Cargando…
Using DIVAN to assess disease/trait-associated single nucleotide variants in genome-wide scale
OBJECTIVE: The majority of sequence variants identified by Genome-wide association studies (GWASs) fall outside of the protein-coding regions. Unlike coding variants, it is challenging to connect these noncoding variants to the pathophysiology of complex diseases/traits due to the lack of functional...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5663107/ https://www.ncbi.nlm.nih.gov/pubmed/29084591 http://dx.doi.org/10.1186/s13104-017-2851-y |
_version_ | 1783274767051128832 |
---|---|
author | Chen, Li Qin, Zhaohui S. |
author_facet | Chen, Li Qin, Zhaohui S. |
author_sort | Chen, Li |
collection | PubMed |
description | OBJECTIVE: The majority of sequence variants identified by Genome-wide association studies (GWASs) fall outside of the protein-coding regions. Unlike coding variants, it is challenging to connect these noncoding variants to the pathophysiology of complex diseases/traits due to the lack of functional annotations in the non-coding regions. To overcome this, by leveraging the rich collection of genomic and epigenomic profiles, we have developed DIVAN, or Disease/trait-specific Variant ANnotation, which enables the assignment of a measurement (D-score) for each base of the human genome in a disease/trait-specific manner. To facilitate the utilization of DIVAN, we pre-computed D-scores for every base of the human genome (hg19) for 45 different diseases/traits. RESULTS: In this work, we present a detailed protocol on how to utilize DIVAN software toolkit to retrieve D-scores either by variant identifiers or by genomic regions for a disease/trait of interest. We also demonstrate the utilities of the D-scores using real data examples. We believe that the pre-computed D-scores for 45 diseases/traits is a useful resource to follow up on the discoveries made by GWASs, and the DIVAN software toolkit provides a convenient way to access this resource. DIVAN is freely available at https://sites.google.com/site/emorydivan/software. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13104-017-2851-y) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5663107 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-56631072017-11-01 Using DIVAN to assess disease/trait-associated single nucleotide variants in genome-wide scale Chen, Li Qin, Zhaohui S. BMC Res Notes Research Note OBJECTIVE: The majority of sequence variants identified by Genome-wide association studies (GWASs) fall outside of the protein-coding regions. Unlike coding variants, it is challenging to connect these noncoding variants to the pathophysiology of complex diseases/traits due to the lack of functional annotations in the non-coding regions. To overcome this, by leveraging the rich collection of genomic and epigenomic profiles, we have developed DIVAN, or Disease/trait-specific Variant ANnotation, which enables the assignment of a measurement (D-score) for each base of the human genome in a disease/trait-specific manner. To facilitate the utilization of DIVAN, we pre-computed D-scores for every base of the human genome (hg19) for 45 different diseases/traits. RESULTS: In this work, we present a detailed protocol on how to utilize DIVAN software toolkit to retrieve D-scores either by variant identifiers or by genomic regions for a disease/trait of interest. We also demonstrate the utilities of the D-scores using real data examples. We believe that the pre-computed D-scores for 45 diseases/traits is a useful resource to follow up on the discoveries made by GWASs, and the DIVAN software toolkit provides a convenient way to access this resource. DIVAN is freely available at https://sites.google.com/site/emorydivan/software. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13104-017-2851-y) contains supplementary material, which is available to authorized users. BioMed Central 2017-10-30 /pmc/articles/PMC5663107/ /pubmed/29084591 http://dx.doi.org/10.1186/s13104-017-2851-y Text en © The Author(s) 2017 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 | Research Note Chen, Li Qin, Zhaohui S. Using DIVAN to assess disease/trait-associated single nucleotide variants in genome-wide scale |
title | Using DIVAN to assess disease/trait-associated single nucleotide variants in genome-wide scale |
title_full | Using DIVAN to assess disease/trait-associated single nucleotide variants in genome-wide scale |
title_fullStr | Using DIVAN to assess disease/trait-associated single nucleotide variants in genome-wide scale |
title_full_unstemmed | Using DIVAN to assess disease/trait-associated single nucleotide variants in genome-wide scale |
title_short | Using DIVAN to assess disease/trait-associated single nucleotide variants in genome-wide scale |
title_sort | using divan to assess disease/trait-associated single nucleotide variants in genome-wide scale |
topic | Research Note |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5663107/ https://www.ncbi.nlm.nih.gov/pubmed/29084591 http://dx.doi.org/10.1186/s13104-017-2851-y |
work_keys_str_mv | AT chenli usingdivantoassessdiseasetraitassociatedsinglenucleotidevariantsingenomewidescale AT qinzhaohuis usingdivantoassessdiseasetraitassociatedsinglenucleotidevariantsingenomewidescale |