Identification Of Disease Genes Research Articles

Abstract IA16: The ceRNA hypothesis and the non-coding revolution in cancer research and therapy

Abstract The central dogma of molecular biology, as proposed by Francis Crick, demonstrates that genetic information is transferred from DNA in our genomes to the generation of functional proteins, through a messenger RNA (mRNA) intermediate. This suggests that the key function of each mRNA is to encode for protein. We have demonstrated that mRNAs can exert a biological activity that is independent of the protein for which they encode. Since this phenomenon is dependent on sequences contained in RNA transcripts, this phenomenon applies to protein-coding genes, pseudogenes, of which 19,000 have been predicted in the genome as well as long non-coding RNAs that have been recently identified to be pervasive in the cell and in biology. This new function is brought about by the ability of mRNAs—and RNAs in general—to bind and sequester microRNA molecules. microRNAs specifically repress the expression levels of many genes and have consequently been shown to play important roles in diseases including cancer. We show that any messenger RNA and RNA molecule can sequester microRNA molecules acting as a “competitive endogenous RNAs.” We therefore term them ceRNAs. Given that microRNAs can bind to multiple mRNAs, we argued that mRNAs compete for the binding to a given microRNA. We originally tested this hypothesis and proved its validity by studying the interaction between the mRNA encoding for the PTEN tumor suppressor gene and its closely related pseudogene, PTENP1, which we show to act as a tumor suppressor through this new mechanism. We therefore identify PTENP1 as well as the several thousand uncharacterized RNA molecules as potential human disease genes. We have now expanded this analysis to the whole transcriptome. Our findings therefore defines a new biological dimension that will allow for the rapid identification and functional characterization of new disease genes. Citation Format: Pier Paolo Pandolfi. The ceRNA hypothesis and the non-coding revolution in cancer research and therapy [abstract]. In: Proceedings of the AACR Special Conference on Noncoding RNAs and Cancer; 2012 Jan 8-11; Miami Beach, FL. Philadelphia (PA): AACR; Cancer Res 2012;72(2 Suppl):Abstract nr IA16.

Disease gene identification by random walk on multigraphs merging heterogeneous genomic and phenotype data

BackgroundHigh throughput experiments resulted in many genomic datasets and hundreds of candidate disease genes. To discover the real disease genes from a set of candidate genes, computational methods have been proposed and worked on various types of genomic data sources. As a single source of genomic data is prone of bias, incompleteness and noise, integration of different genomic data sources is highly demanded to accomplish reliable disease gene identification.ResultsIn contrast to the commonly adapted data integration approach which integrates separate lists of candidate genes derived from the each single data sources, we merge various genomic networks into a multigraph which is capable of connecting multiple edges between a pair of nodes. This novel approach provides a data platform with strong noise tolerance to prioritize the disease genes. A new idea of random walk is then developed to work on multigraphs using a modified step to calculate the transition matrix. Our method is further enhanced to deal with heterogeneous data types by allowing cross-walk between phenotype and gene networks. Compared on benchmark datasets, our method is shown to be more accurate than the state-of-the-art methods in disease gene identification. We also conducted a case study to identify disease genes for Insulin-Dependent Diabetes Mellitus. Some of the newly identified disease genes are supported by recently published literature.ConclusionsThe proposed RWRM (Random Walk with Restart on Multigraphs) model and CHN (Complex Heterogeneous Network) model are effective in data integration for candidate gene prioritization.

Evolutionary dynamics of human autoimmune disease genes and malfunctioned immunological genes

BackgroundOne 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.ResultsHere, 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.ConclusionsOur 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.

Metals and Genetics

Covers significant discoveries in the rapidly advancing field of metals and genetics. The aim of this volume is to bring together investigators from diverse fields of clinical medicine, genetics, biochemistry, and chemistry to reflect on the broad implications of direct and indirect interactions of metals and genetic components. The volume is divided into five sections. The first discusses genetic response to environmental exposure to metals. The section on metal carcinogenesis and metal caused DNA damage presents the latest advances in our knowledge of the molecular mechanisms of metal-induced mutagenis and carcinogenesis. A section is devoted to metals and neurodegenerative diseases. The identification of several disease genes related to metals is a major breakthrough in recent years. The section on genetics and biochemistry of metal-related diseases presents authoritative accounts of current (1999) information on both inherited and acquired metal-related diseases. They have discussed the genetics and pathophysiology of these diseases and reported the cloning, expression, purification, and characterization of gene products.

