Data integration workflow for search of disease driving genes and genetic variants.

Comprehensive characterization of a gene's impact on phenotypes requires knowledge of the context of the gene. To address this issue we introduce a systematic data integration method Candidate Genes and SNPs (CANGES) that links SNP and linkage disequilibrium data to pathway- and protein-protein...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Sirkku Karinen, Tuomas Heikkinen, Heli Nevanlinna, Sampsa Hautaniemi
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e5bdbf09ac664c2eb2a02c516c9a3014
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:e5bdbf09ac664c2eb2a02c516c9a3014
record_format	dspace
spelling	oai:doaj.org-article:e5bdbf09ac664c2eb2a02c516c9a30142021-11-18T06:55:53ZData integration workflow for search of disease driving genes and genetic variants.1932-620310.1371/journal.pone.0018636https://doaj.org/article/e5bdbf09ac664c2eb2a02c516c9a30142011-04-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21533266/?tool=EBIhttps://doaj.org/toc/1932-6203Comprehensive characterization of a gene's impact on phenotypes requires knowledge of the context of the gene. To address this issue we introduce a systematic data integration method Candidate Genes and SNPs (CANGES) that links SNP and linkage disequilibrium data to pathway- and protein-protein interaction information. It can be used as a knowledge discovery tool for the search of disease associated causative variants from genome-wide studies as well as to generate new hypotheses on synergistically functioning genes. We demonstrate the utility of CANGES by integrating pathway and protein-protein interaction data to identify putative functional variants for (i) the p53 gene and (ii) three glioblastoma multiforme (GBM) associated risk genes. For the GBM case, we further integrate the CANGES results with clinical and genome-wide data for 209 GBM patients and identify genes having effects on GBM patient survival. Our results show that selecting a focused set of genes can result in information beyond the traditional genome-wide association approaches. Taken together, holistic approach to identify possible interacting genes and SNPs with CANGES provides a means to rapidly identify networks for any set of genes and generate novel hypotheses. CANGES is available in http://csbi.ltdk.helsinki.fi/CANGES/Sirkku KarinenTuomas HeikkinenHeli NevanlinnaSampsa HautaniemiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 4, p e18636 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sirkku Karinen Tuomas Heikkinen Heli Nevanlinna Sampsa Hautaniemi Data integration workflow for search of disease driving genes and genetic variants.
description	Comprehensive characterization of a gene's impact on phenotypes requires knowledge of the context of the gene. To address this issue we introduce a systematic data integration method Candidate Genes and SNPs (CANGES) that links SNP and linkage disequilibrium data to pathway- and protein-protein interaction information. It can be used as a knowledge discovery tool for the search of disease associated causative variants from genome-wide studies as well as to generate new hypotheses on synergistically functioning genes. We demonstrate the utility of CANGES by integrating pathway and protein-protein interaction data to identify putative functional variants for (i) the p53 gene and (ii) three glioblastoma multiforme (GBM) associated risk genes. For the GBM case, we further integrate the CANGES results with clinical and genome-wide data for 209 GBM patients and identify genes having effects on GBM patient survival. Our results show that selecting a focused set of genes can result in information beyond the traditional genome-wide association approaches. Taken together, holistic approach to identify possible interacting genes and SNPs with CANGES provides a means to rapidly identify networks for any set of genes and generate novel hypotheses. CANGES is available in http://csbi.ltdk.helsinki.fi/CANGES/
format	article
author	Sirkku Karinen Tuomas Heikkinen Heli Nevanlinna Sampsa Hautaniemi
author_facet	Sirkku Karinen Tuomas Heikkinen Heli Nevanlinna Sampsa Hautaniemi
author_sort	Sirkku Karinen
title	Data integration workflow for search of disease driving genes and genetic variants.
title_short	Data integration workflow for search of disease driving genes and genetic variants.
title_full	Data integration workflow for search of disease driving genes and genetic variants.
title_fullStr	Data integration workflow for search of disease driving genes and genetic variants.
title_full_unstemmed	Data integration workflow for search of disease driving genes and genetic variants.
title_sort	data integration workflow for search of disease driving genes and genetic variants.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/e5bdbf09ac664c2eb2a02c516c9a3014
work_keys_str_mv	AT sirkkukarinen dataintegrationworkflowforsearchofdiseasedrivinggenesandgeneticvariants AT tuomasheikkinen dataintegrationworkflowforsearchofdiseasedrivinggenesandgeneticvariants AT helinevanlinna dataintegrationworkflowforsearchofdiseasedrivinggenesandgeneticvariants AT sampsahautaniemi dataintegrationworkflowforsearchofdiseasedrivinggenesandgeneticvariants
_version_	1718424201328590848

Data integration workflow for search of disease driving genes and genetic variants.

Ejemplares similares