Abstract
Identifying disease genes is crucial to the understanding of disease pathogenesis, and to the improvement of disease diagnosis and treatment. In recent years, many researchers have proposed approaches to prioritize candidate genes by considering the relationship of candidate genes and existing known disease genes, reflected in other data sources. In this paper, we propose an expandable framework for gene prioritization that can integrate multiple heterogeneous data sources by taking advantage of a unified graphic representation. Gene-gene relationships and gene-disease relationships are then defined based on the overall topology of each network using a diffusion kernel measure. These relationship measures are in turn normalized to derive an overall measure across all networks, which is utilized to rank all candidate genes. Based on the informativeness of available data sources with respect to each specific disease, we also propose an adaptive threshold score to select a small subset of candidate genes for further validation studies. We performed large scale cross-validation analysis on 110 disease families using three data sources. Results have shown that our approach consistently outperforms other two state of the art programs. A case study using Parkinson disease (PD) has identified four candidate genes (UBB, SEPT5, GPR37 and TH) that ranked higher than our adaptive threshold, all of which are involved in the PD pathway. In particular, a very recent study has observed a deletion of TH in a patient with PD, which supports the importance of the TH gene in PD pathogenesis. A web tool has been implemented to assist scientists in their genetic studies.
Highlights
Dissecting genetic architectures of human diseases is a fundamental task in human genetics and has profound implications in biomedical research
We identified four candidate genes (UBB, septin 5 (SEPT5), G protein-coupled receptor 37 (GPR37) and Tyrosine hydroxylase (TH)) that ranked higher than our adaptive threshold, all of which are involved in the Parkinson disease (PD) pathway
We have proposed a candidate gene prioritization approach that can integrate multiple data sources by taking advantage of a unified graphic representation of information
Summary
Dissecting genetic architectures of human diseases is a fundamental task in human genetics and has profound implications in biomedical research. Great challenges exist because many common diseases are caused by multiple disease genes with small to moderate effects. Even diseases that show Mendelian inheritance may involve multiple genes due to heterogeneity. Researchers have increasingly realized that there are many levels of controls along the paths from genotypes to phenotypes, resulting in a weaker relationship between genotypes and phenotypes [1] that may or may not be captured using traditional linkage or association approaches. Linkage analysis usually can only identify chromosomal intervals that may contain up to hundreds of candidate genes owning to the limited number of crossovers in sampled families. Genome-wide association studies may return many regions that show moderate to high signals. Experimental validations of so many candidate genes are usually beyond the ability of individual researchers owning to prohibitively high costs, both in terms of fund and time
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