Disease gene

“ Other refers to diseases caused by chromosomal translocations or chimeric proteins. Disease lethal genes are more likely to demonstrate a dominant mode of inheritance than other disease classes
Although genes from all groups seemingly cause disease predominantly through loss-of-function mechanisms, it is possible that essentiality may affect the mode of inheritance of disease genes. We therefore classified disease gene mutations as autosomal dominant, autosomal recessive, or sex-linked, and categorized them according to essentiality. ”

“ The similar physiology and genome structure between the mouse and human facilitate ortholog comparison and functional identification between the two species. To determine if differences exist between essential and non-essential disease genes we examined several parameters in our analyses, including the physiological systems affected by each disease gene, the connectivity of each gene in protein-protein interaction networks, and the genetic mechanisms by which genes cause human disease. ”

“ Abstract Many diseases have complex genetic causes, where a set of alleles can affect the propensity of getting the disease. The identification of such disease genes is important to understand the mechanistic and evolutionary aspects of pathogenesis, improve diagnosis and treatment of the disease, and aid in drug discovery. ”

“ The authors of one study therefore conclude that essential genes are not human disease genes [9] , as mutations prevent viability. However, this assumption fails to consider the impact of alleles on gene function. Genes have been defined as essential due to the phenotype of mouse knockouts, which result from a deletion of the protein-coding region of the gene from the genome. These mouse mutants therefore are null alleles, and represent the phenotype caused by complete absence of functional protein. However, point mutations in these same genes do not necessarily fully remove protein function. ”

“ In this study, we propose an approach to prioritize candidate genes by integrating data of gene expression level, protein-protein interaction strength and known disease genes. Our method is based only on two, simple, biologically motivated assumptions—that a gene is a good disease-gene candidate if it is differentially expressed in cases and controls, or that it is close to other disease-gene candidates in its protein interaction network. We tested our method on 40 diseases in 58 gene expression datasets of the NCBI Gene Expression Omnibus database. ”

“ It is important to note that the essentiality classification is based upon mouse null alleles, whereas human disease alleles are rarely null mutations. Therefore, when a gene is referred to as “disease lethal” it is not an indication that the human diseases associated with mutations in that gene are lethal, but rather that complete removal of protein function causes lethality in mouse. Importantly, in contrast to prior studies [5] , we considered all disease genes for which there is no mouse knockout data available as a separate group of “unknown” disease genes (DU, n = 1172) . ”

“ For a specific disease, the prediction result will be limited by existing knowledge about the disease, including the number of known disease genes and their relationships within the existing data sources. We have used the concept of disease families in order to increase the number of known disease genes in each family. Some recent studies have considered relationships/similarities between diseases/phenotypes [29] and have utilized phenotype similarities in their gene prioritization approach [30] – [31] . We will investigate approaches to incorporate phenotype similarities into our framework. ”

“ We searched the Morbid map of the Online Mendelian Inheritance in Man (OMIM) database [33] and identified 359 genes associated with the 40 distinct diseases in our microarray data, in which one disease was associated with at least one gene. As listed in Table S2, the disease with the most known causing genes is cardiomyopathy, with 32 disease genes known. A total of 318 of the 359 genes were found to present in the protein-protein interaction network constructed from STRING, and these genes were used to validate our algorithm (see Table S2) . ”

“ The recognition that disease genes are not a homogenous subset of human genes, and that essential genes cannot be excluded from consideration as candidates for all types of human disease genes, will aid in the identification of candidate disease gene loci for a variety of human diseases. ”

“ At the end of the day, how a disease is caused and thus how it can be treated can only be studied on the basis of the entire body of knowledge including all genes that are associated with the disease and their interactions through biological pathways. However, with the unprecedented wealth of information available, it is extremely difficult to obtain a complete picture of the genetic basis of diseases. In order to obtain such a complete picture, data integration from different sources is required. ”

“ This classification, however, over-estimates the size of the non-essential gene group, due to the inclusion of genes with no reported knockout data. As currently only approximately 9% of mouse genes have been knocked out (Dataset S1) , it is very likely that disease genes with no known mouse knockout phenotype would include both lethal and viable genes. Therefore, to include genes with no knockout data in the viable gene group confounds the analysis, and could lead to erroneous conclusions about the relative importance of lethal and viable genes in human disease. ”

“ Once the information from a data source is represented by a network, the relationship between a candidate gene and a disease can be measured by the relationship between the candidate gene and all known disease genes. ”