Recombination mapping using Boolean logic and high-density SNP genotyping for exome sequence filtering

Whole genome sequence data for small pedigrees has been shown to provide sufficient information to resolve detailed haplotypes in small pedigrees. Using such information, recombinations can be mapped onto chromosomes, compared with the segregation of a disease of interest and used to filter genome sequence variants. We now show that relatively inexpensive SNP array data from small pedigrees can be used in a similar manner to provide a means of identifying regions of interest in exome sequencing projects. We demonstrate that in those situations where one can assume complete penetrance and parental DNA is available, SNP recombination mapping using Boolean logic identifies chromosomal regions identical to those detected by multipoint linkage using microsatellites but with much less computation. We further show that this approach is successful because the probability of a double crossover between informative SNP loci is negligible. Our observations provide a rationale for using SNP arrays and recombination mapping as a rapid and cost-effective means of incorporating chromosome segregation information into exome sequencing projects intended for disease-gene identification.

Identification of dysfunctional modules and disease genes in congenital heart disease by a network-based approach

BackgroundThe incidence of congenital heart disease (CHD) is continuously increasing among infants born alive nowadays, making it one of the leading causes of infant morbidity worldwide. Various studies suggest that both genetic and environmental factors lead to CHD, and therefore identifying its candidate genes and disease-markers has been one of the central topics in CHD research. By using the high-throughput genomic data of CHD which are available recently, network-based methods provide powerful alternatives of systematic analysis of complex diseases and identification of dysfunctional modules and candidate disease genes.ResultsIn this paper, by modeling the information flow from source disease genes to targets of differentially expressed genes via a context-specific protein-protein interaction network, we extracted dysfunctional modules which were then validated by various types of measurements and independent datasets. Network topology analysis of these modules revealed major and auxiliary pathways and cellular processes in CHD, demonstrating the biological usefulness of the identified modules. We also prioritized a list of candidate CHD genes from these modules using a guilt-by-association approach, which are well supported by various kinds of literature and experimental evidence.ConclusionsWe provided a network-based analysis to detect dysfunctional modules and disease genes of CHD by modeling the information transmission from source disease genes to targets of differentially expressed genes. Our method resulted in 12 modules from the constructed CHD subnetwork. We further identified and prioritized candidate disease genes of CHD from these dysfunctional modules. In conclusion, module analysis not only revealed several important findings with regard to the underlying molecular mechanisms of CHD, but also suggested the distinct network properties of causal disease genes which lead to identification of candidate CHD genes.

Identification of autosomal recessive disease loci using out-bred nuclear families

Autozygosity mapping has been a powerful method for the identification of autosomal recessive disease genes. However, the approach is limited by the availability of suitable consanguineous pedigrees. While rare autosomal recessive diseases are overrepresented in consanguineous families, a significant proportion of affected patients nonetheless originate in families where the parents are apparently unrelated. However, due to their relative rarity and the heterogeneity of disease alleles, it has proved difficult to use these patients to identify disease loci. Therefore, we developed "Phaser," a computer application that is able to infer the phase of SNP alleles and so haplotype entire chromosomes in small nuclear families (http://dna.leeds.ac.uk/Phaser). Once the index case's chromosomes have been haplotyped, it is then possible to deduce those of the parents and subsequently identify the parental origin of all the siblings' DNA. By combining information from a small number of nuclear families, it may then be possible to identify linkage to the recessive disease locus, in both in-bred and out-bred families. We have illustrated the program's utility by using it to correctly identify both the cystic fibrosis locus (using two unrelated compound heterozygous CEPH families) and a new gene mutated in early-onset myopathy with respiratory distress and dysphagia locus in a single consanguineous pedigree.

