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Identifying causal variants and genes using functional genomics in specialized cell types and contexts.

ABSTRACT: A central goal in human genetics is the identification of variants and genes that influence the risk of polygenic diseases. In the past decade, genome-wide association studies (GWAS) have identified tens of thousands of genetic loci associated with various diseases. Since the majority of such loci lie within non-coding regions and have many candidate variants in linkage disequilibrium, it has been challenging to accurately identify specific causal variants and genes. To aid in their discovery a variety of statistical and experimental approaches have been developed. These approaches often borrow information from functional genomics assays such as ATAC-seq, ChIP-seq and RNA-seq to annotate functional variants and identify regulatory relationships between variants and genes. While such approaches are powerful, given the diversity of cell types and environments, it is paramount to select disease-relevant contexts for follow-up analyses. In this review, we discuss the latest developments, challenges, and best practices for determining the causal mechanisms of polygenic disease risk variants with functional genomics data from specialized cell types.

SUBMITTER: Liu B

PROVIDER: S-EPMC6942616 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Identifying causal variants and genes using functional genomics in specialized cell types and contexts.

Liu Boxiang B Montgomery Stephen B SB

Human genetics 20190717 1

A central goal in human genetics is the identification of variants and genes that influence the risk of polygenic diseases. In the past decade, genome-wide association studies (GWAS) have identified tens of thousands of genetic loci associated with various diseases. Since the majority of such loci lie within non-coding regions and have many candidate variants in linkage disequilibrium, it has been challenging to accurately identify specific causal variants and genes. To aid in their discovery a ...[more]

PMID: 31317254

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Project description:BackgroundThe chromosomal region 6q23 has been found to be associated with multiple sclerosis (MS) predisposition through genome wide association studies (GWAS). There are four independent single nucleotide polymorphisms (SNPs) associated with MS in this region, which spans around 2.5 Mb. Most GWAS variants associated with complex traits, including these four MS associated SNPs, are non-coding and their function is currently unknown. However, GWAS variants have been found to be enriched in enhancers and there is evidence that they may be involved in transcriptional regulation of their distant target genes through long range chromatin looping.AimThe aim of this work is to identify causal disease genes in the 6q23 locus by studying long range chromatin interactions, using the recently developed Capture Hi-C method in human T and B-cell lines. Interactions involving four independent associations unique to MS, tagged by rs11154801, rs17066096, rs7769192 and rs67297943 were analysed using Capture Hi-C Analysis of Genomic Organisation (CHiCAGO).ResultsWe found that the pattern of chromatin looping interactions in the MS 6q23 associated region is complex. Interactions cluster in two regions, the first involving the rs11154801 region and a second containing the rs17066096, rs7769192 and rs67297943 SNPs. Firstly, SNPs located within the AHI1 gene, tagged by rs11154801, are correlated with expression of AHI1 and interact with its promoter. These SNPs also interact with other potential candidate genes such as SGK1 and BCLAF1. Secondly, the rs17066096, rs7769192 and rs67297943 SNPs interact with each other and with immune-related genes such as IL20RA, IL22RA2, IFNGR1 and TNFAIP3. Finally, the above-mentioned regions interact with each other and therefore, may co-regulate these target genes.ConclusionThese results suggest that the four 6q23 variants, independently associated with MS, are involved in the regulation of several genes, including immune genes. These findings could help understand mechanisms of disease and suggest potential novel therapeutic targets.

Dataset Information

Identifying causal variants and genes using functional genomics in specialized cell types and contexts.

Publications

Identifying causal variants and genes using functional genomics in specialized cell types and contexts.

Similar Datasets

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