“ These datasets represent the gene expression levels of 40 distinct diseases. Our human protein interaction data comes from the STRING database of the human genome and proteome. We got the data on disease genes of the mentioned 40 diseases from the OMIM database. First, we predicted disease genes within disease-associated loci only based on gene expression levels and protein-protein interactions. ”

“ We obtained the human–mouse orthology and mouse viable/lethal phenotype data from Mouse Genome Informatics (http://www.informatics.jax.org) [26] , to give 2,360 human genes with information about lethality status of the mouse knockout of their ortholog in the whole genome, without consideration of whether the gene is annotated as a human disease gene. ”

“ It could be argued that such genes encode multifunctional proteins that participate in different biological processes, and mutations in these proteins affecting different functions can then lead to different disease phenotypes. ”

“ Moreover, influences of environmental factors, infectious agents or drugs have to be considered when studying the occurrence and evolution of a disease. In complex diseases, alterations in several genes can make subtle contributions to the susceptibility of a particular individual. ”

“ This means that both the protein interaction network and the differential expression contain information that can be exploited in disease-gene ranking, as hypothesized. On the other hand, we see that putting Ø = 0 worsen the performance much more than putting η = 0, which suggest that there is more information for the benefit of predicting unknown disease genes in the interaction compared with the microarray data, at least with our setup. Furthermore, we were able to increase our method's performance by including partial information about known disease genes. ”

“ Scientists have been trying to understand the molecular mechanisms of diseases to design preventive and therapeutic strategies for a long time. For some diseases, it has become evident that it is not enough to obtain a catalogue of the disease-related genes but to uncover how disruptions of molecular networks in the cell give rise to disease phenotypes. ”

“ From our study we can now present a composite profile of human disease gene types. We have found that DL and DV genes differ from each other in many of the characteristics we have analyzed. Overall, DL genes are more likely to be involved in more protein-protein interactions, encode nuclear proteins, be associated with diseases such as cancer and those affecting multiple systems, and have an autosomal dominant mode of inheritance. ”

“ In this work we developed a comprehensive database of human gene-disease associations by integrating both, information from different databases and from literature, in order to bridge the gaps between the aforementioned knowledge pockets. The resulting database (DisGeNET database) comprises the whole spectrum of human diseases with genetic origin, including mendelian, complex and environmental diseases, and represents, to the best of our knowledge, the most complete view on human gene-disease associations that is currently publicly available. ”

“ This result suggests that the known disease genes were averagely ranked top 24.6% of the candidates by integrating gene expression levels with protein interactions, whereas they ranked top 41.8% and 25%, on average, respectively, if only gene expression data or protein interaction network were utilized. ”

“ Even though the networks are quite comprehensive, some disease genes may not occur in a network at all, or may have limited connections. Therefore, for some disease families, it is not appropriate to use the data sources to prioritize genes if the networks themselves do not contain enough information about these disease families. ”

“ In conclusion, clustering analysis of the gene projection network followed by functional enrichment analysis can be used to propose new candidate disease genes. We would also like to mention here that the CURATED set of DisGeNET represents a suitable dataset to benchmark computational methods for the prediction of candidate disease genes. ”

“ To calculate random controls for pathway homogeneity for single diseases and disease clusters, we randomly sampled genes from the set of disease genes of the studied network with annotation to pathways. We then took the annotation of the corresponding gene products and calculated pathway homogeneity values. Random controls for gene clusters were obtained by randomly assigning genes to clusters while total number of clusters and original cluster sizes were maintained. ”

“ Gene alleles with reduced function, called hypomorphic alleles, therefore can generate different phenotypes from those of null alleles. Although null or severe loss-of-function mutations in essential genes may indeed contribute to spontaneous miscarriages, hypomorphic mutations in the same genes can contribute to less severe abnormalities that are recognized as human disease. Therefore, the human orthologs of genes required for embryonic development in the mouse can cause disease in mutated forms through a variety of mechanisms. ”

“ Diseases are coloured according to their disease class (see Fig. S4) . The most significantly enriched Jak-STAT signaling pathway is displayed with some nodes from the cluster colored in red (right part) . GO term and pathway enrichment analysis showed that for this heterogeneous cluster, there are common biological processes associated with the distinct diseases. For instance, although there are 59 pathways annotated to this cluster, the pathway homogeneity is 41% indicating that almost half of the gene products appear in the same pathway. ”