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The identification of genes involved in human hereditary diseases often requires the time-consuming and expensive examination of a great number of possible candidate genes, since genome-wide techniques such as linkage analysis and association studies frequently select many hundreds of 'positional' candidates. Even considering the positive impact of next-generation sequencing technologies, the prioritization of candidate genes may be an important step for disease-gene identification. In this paper we develop a basic classification scheme for computational approaches to disease-gene prediction and apply it to exhaustively review bioinformatics tools that have been developed for this purpose, focusing on conceptual aspects rather than technical detail and performance. Finally, we discuss some past successes obtained by computational approaches to illustrate their beneficial contribution to medical research., (© 2012 The Authors Journal compilation © 2012 FEBS.)

Background: High-throughput gene expression data can predict gene function through the ''guilt by association'' principle: coexpressed genes are likely to be functionally associated. Methodology/Principal Findings: We analyzed publicly available expression data on normal human tissues. The analysis is based on the integration of data obtained with two experimental platforms (microarrays and SAGE) and of various measures of dissimilarity between expression profiles. The building blocks of the procedure are the Ranked Coexpression Groups (RCG), small sets of tightly coexpressed genes which are analyzed in terms of functional annotation. Functionally characterized RCGs are selected by means of the majority rule and used to predict new functional annotations. Functionally characterized RCGs are enriched in groups of genes associated to similar phenotypes. We exploit this fact to find new candidate disease genes for many OMIM phenotypes of unknown molecular origin. Conclusions/Significance: We predict new functional annotations for many human genes, showing that the integration of different data sets and coexpression measures significantly improves the scope of the results. Combining gene expression data, functional annotation and known phenotype-gene associations we provide candidate genes for several genetic diseases of unknown molecular basis. [ABSTRACT FROM AUTHOR]

Background: Even in the post-genomic era, the identification of candidate genes within loci associated with human genetic diseases is a very demanding task, because the critical region may typically contain hundreds of positional candidates. Since genes implicated in similar phenotypes tend to share very similar expression profiles, high throughput gene expression data may represent a very important resource to identify the best candidates for sequencing. However, so far, gene coexpression has not been used very successfully to prioritize positional candidates. Methodology/Principal Findings: We show that it is possible to reliably identify disease-relevant relationships among genes from massive microarray datasets by concentrating only on genes sharing similar expression profiles in both human and mouse. Moreover, we show systematically that the integration of human-mouse conserved coexpression with a phenotype similarity map allows the efficient identification of disease genes in large genomic regions. Finally, using this approach on 850 OMIM loci characterized by an unknown molecular basis, we propose high-probability candidates for 81 genetic diseases. Conclusion: Our results demonstrate that conserved coexpression, even at the human-mouse phylogenetic distance, represents a very strong criterion to predict disease-relevant relationships among human genes. [ABSTRACT FROM AUTHOR]

Haptoglobin and Hemopexin are plasma acute phase proteins that bind with high-affinity hemoglobin and heme, respectively. They play a key role in the protection against oxidative stress and inflammation. To dissect in more detail the mechanism of action of Haptoglobin and Hemopexin, it is important to identify their downstream effectors as well as genes functionally related to them. To this end, we performed a cDNA microarray analysis to compare gene expression profiles of the liver of Haptoglobin and Hemopexin single and double null mice to that of wild-type controls. Then, to extract the best candidates considered to be functionally related to Haptoglobin and/or Hemopexin from microarray-derived gene lists, we used a bioinformatic approach consisting in the screening of published microarray data for genes showing coexpression with Haptoglobin or Hemopexin. This strategy allowed us to identify a group of genes coexpressed with Haptoglobin or Hemopexin and transcriptionally modulated by their lack. These genes present a high probability to be functionally related to Haptoglobin and Hemopexin. Based on literature data, we picked up from this group of genes the ras suppressor Rsu1, the member of the G-protein signal transduction family Gnai2, and the cytokine Mdk as the best candidates mediating the anti-inflammatory action of Haptoglobin and Hemopexin. [ABSTRACT FROM AUTHOR]

Background: Gene regulation in eukaryotes is mainly effected through transcription factors binding to rather short recognition motifs generally located upstream of the coding region. We present a novel computational method to identify regulatory elements in the upstream region of eukaryotic genes. The genes are grouped in sets sharing an overrepresented short motif in their upstream sequence. For each set, the average expression level from a microarray experiment is determined: If this level is significantly higher or lower than the average taken over the whole genome, then the overerpresented motif shared by the genes in the set is likely to play a role in their regulation. Results: The method was tested by applying it to the genome of Saccharomyces cerevisiae, using the publicly available results of a DNA microarray experiment, in which expression levels for virtually all the genes were measured during the diauxic shift from fermentation to respiration. Several known motifs were correctly identified, and a new candidate regulatory sequence was determined. Conclusions: We have described and successfully tested a simple computational method to identify upstream motifs relevant to gene regulation in eukaryotes by studying the statistical correlation between overepresented upstream motifs and gene expression levels. [ABSTRACT FROM AUTHOR]

Motivation: The identification of genes involved in specific phenotypes, such as human hereditary diseases, often requires the time-consuming and expensive examination of a large number of positional candidates selected by genome-wide techniques such as linkage analysis and association studies. Even considering the positive impact of next-generation sequencing technologies, the prioritization of these positional candidates may be an important step for disease-gene identification. Results: Here, we report a large-scale analysis of spatial, i.e. 3D, gene-expression data from an entire organ (the mouse brain) for the purpose of evaluating and ranking positional candidate genes, showing that the spatial gene-expression patterns can be successfully exploited for the prediction of gene-phenotype associations not only for mouse phenotypes, but also for human central nervous system-related Mendelian disorders. We apply our method to the case of X-linked mental retardation, compare the predictions to the results obtained from a previous large-scale resequencing study of chromosome X and discuss some promising novel candidates. [ABSTRACT FROM AUTHOR]