Identifying causal genes and dysregulated pathways in complex diseases

In complex diseases, various combinations of genomic perturbations often lead to the same phenotype. On a molecular level, combinations of genomic perturbations are assumed to dys-regulate the same cellular pathways. Such a pathway-centric perspective is fundamental to understanding the mechanisms of complex diseases and the identification of potential drug targets. In order to provide an integrated perspective on complex disease mechanisms, we developed a novel computational method to simultaneously identify causal genes and dys-regulated pathways. First, we identified a representative set of genes that are differentially expressed in cancer compared to non-tumor control cases. Assuming that disease-associated gene expression changes are caused by genomic alterations, we determined potential paths from such genomic causes to target genes through a network of molecular interactions. Applying our method to sets of genomic alterations and gene expression profiles of 158 Glioblastoma multiforme (GBM) patients we uncovered candidate causal genes and causal paths that are potentially responsible for the altered expression of disease genes. We discovered a set of putative causal genes that potentially play a role in the disease. Combining an expression Quantitative Trait Loci (eQTL) analysis with pathway information, our approach allowed us not only to identify potential causal genes but also to find intermediate nodes and pathways mediating the information flow between causal and target genes. Our results indicate that different genomic perturbations indeed dys-regulate the same functional pathways, supporting a pathway-centric perspective of cancer. While copy number alterations and gene expression data of glioblastoma patients provided opportunities to test our approach, our method can be applied to any disease system where genetic variations play a fundamental causal role.     

Modifier genes of hereditary hearing loss

Phenotypic variation between individuals with the same disease alleles may be attributable to the genotype at another locus, which is referred to as a modifier gene. Recent functional studies of modifier genes of hearing-loss loci have begun to refine our understanding of hearing processes and will guide the rational design of medical therapies for hearing loss.     

Evolutionary dynamics of oncogenes and tumor suppressor genes: higher intensities of purifying selection than other genes

Oncogenes and tumor suppressor genes (hereafter referred to as "cancer genes") result in cancer when they experience substitutions that prevent or distort their normal function. We examined evolutionary pressures acting on cancer genes and other classes of disease-related genes and compared our results to analyses of genes without known association to disease. We compared synonymous and nonsynonymous substitution rates in 3,035 human genes-approximately 10% of the genome-measuring the intensity of purifying selection on 311 human disease genes, including 122 cancer-related genes. Although the genes examined are similar to nondisease genes in product, expression, function, and pathway affiliation, we found intriguing differences in the selective pressures experienced by cancer genes relative to other (noncancer) disease-related and non-disease-related genes. We found a statistically significant increase in the intensity of purifying selection exerted on cancer genes (the average ratio of nonsynonymous to synonymous substitutions, omega, was 0.079) relative to all other disease-related genes groups (omega = 0.101) and non-disease-related genes (omega = 0.100). This difference indicates a striking increase in selection against nonsynonymous substitutions in oncogenes and tumor suppressor genes. This finding provides insight into the etiology of cancer and the differences between genes involved in cancer and those implicated in other human diseases. Specifically, we found a significant overlap between human oncogenes and tumor suppressor genes and "essential genes," human homologs of mouse lethal genes identified by knockout experiments. This insight may improve our ability to identify cancer-related genes and enhances our understanding of the nature of these genes.     

Hunting human disease genes: lessons from the past, challenges for the future

The concept that a specific alteration in an individual’s DNA can result in disease is central to our notion of molecular medicine. The molecular basis of more than 3,500 Mendelian disorders has now been identified. In contrast, the identification of genes for common disease has been much more challenging. We discuss historical and contemporary approaches to disease gene identification, focusing on novel opportunities such as the use of population extremes and the identification of rare variants. While our ability to sequence DNA has advanced dramatically, assigning function to a given sequence change remains a major challenge, highlighting the need for both bioinformatics and functional approaches to appropriately interpret these data. We review progress in mapping and identifying human disease genes and discuss future challenges and opportunities for the field.     

