Identification Of Disease Genes Research Articles

Prioritizing Type 2 Diabetes Genes by Weighted PageRank on Bilayer Heterogeneous Networks.

The prevalence of diabetes mellitus has been increasing rapidly in recent years. Type 2 diabetes makes up about 90 percent cases of diabetes. The interacting mixed effects of genetics and environments build possible interpretable pathogenesis. Thus, finding the causal disease genes is crucial in its clinical diagnosis and medical treatment. Currently, network-based computational method becomes a powerful tool of systematically analyzing complex diseases, such as the identification of candidate disease genes from networks. In this paper, we propose a bioinformatics framework of prioritizing type 2 diabetes genes by leveraging the modified PageRank algorithm on bilayer biomolecular networks consisting an ensemble gene-gene regulatory network and an integrative protein-protein interaction network. We specifically weigh the networks by differential mutual information for measuring the context specificities between genes and between proteins by transcriptomic and proteomic datasets, respectively. After formulating the network into two components of known disease genes and the other normal healthy genes, we rank the diabetes genes and others by bringing the orders in the bilayer network via an improved PageRank algorithm. We conclude that these known disease genes achieve significantly higher ranks compared to these randomly-selected normal genes, and the ranks are robust and consistent in multiple validation scenarios. In functional analysis, these high-ranked genes are identified to perform relevant risks and dysfunctions of type 2 diabetes.

Cancer Diagnosis and Disease Gene Identification via Statistical Machine Learning

Diagnosing cancer and identifying the disease gene by using DNA microarray gene expression data are the hot topics in current bioinformatics. This paper is devoted to the latest development in cancer diagnosis and gene selection via statistical machine learning. A support vector machine is firstly introduced for the binary cancer diagnosis. Then, 1-norm support vector machine, doubly regularized support vector machine, adaptive huberized support vector machine and other extensions are presented to improve the performance of gene selection. Lasso, elastic net, partly adaptive elastic net, group lasso, sparse group lasso, adaptive sparse group lasso and other sparse regression methods are also introduced for performing simultaneous binary cancer classification and gene selection. In addition to introducing three strategies for reducing multiclass to binary, methods of directly considering all classes of data in a learning model (multi_class support vector, sparse multinomial regression, adaptive multinomial regression and so on) are presented for performing multiple cancer diagnosis. Limitations and promising directions are also discussed.

NEW GENES AND DISEASES / NGS & RELATED TECHNIQUES: P.330 An approach to prioritizing novel disease gene candidates and investigating their role in muscle biology and disease

Although nearly 600 genes are now associated with neuromuscular diseases, 50% or more of patients remain undiagnosed following genomic diagnostics. One reason is that more novel disease genes remain to be identified. Next generation sequencing has greatly enhanced identification of novel human disease genes, especially for recessive diseases. However, it remains difficult to find novel disease genes for small dominant families, as hundreds of candidate rare coding variants may be identified. Many of these variants are in genes with unknown function, and thus have no clear association with disease phenotype.

Genetic Variant Reinterpretation: Economic and Population Health Management Challenges.

Genetic Variant Reinterpretation: Economic and Population Health Management Challenges.

An update of pathogenic variants in ASPM, WDR62, CDK5RAP2, STIL, CENPJ, and CEP135 underlying autosomal recessive primary microcephaly in 32 consanguineous families from Pakistan.

