Cargando…

Making sense of GWAS: using epigenomics and genome engineering to understand the functional relevance of SNPs in non-coding regions of the human genome

Considerable progress towards an understanding of complex diseases has been made in recent years due to the development of high-throughput genotyping technologies. Using microarrays that contain millions of single-nucleotide polymorphisms (SNPs), Genome Wide Association Studies (GWASs) have identifi...

Descripción completa

Detalles Bibliográficos
Autores principales: Tak, Yu Gyoung, Farnham, Peggy J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4696349/
https://www.ncbi.nlm.nih.gov/pubmed/26719772
http://dx.doi.org/10.1186/s13072-015-0050-4
_version_ 1782407783142916096
author Tak, Yu Gyoung
Farnham, Peggy J.
author_facet Tak, Yu Gyoung
Farnham, Peggy J.
author_sort Tak, Yu Gyoung
collection PubMed
description Considerable progress towards an understanding of complex diseases has been made in recent years due to the development of high-throughput genotyping technologies. Using microarrays that contain millions of single-nucleotide polymorphisms (SNPs), Genome Wide Association Studies (GWASs) have identified SNPs that are associated with many complex diseases or traits. For example, as of February 2015, 2111 association studies have identified 15,396 SNPs for various diseases and traits, with the number of identified SNP-disease/trait associations increasing rapidly in recent years. However, it has been difficult for researchers to understand disease risk from GWAS results. This is because most GWAS-identified SNPs are located in non-coding regions of the genome. It is important to consider that the GWAS-identified SNPs serve only as representatives for all SNPs in the same haplotype block, and it is equally likely that other SNPs in high linkage disequilibrium (LD) with the array-identified SNPs are causal for the disease. Because it was hoped that disease-associated coding variants would be identified if the true casual SNPs were known, investigators have expanded their analyses using LD calculation and fine-mapping. However, such analyses also identified risk-associated SNPs located in non-coding regions. Thus, the GWAS field has been left with the conundrum as to how a single-nucleotide change in a non-coding region could confer increased risk for a specific disease. One possible answer to this puzzle is that the variant SNPs cause changes in gene expression levels rather than causing changes in protein function. This review provides a description of (1) advances in genomic and epigenomic approaches that incorporate functional annotation of regulatory elements to prioritize the disease risk-associated SNPs that are located in non-coding regions of the genome for follow-up studies, (2) various computational tools that aid in identifying gene expression changes caused by the non-coding disease-associated SNPs, and (3) experimental approaches to identify target genes of, and study the biological phenotypes conferred by, non-coding disease-associated SNPs.
format Online
Article
Text
id pubmed-4696349
institution National Center for Biotechnology Information
language English
publishDate 2015
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-46963492015-12-31 Making sense of GWAS: using epigenomics and genome engineering to understand the functional relevance of SNPs in non-coding regions of the human genome Tak, Yu Gyoung Farnham, Peggy J. Epigenetics Chromatin Review Considerable progress towards an understanding of complex diseases has been made in recent years due to the development of high-throughput genotyping technologies. Using microarrays that contain millions of single-nucleotide polymorphisms (SNPs), Genome Wide Association Studies (GWASs) have identified SNPs that are associated with many complex diseases or traits. For example, as of February 2015, 2111 association studies have identified 15,396 SNPs for various diseases and traits, with the number of identified SNP-disease/trait associations increasing rapidly in recent years. However, it has been difficult for researchers to understand disease risk from GWAS results. This is because most GWAS-identified SNPs are located in non-coding regions of the genome. It is important to consider that the GWAS-identified SNPs serve only as representatives for all SNPs in the same haplotype block, and it is equally likely that other SNPs in high linkage disequilibrium (LD) with the array-identified SNPs are causal for the disease. Because it was hoped that disease-associated coding variants would be identified if the true casual SNPs were known, investigators have expanded their analyses using LD calculation and fine-mapping. However, such analyses also identified risk-associated SNPs located in non-coding regions. Thus, the GWAS field has been left with the conundrum as to how a single-nucleotide change in a non-coding region could confer increased risk for a specific disease. One possible answer to this puzzle is that the variant SNPs cause changes in gene expression levels rather than causing changes in protein function. This review provides a description of (1) advances in genomic and epigenomic approaches that incorporate functional annotation of regulatory elements to prioritize the disease risk-associated SNPs that are located in non-coding regions of the genome for follow-up studies, (2) various computational tools that aid in identifying gene expression changes caused by the non-coding disease-associated SNPs, and (3) experimental approaches to identify target genes of, and study the biological phenotypes conferred by, non-coding disease-associated SNPs. BioMed Central 2015-12-30 /pmc/articles/PMC4696349/ /pubmed/26719772 http://dx.doi.org/10.1186/s13072-015-0050-4 Text en © Tak and Farnham. 2015 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Review
Tak, Yu Gyoung
Farnham, Peggy J.
Making sense of GWAS: using epigenomics and genome engineering to understand the functional relevance of SNPs in non-coding regions of the human genome
title Making sense of GWAS: using epigenomics and genome engineering to understand the functional relevance of SNPs in non-coding regions of the human genome
title_full Making sense of GWAS: using epigenomics and genome engineering to understand the functional relevance of SNPs in non-coding regions of the human genome
title_fullStr Making sense of GWAS: using epigenomics and genome engineering to understand the functional relevance of SNPs in non-coding regions of the human genome
title_full_unstemmed Making sense of GWAS: using epigenomics and genome engineering to understand the functional relevance of SNPs in non-coding regions of the human genome
title_short Making sense of GWAS: using epigenomics and genome engineering to understand the functional relevance of SNPs in non-coding regions of the human genome
title_sort making sense of gwas: using epigenomics and genome engineering to understand the functional relevance of snps in non-coding regions of the human genome
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4696349/
https://www.ncbi.nlm.nih.gov/pubmed/26719772
http://dx.doi.org/10.1186/s13072-015-0050-4
work_keys_str_mv AT takyugyoung makingsenseofgwasusingepigenomicsandgenomeengineeringtounderstandthefunctionalrelevanceofsnpsinnoncodingregionsofthehumangenome
AT farnhampeggyj makingsenseofgwasusingepigenomicsandgenomeengineeringtounderstandthefunctionalrelevanceofsnpsinnoncodingregionsofthehumangenome