Cargando…

INFIMA leverages multi-omics model organism data to identify effector genes of human GWAS variants

Genome-wide association studies reveal many non-coding variants associated with complex traits. However, model organism studies largely remain as an untapped resource for unveiling the effector genes of non-coding variants. We develop INFIMA, Integrative Fine-Mapping, to pinpoint causal SNPs for div...

Descripción completa

Detalles Bibliográficos
Autores principales: Dong, Chenyang, Simonett, Shane P., Shin, Sunyoung, Stapleton, Donnie S., Schueler, Kathryn L., Churchill, Gary A., Lu, Leina, Liu, Xiaoxiao, Jin, Fulai, Li, Yan, Attie, Alan D., Keller, Mark P., Keleş, Sündüz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8381555/
https://www.ncbi.nlm.nih.gov/pubmed/34425882
http://dx.doi.org/10.1186/s13059-021-02450-8
_version_ 1783741394022563840
author Dong, Chenyang
Simonett, Shane P.
Shin, Sunyoung
Stapleton, Donnie S.
Schueler, Kathryn L.
Churchill, Gary A.
Lu, Leina
Liu, Xiaoxiao
Jin, Fulai
Li, Yan
Attie, Alan D.
Keller, Mark P.
Keleş, Sündüz
author_facet Dong, Chenyang
Simonett, Shane P.
Shin, Sunyoung
Stapleton, Donnie S.
Schueler, Kathryn L.
Churchill, Gary A.
Lu, Leina
Liu, Xiaoxiao
Jin, Fulai
Li, Yan
Attie, Alan D.
Keller, Mark P.
Keleş, Sündüz
author_sort Dong, Chenyang
collection PubMed
description Genome-wide association studies reveal many non-coding variants associated with complex traits. However, model organism studies largely remain as an untapped resource for unveiling the effector genes of non-coding variants. We develop INFIMA, Integrative Fine-Mapping, to pinpoint causal SNPs for diversity outbred (DO) mice eQTL by integrating founder mice multi-omics data including ATAC-seq, RNA-seq, footprinting, and in silico mutation analysis. We demonstrate INFIMA’s superior performance compared to alternatives with human and mouse chromatin conformation capture datasets. We apply INFIMA to identify novel effector genes for GWAS variants associated with diabetes. The results of the application are available at http://www.statlab.wisc.edu/shiny/INFIMA/. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s13059-021-02450-8).
format Online
Article
Text
id pubmed-8381555
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-83815552021-08-23 INFIMA leverages multi-omics model organism data to identify effector genes of human GWAS variants Dong, Chenyang Simonett, Shane P. Shin, Sunyoung Stapleton, Donnie S. Schueler, Kathryn L. Churchill, Gary A. Lu, Leina Liu, Xiaoxiao Jin, Fulai Li, Yan Attie, Alan D. Keller, Mark P. Keleş, Sündüz Genome Biol Method Genome-wide association studies reveal many non-coding variants associated with complex traits. However, model organism studies largely remain as an untapped resource for unveiling the effector genes of non-coding variants. We develop INFIMA, Integrative Fine-Mapping, to pinpoint causal SNPs for diversity outbred (DO) mice eQTL by integrating founder mice multi-omics data including ATAC-seq, RNA-seq, footprinting, and in silico mutation analysis. We demonstrate INFIMA’s superior performance compared to alternatives with human and mouse chromatin conformation capture datasets. We apply INFIMA to identify novel effector genes for GWAS variants associated with diabetes. The results of the application are available at http://www.statlab.wisc.edu/shiny/INFIMA/. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s13059-021-02450-8). BioMed Central 2021-08-23 /pmc/articles/PMC8381555/ /pubmed/34425882 http://dx.doi.org/10.1186/s13059-021-02450-8 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Method
Dong, Chenyang
Simonett, Shane P.
Shin, Sunyoung
Stapleton, Donnie S.
Schueler, Kathryn L.
Churchill, Gary A.
Lu, Leina
Liu, Xiaoxiao
Jin, Fulai
Li, Yan
Attie, Alan D.
Keller, Mark P.
Keleş, Sündüz
INFIMA leverages multi-omics model organism data to identify effector genes of human GWAS variants
title INFIMA leverages multi-omics model organism data to identify effector genes of human GWAS variants
title_full INFIMA leverages multi-omics model organism data to identify effector genes of human GWAS variants
title_fullStr INFIMA leverages multi-omics model organism data to identify effector genes of human GWAS variants
title_full_unstemmed INFIMA leverages multi-omics model organism data to identify effector genes of human GWAS variants
title_short INFIMA leverages multi-omics model organism data to identify effector genes of human GWAS variants
title_sort infima leverages multi-omics model organism data to identify effector genes of human gwas variants
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8381555/
https://www.ncbi.nlm.nih.gov/pubmed/34425882
http://dx.doi.org/10.1186/s13059-021-02450-8
work_keys_str_mv AT dongchenyang infimaleveragesmultiomicsmodelorganismdatatoidentifyeffectorgenesofhumangwasvariants
AT simonettshanep infimaleveragesmultiomicsmodelorganismdatatoidentifyeffectorgenesofhumangwasvariants
AT shinsunyoung infimaleveragesmultiomicsmodelorganismdatatoidentifyeffectorgenesofhumangwasvariants
AT stapletondonnies infimaleveragesmultiomicsmodelorganismdatatoidentifyeffectorgenesofhumangwasvariants
AT schuelerkathrynl infimaleveragesmultiomicsmodelorganismdatatoidentifyeffectorgenesofhumangwasvariants
AT churchillgarya infimaleveragesmultiomicsmodelorganismdatatoidentifyeffectorgenesofhumangwasvariants
AT luleina infimaleveragesmultiomicsmodelorganismdatatoidentifyeffectorgenesofhumangwasvariants
AT liuxiaoxiao infimaleveragesmultiomicsmodelorganismdatatoidentifyeffectorgenesofhumangwasvariants
AT jinfulai infimaleveragesmultiomicsmodelorganismdatatoidentifyeffectorgenesofhumangwasvariants
AT liyan infimaleveragesmultiomicsmodelorganismdatatoidentifyeffectorgenesofhumangwasvariants
AT attiealand infimaleveragesmultiomicsmodelorganismdatatoidentifyeffectorgenesofhumangwasvariants
AT kellermarkp infimaleveragesmultiomicsmodelorganismdatatoidentifyeffectorgenesofhumangwasvariants
AT kelessunduz infimaleveragesmultiomicsmodelorganismdatatoidentifyeffectorgenesofhumangwasvariants