Global risk transformative prioritization for prostate cancer candidate genes in molecular networks

Understanding the pathogenesis of complex diseases is aided by precise identification of the genes responsible. Many computational methods have been developed to prioritize candidate disease genes, but coverage of functional annotations may be a limiting factor for most of these methods. Here, we introduce a global candidate gene prioritization approach that considers information about network properties in the human protein interaction network and risk transformative contents from known disease genes. Global risk transformative scores were then used to prioritize candidate genes. This method was introduced to prioritize candidate genes for prostate cancer. The effectiveness of our global risk transformative algorithm for prioritizing candidate genes was evaluated according to validation studies. Compared with ToppGene and random walk-based methods, our method outperformed the two other candidate gene prioritization methods. The generality of our method was assessed by testing it on prostate cancer and other types of cancer. The performance was evaluated using standard leave-one-out cross-validation.     

Finding edging genes from microarray data

MOTIVATION: A set of genes and their gene expression levels are used to classify disease and normal tissues. Due to the massive number of genes in microarray, there are a large number of edges to divide different classes of genes in microarray space. The edging genes (EGs) can be co-regulated genes, they can also be on the same pathway or deregulated by the same non-coding genes, such as siRNA or miRNA. Every gene in EGs is vital for identifying a tissue's class. The changing in one EG's gene expression may cause a tissue alteration from normal to disease and vice versa. Finding EGs is of biological importance. In this work, we propose an algorithm to effectively find these EGs. RESULT: We tested our algorithm with five microarray datasets. The results are compared with the border-based algorithm which was used to find gene groups and subsequently divide different classes of tissues. Our algorithm finds a significantly larger amount of EGs than does the border-based algorithm. As our algorithm prunes irrelevant patterns at earlier stages, time and space complexities are much less prevalent than in the border-based algorithm. AVAILABILITY: The algorithm proposed is implemented in C++ on Linux platform. The EGs in five microarray datasets are calculated. The preprocessed datasets and the discovered EGs are available at http://www3.it.deakin.edu.au/~phoebe/microarray.html.     

Identifying modifier genes of monogenic disease: strategies and difficulties

Substantial clinical variability is observed in many Mendelian diseases, so that patients with the same mutation may develop a very severe form of disease, a mild form or show no symptoms at all. Among the factors that may explain these differences in disease expression are modifier genes. In this paper, we review the different strategies that can be used to identify modifier genes and explain their advantages and limitations. We focus mainly on the statistical aspects but illustrate our points with a variety of examples from the literature.     

Variations in inflammatory genes are associated with periodontitis

Background

Periodontitis is a multi-factorial disease and several risk-factors such as infections, inflammatory responses, oral hygiene, smoke, aging and individual predisposition are involved in the disease. Pathogens trigger chronic inflammation with cytokines release which in turn leads to the destruction of the connective and the teeth supporting bone. The identification of genetic factors controlling oral inflammation may increase our understanding of genetic predisposition to periodontitis.Single nucleotide polymorphisms in the promoter region of Vascular Endothelial Growth Factor, Alpha-1-Antichymotripsin, hydroxy-methyl-glutaryl CoA reductase, Interferon alpha, Interleukin-1 Beta, Interleukin 10, Interleukin 6 and Tumor Necrosis Factor- alpha genes from a case/control study were investigated.

Results

The C allele of Vascular Endothelial Growth Factor, A allele of Interleukin 10 and GG genotype of Tumor Necrosis Factor-α were individually associated with chronic periodontitis. However, the concomitant presence of the three genetic markers in the same subjects appeared to play a synergistic role and increased several folds the risk of the disease.

Conclusions

Our findings offer new tools to implement the screening of unaffected subjects with an increased susceptibility of periodontitis and increase our understanding regarding the genetic inflammatory background related to familiarity of the disease.