ProDiGe: Prioritization Of Disease Genes with multitask machine learning from positive and unlabeled examples

BackgroundElucidating the genetic basis of human diseases is a central goal of genetics and molecular biology. While traditional linkage analysis and modern high-throughput techniques often provide long lists of tens or hundreds of disease gene candidates, the identification of disease genes among the candidates remains time-consuming and expensive. Efficient computational methods are therefore needed to prioritize genes within the list of candidates, by exploiting the wealth of information available about the genes in various databases.ResultsWe propose ProDiGe, a novel algorithm for Prioritization of Disease Genes. ProDiGe implements a novel machine learning strategy based on learning from positive and unlabeled examples, which allows to integrate various sources of information about the genes, to share information about known disease genes across diseases, and to perform genome-wide searches for new disease genes. Experiments on real data show that ProDiGe outperforms state-of-the-art methods for the prioritization of genes in human diseases.ConclusionsProDiGe implements a new machine learning paradigm for gene prioritization, which could help the identification of new disease genes. It is freely available at http://cbio.ensmp.fr/prodige.

SNP Array Analysis in Constitutional and Cancer Genome Diagnostics – Copy Number Variants, Genotyping and Quality Control

Array-based comparative genomic hybridization analysis of genomic DNA was first applied in postnatal diagnosis for patients with intellectual disability (ID) and/or congenital anomalies (CA). Genome-wide single-nucleotide polymorphism (SNP) array analysis was subsequently implemented as the first line diagnostic test for ID/CA patients in our laboratory in 2009, because its diagnostic yield is significantly higher than that of routine cytogenetic analysis. In addition to the detection of copy number variations, the genotype information obtained with SNP array analysis enables the detection of stretches of homozygosity and thereby the possible identification of recessive disease genes, mosaic aneuploidy, or uniparental disomy. Patient-parent (trio) information analysis is used to screen for the presence of any form of uniparental disomy in the patient and can determine the parental origin of a de novo copy number variation. Moreover, the outcome of a genotype analysis is used as a final quality control by ruling out potential sample mismatches due to non-paternity or sample mix-up. SNP array analysis is now also used in our laboratory for patients with disorders for which locus heterogeneity is known (homozygosity pre-screening), in prenatal diagnosis in case of structural ultrasound anomalies, and for patients with leukemia. In this report, we summarize our array findings and experiences in the various diagnostic applications and demonstrate the power of a SNP-based array platform for molecular karyotyping, because it not only significantly improves the diagnostic yield in both constitutional and cancer genome diagnostics, but it also enhances the quality of the diagnostic laboratory workflow.

Pre-mRNA splicing and retinitis pigmentosa

Retinitis pigmentosa (RP) is a large group of common hereditary eye diseases with highly heterogeneous genetic background. Over forty genes with diverse functionalities are associated with RP and they include a set of ubiquitously expressed genes. These include five genes involved in the precursor messenger RNA( premRNA) splicing. Recent progress in disease gene identification for RP has established the involvement of pre-mRNA splicing as one important mechanism in the disease etiology and has shed light on the splicing process itself, a fundamental biological process. To this date, studies in this field have been focused on two major issues. First, how do the mutations of the adRP associated splicing factors (adRP-SF) affect the splicing function? Second, how do the mutations in these ubiquitously expressed genes lead to specific retinopathy? The two topics fit with the two continuous important steps of the disease pathogenesis. Recently, researchers have made a dramatic progress in the first topic. The identification of the SNRNP200 gene,the fifth adRP-SF and its relevant functional study has shown significance to the progress in the study of RP. Numerous investigations are also being carried out in addressing the second issue.Generation of a variety of models led to a better description of the pathological process of the disease. However, in respect to the key pathogenic mechanism,researchers are still puzzled with a number of confusing questions. In this commentary,the results from the latest investigations were summarized, and in particular,the difficulties in studying the molecular mechanism by which the pre-mRNA splicing deficiency causes RP were detailed. Key words: Retinitis pigmentosa; Pre-mRNA; Spliceosome; U4/U6-U5 tri-small nuclei single nucteotide polymorphism ; SNRNP200 gene

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.

Kinases as Targets for Parkinson's Disease: From Genetics to Therapy

Intense research efforts are currently directed at elucidating the etiology of Parkinson's disease (PD). One approach that has begun to shed light on the PD pathogenic pathways is the identification of disease genes through genetic linkage or association studies. These studies have revealed that several kinases may be involved in PD, as some PD genes encode kinases themselves while other PD genes are found in the same cellular pathways as kinases. Two of these kinases stand out as potential drug targets for novel PD therapy, namely leucine rich repeat kinase 2 (LRRK2) and the alpha-synuclein (α-syn) phosphorylating polo-like kinase 2 (PLK2). Indeed, both α- syn and LRRK2 show genetic linkage as well as genetic association with PD, indicating their relevance to a large number of PD cases. Also, due to the dominant mode of α-syn and LRRK2 inheritance and based on current knowledge of LRRK2 and α-syn phosphorylation by PLK2, inhibition of LRRK2 and PLK2 may constitute a potential therapy for PD. Here we discuss the function of these kinases as well as progress in their validation as drug targets for the treatment of PD.

