ZFIN ID: ZDB-PUB-161203-11
Function-driven discovery of disease genes in zebrafish using an integrated genomics big data resource
Shim, H., Kim, J.H., Kim, C.Y., Hwang, S., Kim, H., Yang, S., Lee, J.E., Lee, I.
Date: 2016
Source: Nucleic acids research 44: 9611-9623 (Journal)
Registered Authors: Lee, Ji Eun
Keywords: none
MeSH Terms:
  • Algorithms
  • Animals
  • Bayes Theorem
  • Computational Biology/methods
  • Datasets as Topic
  • Exome
  • Genetic Association Studies*/methods
  • Genetic Predisposition to Disease*
  • Genetic Variation
  • Genomics*/methods
  • High-Throughput Nucleotide Sequencing
  • Humans
  • Molecular Sequence Annotation
  • Zebrafish/genetics*
PubMed: 27903883 Full text @ Nucleic Acids Res.
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ABSTRACT
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.
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