Abstract
BackgroundHigh-throughput molecular interaction data have been used effectively to prioritize candidate genes that are linked to a disease, based on the observation that the products of genes associated with similar diseases are likely to interact with each other heavily in a network of protein-protein interactions (PPIs). An important challenge for these applications, however, is the incomplete and noisy nature of PPI data. Information flow based methods alleviate these problems to a certain extent, by considering indirect interactions and multiplicity of paths.ResultsWe demonstrate that existing methods are likely to favor highly connected genes, making prioritization sensitive to the skewed degree distribution of PPI networks, as well as ascertainment bias in available interaction and disease association data. Motivated by this observation, we propose several statistical adjustment methods to account for the degree distribution of known disease and candidate genes, using a PPI network with associated confidence scores for interactions. We show that the proposed methods can detect loosely connected disease genes that are missed by existing approaches, however, this improvement might come at the price of more false negatives for highly connected genes. Consequently, we develop a suite called DADA, which includes different uniform prioritization methods that effectively integrate existing approaches with the proposed statistical adjustment strategies. Comprehensive experimental results on the Online Mendelian Inheritance in Man (OMIM) database show that DADA outperforms existing methods in prioritizing candidate disease genes.ConclusionsThese results demonstrate the importance of employing accurate statistical models and associated adjustment methods in network-based disease gene prioritization, as well as other network-based functional inference applications. DADA is implemented in Matlab and is freely available at http://compbio.case.edu/dada/.
Highlights
Identification of disease-associated genes is an important step toward enhancing our understanding of the cellular mechanisms that drive human diseases, with profound applications in modeling, diagnosis, prognosis, and therapeutic intervention [1]
Several algorithms have been proposed to incorporate topological properties of protein-protein interactions (PPIs) networks in understanding genetic diseases [3,8,13]. These algorithms mostly focus on prioritization of candidate genes and mainly exploit the notion that the products of genes associated with similar diseases have a higher chance of being connected in the network of PPIs
We use three reference models that take into account the degree distribution of the PPI network: (i) reference model based on degree distribution of known disease gene products, (ii) reference model based on the degree of candidate gene products, and (iii) likelihood ratio test using eigenvector centrality as the reference model
Summary
Identification of disease-associated genes is an important step toward enhancing our understanding of the cellular mechanisms that drive human diseases, with profound applications in modeling, diagnosis, prognosis, and therapeutic intervention [1]. Several algorithms have been proposed to incorporate topological properties of PPI networks in understanding genetic diseases [3,8,13] These algorithms mostly focus on prioritization of candidate genes and mainly exploit the notion that the products of genes associated with similar diseases have a higher chance of being connected in the network of PPIs. an important challenge for these applications is the incomplete and noisy nature of the PPI data [15]. Network-based candidate disease gene prioritization There exists a wide range of disease gene prioritization methods that are based on the analysis of the topological properties of PPI networks These methods commonly rely on the observation that the products of genes that are associated with similar diseases have a higher likelihood of physically interacting [11]. Any reference to interactions between genes in this paper refers to the interactions between their products
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