PPARγ disease gene network and identification of therapeutic targets for prostate cancer

Peroxisome proliferator-activated receptor (PPAR) belongs to the nuclear hormone receptor superfamily. Recently published reports demonstrate the importance of a direct repeat 2 (DR2) as a PPARγ-responsive element in addition to the canonical direct repeat 1 (DR1) Peroxisome proliferator response elements (PPREs). However, a comprehensive and systematic approach to constructing de novo disease-specific gene networks for PPARγ is lacking, especially one that includes PPARγ target genes containing either DR1 or DR2 site within their promoter region. Here, we computationally identified 1154 PPARγ direct target genes and constructed the PPARγ disease gene network, which revealed 138 PPARγ target genes that are associated with 65 unique diseases. The network shows that PPARγ target genes are highly associated with cancer and neurological diseases. Thirty-eight PPARγ direct target genes were found to be involved in prostate cancer and two key (hub) PPARγ direct target genes, PRKCZ and PGK1, were experimentally validated to be repressed upon PPARγ activation by its natural ligand, 15d-PGJ2 in three prostrate cancer cell lines. We proposed that PRKCZ and PGK1 could be novel therapeutic targets for prostate cancer. These investigations would not only aid in understanding the molecular mechanisms by which PPARγ regulates disease targets but would also lead to the identification of novel PPARγ gene targets.

ABCG transporters and disease.

ATP-binding cassette (ABC) transporters form a large family of transmembrane proteins that facilitate the transport of specific substrates across membranes in an ATP-dependent manner. Transported substrates include lipids, lipopolysaccharides, amino acids, peptides, proteins, inorganic ions, sugars and xenobiotics. Despite this broad array of substrates, the physiological substrate of many ABC transporters has remained elusive. ABC transporters are divided into seven subfamilies, A-G, based on sequence similarity and domain organization. Here we review the role of members of the ABCG subfamily in human disease and how the identification of disease genes helped to determine physiological substrates for specific ABC transporters. We focus on the recent discovery of mutations in ABCG2 causing hyperuricemia and gout, which has led to the identification of urate as a physiological substrate for ABCG2.

Massively parallel sequencing and identification of genes for primary lymphoedema: a perfect fit

Primary lymphoedema is a clinically and genetically heterogeneous group of disorders characterized by disruption of the lymphatic system. To date, the majority of the causative genes in primary lymphoedema have been identified through linkage analysis in large families with multiple affected subjects. Studies aimed at isolating additional genes responsible for primary lymphoedema have been hampered by cohorts comprised primarily of sporadic cases and small affected kindreds. In the absence of genetic heterogeneity, recent development of massively parallel DNA sequencing technology, specifically exome sequencing, has provided novel paradigms for disease gene identification in such cohorts. In this review, we summarize the novel approaches to disease gene discovery with massively parallel sequencing also known as Next Generation Sequencing (NGS), and show how the selection of unrelated affected cases from clinically homogenous phenotypic subclassifications is proving to be a successful approach for disease gene discovery in primary lymphoedema.

Exome Sequencing Identifies ZNF644 Mutations in High Myopia

Myopia is the most common ocular disorder worldwide, and high myopia in particular is one of the leading causes of blindness. Genetic factors play a critical role in the development of myopia, especially high myopia. Recently, the exome sequencing approach has been successfully used for the disease gene identification of Mendelian disorders. Here we show a successful application of exome sequencing to identify a gene for an autosomal dominant disorder, and we have identified a gene potentially responsible for high myopia in a monogenic form. We captured exomes of two affected individuals from a Han Chinese family with high myopia and performed sequencing analysis by a second-generation sequencer with a mean coverage of 30× and sufficient depth to call variants at ∼97% of each targeted exome. The shared genetic variants of these two affected individuals in the family being studied were filtered against the 1000 Genomes Project and the dbSNP131 database. A mutation A672G in zinc finger protein 644 isoform 1 (ZNF644) was identified as being related to the phenotype of this family. After we performed sequencing analysis of the exons in the ZNF644 gene in 300 sporadic cases of high myopia, we identified an additional five mutations (I587V, R680G, C699Y, 3′UTR+12 C>G, and 3′UTR+592 G>A) in 11 different patients. All these mutations were absent in 600 normal controls. The ZNF644 gene was expressed in human retinal and retinal pigment epithelium (RPE). Given that ZNF644 is predicted to be a transcription factor that may regulate genes involved in eye development, mutation may cause the axial elongation of eyeball found in high myopia patients. Our results suggest that ZNF644 might be a causal gene for high myopia in a monogenic form.