     

In search of genes involved in neurodegenerative disorders

Dissecting the genetics of Alzheimer's disease (AD) and Parkinson's disease (PD) has contributed significantly to our understanding of the pathogenesis of neurodegeneration in these two complex disorders. For AD, three highly penetrant genes (amyloid precursor protein (APP, PSEN1 and PSEN2) and one susceptibility gene (APOE) have been identified. For PD, seven genes (SNCA, Parkin, UCHL1, NR4A2, DJ1, PINK1 and LRRK2) have been found. These genes explain only a small proportion of AD and PD patients and are mostly associated with an early onset presentation of the disease. APOE remains the only common gene, which increases the risk of both rare early and late onset AD. The ongoing challenge is to unravel the genetics of the most frequent forms of these complex disorders. In the present paper, we briefly review the state of the art in the genetics of AD and PD. We also discuss the prospects of finding new genes associated with common forms of these diseases in light of two hypotheses concerning the genetic variation of complex diseases: common disease/common variants and common disease/rare variants.     

Ranking candidate disease genes from gene expression and protein interaction: a Katz-centrality based approach

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. Current genetic studies typically identify chromosomal regions associated specific diseases. But picking out an unknown disease gene from hundreds of candidates located on the same genomic interval is still challenging. 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. On these datasets our method is able to predict unknown disease genes as well as identifying pleiotropic genes involved in the physiological cellular processes of many diseases. Our study not only provides an effective algorithm for prioritizing candidate disease genes but is also a way to discover phenotypic interdependency, cooccurrence and shared pathophysiology between different disorders.     

Integrating node embeddings and biological annotations for genes to predict disease-gene associations

Background

Predicting disease causative genes (or simply, disease genes) has played critical roles in understanding the genetic basis of human diseases and further providing disease treatment guidelines. While various computational methods have been proposed for disease gene prediction, with the recent increasing availability of biological information for genes, it is highly motivated to leverage these valuable data sources and extract useful information for accurately predicting disease genes.

Results

We present an integrative framework called N2VKO to predict disease genes. Firstly, we learn the node embeddings from protein-protein interaction (PPI) network for genes by adapting the well-known representation learning method node2vec. Secondly, we combine the learned node embeddings with various biological annotations as rich feature representation for genes, and subsequently build binary classification models for disease gene prediction. Finally, as the data for disease gene prediction is usually imbalanced (i.e. the number of the causative genes for a specific disease is much less than that of its non-causative genes), we further address this serious data imbalance issue by applying oversampling techniques for imbalance data correction to improve the prediction performance. Comprehensive experiments demonstrate that our proposed N2VKO significantly outperforms four state-of-the-art methods for disease gene prediction across seven diseases.

Conclusions

In this study, we show that node embeddings learned from PPI networks work well for disease gene prediction, while integrating node embeddings with other biological annotations further improves the performance of classification models. Moreover, oversampling techniques for imbalance correction further enhances the prediction performance. In addition, the literature search of predicted disease genes also shows the effectiveness of our proposed N2VKO framework for disease gene prediction.

     

多基因遗传病基因研究的策略和方法

基因在决定个体表型方面起着决定性的作用。虽然单基因疾病的致病基因的克隆工作取得了显著的进展,但对于多基因疾病来说,仍然存在许多问题,同时也是巨大的挑战。本文综述了多基因疾病的遗传特点和多基因疾病易感基因识别、分离和克隆的一般步骤和存在的问题,介绍了人类基因组计划在此过程中的作用和单核苷酸多态性的应用前景,提出 了最有可能克隆出多基因疾病易感基因的策略和方法。     

From genome-wide association studies to etiology: probing autoimmunity genes by RNAi

Autoimmunity cannot yet be prevented or cured, in large part due to our poor understanding of disease etiology. Remarkable advances in genomic technology have recently enabled the discovery of a large number of disease-associated gene variations by genome-wide association studies. The next step towards understanding autoimmune disorders entails the functional study of susceptibility genes within experimental disease models. RNA interference (RNAi) is a promising tool for such investigations. Several features of RNAi, including its specificity, versatility and reversible nature, allow experimental systems to be tailored to relevant gene variations. This review discusses how the experimental use of RNAi is invaluable in bridging the gap between the identification of susceptibility genes and the elucidation of their functional contribution to autoimmune disease.     

