Function-driven discovery of disease genes in zebrafish using an integrated genomics big data resource

Whole exome sequencing (WES) accelerates disease gene discovery using rare genetic variants, but further statistical and functional evidence is required to avoid false-discovery. To complement variant-driven disease gene discovery, here we present function-driven disease gene discovery in zebrafish...

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Main Authors:	Shim, Hongseok, Kim, Ji Hyun, Kim, Chan Yeong, Hwang, Sohyun, Kim, Hyojin, Yang, Sunmo, Lee, Ji Eun, Lee, Insuk
Format:	Online
Language:	English
Published:	Oxford University Press 2016
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5175370/

id	pubmed-5175370
recordtype	oai_dc
spelling	pubmed-51753702016-12-27 Function-driven discovery of disease genes in zebrafish using an integrated genomics big data resource Shim, Hongseok Kim, Ji Hyun Kim, Chan Yeong Hwang, Sohyun Kim, Hyojin Yang, Sunmo Lee, Ji Eun Lee, Insuk Data Resources and Analyses Whole exome sequencing (WES) accelerates disease gene discovery using rare genetic variants, but further statistical and functional evidence is required to avoid false-discovery. To complement variant-driven disease gene discovery, here we present function-driven disease gene discovery in zebrafish (Danio rerio), a promising human disease model owing to its high anatomical and genomic similarity to humans. To facilitate zebrafish-based function-driven disease gene discovery, we developed a genome-scale co-functional network of zebrafish genes, DanioNet (www.inetbio.org/danionet), which was constructed by Bayesian integration of genomics big data. Rigorous statistical assessment confirmed the high prediction capacity of DanioNet for a wide variety of human diseases. To demonstrate the feasibility of the function-driven disease gene discovery using DanioNet, we predicted genes for ciliopathies and performed experimental validation for eight candidate genes. We also validated the existence of heterozygous rare variants in the candidate genes of individuals with ciliopathies yet not in controls derived from the UK10K consortium, suggesting that these variants are potentially involved in enhancing the risk of ciliopathies. These results showed that an integrated genomics big data for a model animal of diseases can expand our opportunity for harnessing WES data in disease gene discovery. Oxford University Press 2016-11-16 2016-10-05 /pmc/articles/PMC5175370/ /pubmed/27903883 http://dx.doi.org/10.1093/nar/gkw897 Text en © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Shim, Hongseok Kim, Ji Hyun Kim, Chan Yeong Hwang, Sohyun Kim, Hyojin Yang, Sunmo Lee, Ji Eun Lee, Insuk
spellingShingle	Shim, Hongseok Kim, Ji Hyun Kim, Chan Yeong Hwang, Sohyun Kim, Hyojin Yang, Sunmo Lee, Ji Eun Lee, Insuk Function-driven discovery of disease genes in zebrafish using an integrated genomics big data resource
author_facet	Shim, Hongseok Kim, Ji Hyun Kim, Chan Yeong Hwang, Sohyun Kim, Hyojin Yang, Sunmo Lee, Ji Eun Lee, Insuk
author_sort	Shim, Hongseok
title	Function-driven discovery of disease genes in zebrafish using an integrated genomics big data resource
title_short	Function-driven discovery of disease genes in zebrafish using an integrated genomics big data resource
title_full	Function-driven discovery of disease genes in zebrafish using an integrated genomics big data resource
title_fullStr	Function-driven discovery of disease genes in zebrafish using an integrated genomics big data resource
title_full_unstemmed	Function-driven discovery of disease genes in zebrafish using an integrated genomics big data resource
title_sort	function-driven discovery of disease genes in zebrafish using an integrated genomics big data resource
description	Whole exome sequencing (WES) accelerates disease gene discovery using rare genetic variants, but further statistical and functional evidence is required to avoid false-discovery. To complement variant-driven disease gene discovery, here we present function-driven disease gene discovery in zebrafish (Danio rerio), a promising human disease model owing to its high anatomical and genomic similarity to humans. To facilitate zebrafish-based function-driven disease gene discovery, we developed a genome-scale co-functional network of zebrafish genes, DanioNet (www.inetbio.org/danionet), which was constructed by Bayesian integration of genomics big data. Rigorous statistical assessment confirmed the high prediction capacity of DanioNet for a wide variety of human diseases. To demonstrate the feasibility of the function-driven disease gene discovery using DanioNet, we predicted genes for ciliopathies and performed experimental validation for eight candidate genes. We also validated the existence of heterozygous rare variants in the candidate genes of individuals with ciliopathies yet not in controls derived from the UK10K consortium, suggesting that these variants are potentially involved in enhancing the risk of ciliopathies. These results showed that an integrated genomics big data for a model animal of diseases can expand our opportunity for harnessing WES data in disease gene discovery.
publisher	Oxford University Press
publishDate	2016
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5175370/
_version_	1613807587652796416

Function-driven discovery of disease genes in zebrafish using an integrated genomics big data resource

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