BackgroundPrimary microcephaly (MCPH) is a congenital neurodevelopmental disorder manifesting as small brain and intellectual disability. It underlies isolated reduction of the cerebral cortex that is reminiscent of early hominids which makes it suitable model disease to study the hominin‐specific volumetric expansion of brain. Mutations in 25 genes have been reported to cause this disorder. Although majority of these genes were discovered in the Pakistani population, still a significant proportion of these families remains uninvestigated.MethodsWe studied a cohort of 32 MCPH families from different regions of Pakistan. For disease gene identification, genome‐wide linkage analysis, Sanger sequencing, gene panel, and whole‐exome sequencing were performed.ResultsBy employing these techniques individually or in combination, we were able to discern relevant disease‐causing DNA variants. Collectively, 15 novel mutations were observed in five different MCPH genes; ASPM (10), WDR62 (1), CDK5RAP2 (1), STIL (2), and CEP135 (1). In addition, 16 known mutations were also verified. We reviewed the literature and documented the published mutations in six MCPH genes. Intriguingly, our cohort also revealed a recurrent mutation, c.7782_7783delGA;p.(Lys2595Serfs*6), of ASPM reported worldwide. Drawing from this collective data, we propose two founder mutations, ASPM:c.9557C>G;p.(Ser3186*) and CENPJ:c.18delC;p.(Ser7Profs*2), in the Pakistani population.ConclusionsWe discovered novel DNA variants, impairing the function of genes indispensable to build a proper functioning brain. Our study expands the mutational spectra of known MCPH genes and also provides supporting evidence to the pathogenicity of previously reported mutations. These novel DNA variants will be helpful for the clinicians and geneticists for establishing reliable diagnostic strategies for MCPH families.

The morbid cutaneous anatomy of the human genome revealed by a bioinformatic approach

Computational approaches have been developed to prioritize candidate genes in disease gene identification. They are based on different pieces of evidences associating each gene with the given disease. In this study, 648 genes underlying genodermatoses have been compared to 1808 genes involved in other genetic diseases using a bioinformatic approach. These genes were studied at the structural, evolutionary and functional levels. Results show that genes underlying genodermatoses present longer CDS and have more exons. Significant differences were observed in nucleotide motif and amino-acid compositions. Evolutionary conservation analysis revealed that genodermatoses genes have less paralogs, more orthologs in Mouse and Dog and are less conserved. Functional analysis revealed that genodermatosis genes seem to be involved in immune system and skin layers. The Bayesian network model returned a rate of good classification of around 80%. This computational approach could help investigators working in the field of dermatology by prioritizing positional candidate genes for mutation screening.

Significance-based multi-scale method for network community detection and its application in disease-gene prediction.

Community detection in complex networks is an important issue in network science. Several statistical measures have been proposed and widely applied to detecting the communities in various complex networks. However, due to the lack of flexibility resolution, some of them have to encounter the resolution limit and thus are not compatible with multi-scale structures of complex networks. In this paper, we investigated a statistical measure of interest for community detection, Significance [Sci. Rep. 3 (2013) 2930], and analyzed its critical behaviors based on the theoretical derivation of critical number of communities and the phase diagram in community-partition transition. It was revealed that Significance exhibits far higher resolution than the traditional Modularity when the intra- and inter-link densities of communities are obviously different. Following the critical analysis, we developed a multi-resolution version of Significance for identifying communities in the multi-scale networks. Experimental tests in several typical networks have been performed and confirmed that the generalized Significance can be competent for the multi-scale communities detection. Moreover, it can effectively relax the first- and second-type resolution limits. Finally, we displayed an important potential application of the multi-scale Significance in computational biology: disease-gene identification, showing that extracting information from the perspective of multi-scale module mining is helpful for disease gene prediction.

Linked-read genome sequencing identifies biallelic pathogenic variants in DONSON as a novel cause of Meier-Gorlin syndrome

MaterialLinked-read whole genome sequencing (WGS) presents a new opportunity for cost-efficient singleton sequencing in place of traditional trio-based designs while generating informative-phased variants, effective for recessive disorders when parental DNA...

Phenotypic and genotypic characterization of families with complex intellectual disability identified pathogenic genetic variations in known and novel disease genes

Intellectual disability (ID), which presents itself during childhood, belongs to a group of neurodevelopmental disorders (NDDs) that are clinically widely heterogeneous and highly heritable, often being caused by single gene defects. Indeed, NDDs can be attributed to mutations at over 1000 loci, and all type of mutations, ranging from single nucleotide variations (SNVs) to large, complex copy number variations (CNVs), have been reported in patients with ID and other related NDDs. In this study, we recruited seven different recessive NDD families with comorbidities to perform a detailed clinical characterization and a complete genomic analysis that consisted of a combination of high throughput SNP-based genotyping and whole-genome sequencing (WGS). Different disease-associated loci and pathogenic gene mutations were identified in each family, including known (n = 4) and novel (n = 2) mutations in known genes (NAGLU, SLC5A2, POLR3B, VPS13A, SYN1, SPG11), and the identification of a novel disease gene (n = 1; NSL1). Functional analyses were additionally performed in a gene associated with autism-like symptoms and epileptic seizures for further proof of pathogenicity. Lastly, detailed genotype-phenotype correlations were carried out to assist with the diagnosis of prospective families and to determine genomic variation with clinical relevance. We concluded that the combination of linkage analyses and WGS to search for disease genes still remains a fruitful strategy for complex diseases with a variety of mutated genes and heterogeneous phenotypic manifestations, allowing for the identification of novel mutations, genes, and phenotypes, and leading to improvements in both diagnostic strategies and functional characterization of disease mechanisms.

