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

Area of Science:

• Genomics
• Systems Biology
• Zebrafish Model Organisms

Background:

• Whole exome sequencing (WES) is crucial for identifying rare genetic variants in disease gene discovery.
• Statistical and functional validation are essential to confirm WES findings and avoid false discoveries.
• Zebrafish (Danio rerio) offer a valuable model for human disease research due to conserved anatomy and genomics.

Purpose of the Study:

• To develop a function-driven approach for disease gene discovery.
• To create a genome-scale co-functional network for zebrafish genes (DanioNet).
• To demonstrate DanioNet's utility in identifying and validating candidate disease genes.

Main Methods:

• Bayesian integration of big genomics data to construct DanioNet.
• Statistical assessment of DanioNet's predictive capacity for human diseases.
• Experimental validation of candidate genes for ciliopathies identified using DanioNet.
• Analysis of rare heterozygous variants in patient cohorts (UK10K consortium).

Main Results:

• DanioNet demonstrates high predictive power for various human diseases.
• Eight candidate genes for ciliopathies were experimentally validated.
• Heterozygous rare variants in candidate genes were found in individuals with ciliopathies but not controls.
• The findings suggest these variants may increase ciliopathy risk.

Conclusions:

• DanioNet facilitates function-driven disease gene discovery in zebrafish.
• Integrated genomics data from model organisms enhance WES data utility.
• This approach expands opportunities for harnessing genomic data in disease gene discovery.