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.
Bibliographical noteFunding Information:
This study makes use of data generated by the UK10K Consortium, which is derived from samples from the TwinsUK Cohort, the ALSPAC Cohort, and Cilia in Disease and Development study (CINDAD). A full list of the investigators who contributed to the generation of the data is available from www.UK10K.org. Funding for UK10K was provided by the Wellcome Trust under award WT091310. National Research Foundation of Korea [2012M3A9B4028641, 2012M3A9C7050151, 2015R1A2A1A15055859 to I.L. and 2016R1C1B2008930 to J.E.L.]. Funding for open access charge: National Reseach Foundation of Korea [2012M3A9B4028641, 2012M3A9C7050151, 2015R1A2A1A15055859 to I.L. and 2016R1C1B2008930 to J.E.L.].
All Science Journal Classification (ASJC) codes