A Protein Interaction Information-based Generative Model for Enhancing Gene Clustering

In the field of computational bioinformatics, identifying a set of genes which are responsible for a particular cellular mechanism, is very much essential for tasks such as medical diagnosis or disease gene identification. Accurately grouping (clustering) the genes is one of the important tasks in understanding the functionalities of the disease genes. In this regard, ensemble clustering becomes a promising approach to combine different clustering solutions to generate almost accurate gene partitioning. Recently, researchers have used generative model as a smart ensemble method to produce the right consensus solution. In the current paper, we develop a protein-protein interaction-based generative model that can efficiently perform a gene clustering. Utilizing protein interaction information as the generative model’s latent variable enables enhance the generative model’s efficiency in inferring final probabilistic labels. The proposed generative model utilizes different weak supervision sources rather utilizing any ground truth information. For weak supervision sources, we use a multi-objective optimization based clustering technique together with the world’s largest gene ontology based knowledge-base named Gene Ontology Consortium(GOC). These weakly supervised labels are supplied to a generative model that eventually assigns all genes to probabilistic labels. The comparative study with respect to silhouette score, Biological Homogeneity Index (BHI) and Biological Stability Index (BSI) proves that the proposed generative model outperforms than other state-of-the-art techniques.

Genetic diagnoses in epilepsy: The impact of dynamic exome analysis in a pediatric cohort.

We evaluated the yield of systematic analysis and/or reanalysis of whole exome sequencing (WES) data from a cohort of well-phenotyped pediatric patients with epilepsy and suspected but previously undetermined genetic etiology. We identified and phenotyped 125 participants with pediatric epilepsy. Etiology was unexplained at the time of enrollment despite clinical testing, which included chromosomal microarray (57 patients), epilepsy gene panel (n = 48), both (n = 28), or WES (n = 8). Clinical epilepsy diagnoses included developmental and epileptic encephalopathy (DEE), febrile infection-related epilepsy syndrome, Rasmussen encephalitis, and other focal and generalized epilepsies. We analyzed WES data and compared the yield in participants with and without prior clinical genetic testing. Overall, we identified pathogenic or likely pathogenic variants in 40% (50/125) of our study participants. Nine patients with DEE had genetic variants in recently published genes that had not been recognized as epilepsy-related at the time of clinical testing (FGF12, GABBR1, GABBR2, ITPA, KAT6A, PTPN23, RHOBTB2, SATB2), and eight patients had genetic variants in candidate epilepsy genes (CAMTA1, FAT3, GABRA6, HUWE1, PTCHD1). Ninety participants had concomitant or subsequent clinical genetic testing, which was ultimately explanatory for 26% (23/90). Of the 67 participants whose molecular diagnoses were "unsolved" through clinical genetic testing, we identified pathogenic or likely pathogenic variants in 17 (25%). Our data argue for early consideration of WES with iterative reanalysis for patients with epilepsy, particularly those with DEE or epilepsy with intellectual disability. Rigorous analysis of WES data of well-phenotyped patients with epilepsy leads to a broader understanding of gene-specific phenotypic spectra as well as candidate disease gene identification. We illustrate the dynamic nature of genetic diagnosis over time, with analysis and in some cases reanalysis of exome data leading to the identification of disease-associated variants among participants with previously nondiagnostic results from a variety of clinical testing strategies.

Highly efficient induced pluripotent stem cell reprogramming of cryopreserved lymphoblastoid cell lines.