Variation of presence/absence genes among Arabidopsis populations

Background

One of the main issues of molecular evolution is to divulge the principles in dictating the evolutionary rate differences among various gene classes. Immunological genes have received considerable attention in evolutionary biology as candidates for local adaptation and for studying functionally important polymorphisms. The normal structure and function of immunological genes will be distorted when they experience mutations leading to immunological dysfunctions.

Results

Here, we examined the fundamental differences between the genes which on mutation give rise to autoimmune or other immune system related diseases and the immunological genes that do not cause any disease phenotypes. Although the disease genes examined are analogous to non-disease genes in product, expression, function, and pathway affiliation, a statistically significant decrease in evolutionary rate has been found in autoimmune disease genes relative to all other immune related diseases and non-disease genes. Possible ways of accumulation of mutation in the three steps of the central dogma (DNA-mRNA-Protein) have been studied to trace the mutational effects predisposed to disease consequence and acquiring higher selection pressure. Principal Component Analysis and Multivariate Regression Analysis have established the predominant role of single nucleotide polymorphisms in guiding the evolutionary rate of immunological disease and non-disease genes followed by m-RNA abundance, paralogs number, fraction of phosphorylation residue, alternatively spliced exon, protein residue burial and protein disorder.

Conclusions

Our study provides an empirical insight into the etiology of autoimmune disease genes and other immunological diseases. The immediate utility of our study is to help in disease gene identification and may also help in medicinal improvement of immune related disease.     

Comprehensive analysis of photoreceptor gene expression and the identification of candidate retinal disease genes.

To identify the full set of genes expressed by mammalian rods, we conducted serial analysis of gene expression (SAGE) by using libraries generated from mature and developing mouse retina. We identified 264 uncharacterized genes that were specific to or highly enriched in rods. Nearly half of all cloned human retinal disease genes are selectively expressed in rod photoreceptors. In silico mapping of the human orthologs of genes identified in our screen revealed that 86 map within intervals containing uncloned retinal disease genes, representing 37 different loci. We expect these data will allow identification of many disease genes, and that this approach may be useful for cloning genes involved in classes of disease where cell type-specific expression of disease genes is observed.     

Walking the interactome for prioritization of candidate disease genes

The identification of genes associated with hereditary disorders has contributed to improving medical care and to a better understanding of gene functions, interactions, and pathways. However, there are well over 1500 Mendelian disorders whose molecular basis remains unknown. At present, methods such as linkage analysis can identify the chromosomal region in which unknown disease genes are located, but the regions could contain up to hundreds of candidate genes. In this work, we present a method for prioritization of candidate genes by use of a global network distance measure, random walk analysis, for definition of similarities in protein-protein interaction networks. We tested our method on 110 disease-gene families with a total of 783 genes and achieved an area under the ROC curve of up to 98% on simulated linkage intervals of 100 genes surrounding the disease gene, significantly outperforming previous methods based on local distance measures. Our results not only provide an improved tool for positional-cloning projects but also add weight to the assumption that phenotypically similar diseases are associated with disturbances of subnetworks within the larger protein interactome that extend beyond the disease proteins themselves.     

Protein tyrosine phosphatases: from genes, to function, to disease

The protein tyrosine phosphatase (PTP) superfamily of enzymes functions in a coordinated manner with protein tyrosine kinases to control signalling pathways that underlie a broad spectrum of fundamental physiological processes. In this review, I describe recent breakthroughs in our understanding of the role of the PTPs in the regulation of signal transduction and the aetiology of human disease.