variants, identifying 119 novel interacting regions resulting in the identification of 51 candidate target genes. We further demonstrated the utility of the workflow by applying it to three other fine-mapped/meta-analysed SNP autoimmune datasets, identifying 17 Treg-centric candidate variants and 35 interacting genes. Finally, we demonstrate the broad utility of 3DFAACTS-SNP for functional annotation of any genetic variation using all common (>10% allele frequency) variants from the Genome Aggregation Database (gnomAD). We identified 7,900 candidate variants and 3,245 candidate target genes, generating a list of potential sites for future T1D or autoimmune research.ConclusionsWe demonstrate that it is possible to further prioritise variants that contribute to T1D based on regulatory function and illustrate the power of using cell type specific multi-omics datasets to determine disease mechanisms. The 3DFAACTS-SNP workflow can be customised to any cell type for which the individual datasets for functional annotation have been generated, giving broad applicability and utility.

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