Disease Gene Prioritization Using Network and Feature

Abstract

Identifying high-confidence candidate genes that are causative for disease phenotypes, from the large lists of variations produced by high-throughput genomics, can be both timeconsuming and costly. The development of novel computational approaches, utilizing existing biological knowledge for the prioritization of such candidate genes, can improve the efficiency and accuracy of the biomedical data analysis. It can also reduce the cost of such studies by avoiding experimental validations of irrelevant candidates. In this study, we address this challenge by proposing a novel gene prioritization approach that ranks promising candidate genes that are likely to be involved in a disease or phenotype under study. This algorithm is based on the modified conditional random field (CRF) model that simultaneously makes use of both gene annotations and gene interactions, while preserving their original representation. We validated our approach on two independent disease benchmark studies by ranking candidate genes using network and feature information. Our results showed both high area under the curve (AUC) value (0.86), and more importantly high partial AUC (pAUC) value (0.1296), and revealed higher accuracy and precision at the top predictions as compared with other well-performed gene prioritization tools, such as Endeavour (AUC-0.82, pAUC-0.083) and PINTA (AUC-0.76, pAUC-0.066). We were able to detect more target genes (9/18/19/27) on top positions (1/5/10/20) compared to Endeavour (3/11/14/23) and PINTA (6/10/13/18). To demonstrate its usability, we applied our method to a case study for the prediction of molecular mechanisms contributing to intellectual disability and autism. Our approach was able to correctly recover genes related to both disorders and provide suggestions for possible additional candidates based on their rankings and functional annotations.

A large-scale benchmark of gene prioritization methods

“…Some, including, a Conditional Random Field based algorithm39, Endeavour40 and PINTA41 have demonstrated good performance on a subset of metrics tested here139. These and many other methods rely on annotations and or phenotype profiles while our interest lies in being able to assess algorithms which rely solely on the underlying interaction network without the addition of annotations which may bias results towards the more studied genes.…”

Lynx: a knowledge base and an analytical workbench for integrative medicine

“…Please refer to Xie et al . ( 3 , 4 ) for a detailed description of the Cheetoh algorithm and its performance evaluation and validation procedures. The output of the tool consists of 1000 top ranked genes ordered by ascending Bonferroni (multiple testing correction) corrected P -values based on all user-selected categories as well as rankings and corrected P -values from individual category.…”

“…Enrichment analysis tool) and the new tools developed (e.g. Cheetoh algorithm) ( 3 , 4 ). Integration of this information also enhances data annotation in Lynx.…”

“…Since the last release the Lynx workbench has been supplemented with a number of new tools. These include Cheetoh ( 3 ), a unique feature-and-network-based gene-prioritization tool and NetLynx (in press), a tool for the reconstruction of co-expression networks.…”

Mislocalization-related disease gene discovery using gene expression based computational protein localization prediction

“…To address this issue, many computational candidate gene prioritization algorithms have been developed by exploiting the biomedical knowledge available about the disease of interest and related genes [4]. For example, network information and heterogeneous phenomic and genomic data sources have been used to rank candidate genes [5,6,7,8,9,10,11]. A recent work [4] integrates a plethora of phenomic and genomic data to pinpoint disease genes including disease phenotype similarity derived from the Unified Medical Language System (UMLS) and seven types of gene functional similarities calculated from gene expression, gene ontology, pathway membership, protein sequence, protein domain, protein-protein interaction and regulation pattern, respectively.…”