» Articles » PMID: 36340032

3D Genome Organization Links Non-coding Disease-associated Variants to Genes

Overview
Specialty Cell Biology
Date 2022 Nov 7
PMID 36340032
Authors
Affiliations
Soon will be listed here.
Abstract

Genome sequencing has revealed over 300 million genetic variations in human populations. Over 90% of variants are single nucleotide polymorphisms (SNPs), the remainder include short deletions or insertions, and small numbers of structural variants. Hundreds of thousands of these variants have been associated with specific phenotypic traits and diseases through genome wide association studies which link significant differences in variant frequencies with specific phenotypes among large groups of individuals. Only 5% of disease-associated SNPs are located in gene coding sequences, with the potential to disrupt gene expression or alter of the function of encoded proteins. The remaining 95% of disease-associated SNPs are located in non-coding DNA sequences which make up 98% of the genome. The role of non-coding, disease-associated SNPs, many of which are located at considerable distances from any gene, was at first a mystery until the discovery that gene promoters regularly interact with distal regulatory elements to control gene expression. Disease-associated SNPs are enriched at the millions of gene regulatory elements that are dispersed throughout the non-coding sequences of the genome, suggesting they function as gene regulation variants. Assigning specific regulatory elements to the genes they control is not straightforward since they can be millions of base pairs apart. In this review we describe how understanding 3D genome organization can identify specific interactions between gene promoters and distal regulatory elements and how 3D genomics can link disease-associated SNPs to their target genes. Understanding which gene or genes contribute to a specific disease is the first step in designing rational therapeutic interventions.

Citing Articles

Multi-omics analysis in primary T cells elucidates mechanisms behind disease-associated genetic loci.

Shi C, Zhao D, Butler J, Frantzeskos A, Rossi S, Ding J Genome Biol. 2025; 26(1):26.

PMID: 39930543 PMC: 11808986. DOI: 10.1186/s13059-025-03492-y.


scTrends: A living review of commercial single-cell and spatial 'omic technologies.

De Jonghe J, Opzoomer J, Vilas-Zornoza A, Nilges B, Crane P, Vicari M Cell Genom. 2024; 4(12):100723.

PMID: 39667347 PMC: 11701258. DOI: 10.1016/j.xgen.2024.100723.


Systematic prioritization of functional variants and effector genes underlying colorectal cancer risk.

Law P, Studd J, Smith J, Vijayakrishnan J, Harris B, Mandelia M Nat Genet. 2024; 56(10):2104-2111.

PMID: 39284974 PMC: 11525171. DOI: 10.1038/s41588-024-01900-w.


Loop Catalog: a comprehensive HiChIP database of human and mouse samples.

Reyna J, Fetter K, Ignacio R, Ali Marandi C, Ma A, Rao N bioRxiv. 2024; .

PMID: 38746164 PMC: 11092438. DOI: 10.1101/2024.04.26.591349.


Integrative regulation of hLMR1 by dietary and genetic factors in nonalcoholic fatty liver disease and hyperlipidemia.

Jaso-Vera M, Takaoka S, Patel I, Ruan X Hum Genet. 2024; 143(7):897-906.

PMID: 38493444 DOI: 10.1007/s00439-024-02654-5.


References
1.
Benner C, Spencer C, Havulinna A, Salomaa V, Ripatti S, Pirinen M . FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics. 2016; 32(10):1493-501. PMC: 4866522. DOI: 10.1093/bioinformatics/btw018. View

2.
Morgan S, Mariano N, Bermudez A, Arruda N, Wu F, Luo Y . Manipulation of nuclear architecture through CRISPR-mediated chromosomal looping. Nat Commun. 2017; 8:15993. PMC: 5511349. DOI: 10.1038/ncomms15993. View

3.
Avsec Z, Agarwal V, Visentin D, Ledsam J, Grabska-Barwinska A, Taylor K . Effective gene expression prediction from sequence by integrating long-range interactions. Nat Methods. 2021; 18(10):1196-1203. PMC: 8490152. DOI: 10.1038/s41592-021-01252-x. View

4.
Davies J, Telenius J, McGowan S, Roberts N, Taylor S, Higgs D . Multiplexed analysis of chromosome conformation at vastly improved sensitivity. Nat Methods. 2015; 13(1):74-80. PMC: 4724891. DOI: 10.1038/nmeth.3664. View

5.
Wei X, Xiang Y, Peters D, Marius C, Sun T, Shan R . HiCAR is a robust and sensitive method to analyze open-chromatin-associated genome organization. Mol Cell. 2022; 82(6):1225-1238.e6. PMC: 8934281. DOI: 10.1016/j.molcel.2022.01.023. View