Identification of genes for bone mineral density variation by computational disease gene identification strategy

We previously used five freely available bioinformatics tools (Prioritizer, Geneseeker, PROSPECTR and SUSPECTS, Disease Gene Prediction, and Endeavour) to analyze the thirteen well-replicated osteoporosis susceptibility loci and identify a subset of most likely candidate osteoporosis susceptibility genes (Huang et al. in J Hum Genet 53:644-655, 2008). In the current study, we experimentally tested the association between bone mineral density (BMD) and the 9 most likely candidate genes [LAMC2(1q25-q31), MATN3(2p24-p23), ITGAV(2q31-q32), ACVR1(2q23-q24), TDGF1(3p21.31), EGF(4q25), IGF1(12q22-q23), ZIC2(13q32), BMP2(20p12)] which were pinpointed by 4 or more bioinformatics tools. Forty tag SNPs in nine candidate genes were genotyped in a southern Chinese female case-control cohort consisting of 1643 subjects. Single- and multi-marker association analyses were performed using logistic regression analysis implemented by PLINK. Potential transcription factor binding sites were predicted by MatInspector. The strongest association was observed between rs10178256 (MATN3) and trochanter (P < 0.001) and total hip BMD (P = 0.002). The SNP rs6214 (IGF1) showed consistent association with BMD at all the four measured skeletal sites (P = 0.005-0.044). Prediction of transcription factor binding suggested that the minor allele G of rs10178256 might abolish the binding of MESP1 and MESP2 which play vital roles in bone homeostasis, whereas the minor allele G of rs6214 might create an additional binding site for XBP1, a constitutive regulator of endoplasmic reticulum stress response. Our data suggested that variants in MATN3 and IGF1 were involved in BMD regulation in southern Chinese women.

Genomic medicine and neurological disease.

"Genomic medicine" refers to the diagnosis, optimized management, and treatment of disease--as well as screening, counseling, and disease gene identification--in the context of information provided by an individual patient's personal genome. Genomic medicine, to some extent synonymous with "personalized medicine," has been made possible by recent advances in genome technologies. Genomic medicine represents a new approach to health care and disease management that attempts to optimize the care of a patient based upon information gleaned from his or her personal genome sequence. In this review, we describe recent progress in genomic medicine as it relates to neurological disease. Many neurological disorders either segregate as Mendelian phenotypes or occur sporadically in association with a new mutation in a single gene. Heritability also contributes to other neurological conditions that appear to exhibit more complex genetics. In addition to discussing current knowledge in this field, we offer suggestions for maximizing the utility of genomic information in clinical practice as the field of genomic medicine unfolds.

Genetic Factors in Isolated and Syndromic Esophageal Atresia

Genetic Factors in Isolated and Syndromic Esophageal Atresia

Differential expression pattern-based prioritization of candidate genes through integrating disease-specific expression data

Expression data can reveal subtle transcriptional changes that mediate the clinical phenotype of the disease resulting from interaction between genetic and environmental factors, which offers us a new perspective to prioritize candidate genes. Here, we proposed a novel differential expression pattern (DEP)-based approach integrating numerous disease-specific expression data sets for prioritizing candidate genes. Using breast cancer as a case study, we validated the efficiency of our approach through integrating 12 breast cancer-related expression data sets based on the leave-one-out cross-validation. Particularly, prioritization based on subtype-specific expression data sets could generate significantly higher performance. The performance could be continually improved with the increasing expression data sets regardless of platform heterogeneity. We further validated the robustness of this approach by application to prostate cancer. Additionally, our approach showed higher performance in comparison with other expression-based approaches and better capability of identification of less well-studied disease genes in comparison with other integration-based approaches.