Tissue culture based in-vitro experimental modeling of human inherited disorders provides insight into the cellular and molecular mechanisms involved and the underlying genetic component influencing the disease phenotype. The breakthrough development of induced pluripotent stem cell (iPSC) technology represents a quantum leap in experimental modeling of human diseases, providing investigators with a self-renewing and thus unlimited source of pluripotent cells for targeted differentiation into functionally relevant disease specific tissue/cell types. The existing rich bio-resource of Epstein-Barr virus (EBV) immortalized lymphoblastoid cell line (LCL) repositories generated from a wide array of patients in genetic and epidemiological studies worldwide, many of them with extensive genotypic, genomic and phenotypic data already existing, provides a great opportunity to reprogram iPSCs from any of these LCL donors in the context of their own genetic identity for disease modeling and disease gene identification. However, due to the low reprogramming efficiency and poor success rate of LCL to iPSC reprogramming, these LCL resources remain severely underused for this purpose. Here, we detailed step-by-step instructions to perform our highly efficient LCL-to-iPSC reprogramming protocol using EBNA1/OriP episomal plasmids encoding pluripotency transcription factors (i.e., OCT3/4, SOX2, KLF4, L-MYC, and LIN28), mouse p53DD (p53 carboxy-terminal dominant-negative fragment) and commercially available reprogramming media. We achieved a consistently high reprogramming efficiency and 100% success rate (> 200 reprogrammed iPSC lines) using this protocol.

A novel one-class classification approach to accurately predict disease-gene association in acute myeloid leukemia cancer.

Disease causing gene identification is considered as an important step towards drug design and drug discovery. In disease gene identification and classification, the main aim is to identify disease genes while identifying non-disease genes are of less or no significant. Hence, this task can be defined as a one-class classification problem. Existing machine learning methods typically take into consideration known disease genes as positive training set and unknown genes as negative samples to build a binary-class classification model. Here we propose a new One-class Classification Support Vector Machines (OCSVM) method to precisely classify candidate disease genes. Our aim is to build a model that concentrate its focus on detecting known disease-causing gene to increase sensitivity and precision. We investigate the impact of our proposed model using a benchmark consisting of the gene expression dataset for Acute Myeloid Leukemia (AML) cancer. Compared with the traditional methods, our experimental result shows the superiority of our proposed method in terms of precision, recall, and F-measure to detect disease causing genes for AML. OCSVM codes and our extracted AML benchmark are publicly available at: https://github.com/imandehzangi/OCSVM.

Inborn errors of metabolism leading to neuronal migration defects.

The development and organisation of the human brain start in the embryonic stage and is a highly complex orchestrated process. It depends on series of cellular mechanisms that are precisely regulated by multiple proteins, signalling pathways and non-protein-coding genes. A crucial process during cerebral cortex development is the migration of nascent neuronal cells to their appropriate positions and their associated differentiation into layer-specific neurons. Neuronal migration defects (NMD) comprise a heterogeneous group of neurodevelopmental disorders including monogenetic disorders and residual syndromes due to damaging factors during prenatal development like infections, maternal diabetes mellitus or phenylketonuria, trauma, and drug use. Multifactorial causes are also possible. Classification into lissencephaly, polymicrogyria, schizencephaly, and neuronal heterotopia is based on the visible morphologic cortex anomalies. Characteristic clinical features of NMDs are severe psychomotor developmental delay, severe intellectual disability, intractable epilepsy, and dysmorphisms. Neurometabolic disorders only form a small subgroup within the large group of NMDs. The prototypes are peroxisomal biogenesis disorders, peroxisomal ß-oxidation defects and congenital disorders of O-glycosylation. The rapid evolution of biotechnology has resulted in an ongoing identification of metabolic and non-metabolic disease genes for NMDs. Nevertheless, we are far away from understanding the specific role of cortical genes and metabolites on spatial and temporal regulation of human cortex development and associated malformations. This limited understanding of the pathogenesis hinders the attempt for therapeutic approaches. In this article, we provide an overview of the most important cortical malformations and potential underlying neurometabolic disorders.

Genomics and molecular mechanisms in glaucoma and anterior segment abnormalities

AbstractOcular anterior segment disorders have a significant impact on vision and may lead to glaucoma in over 50% of cases. They occur due to a complex interplay of developmental and genetic factors, and there are often marked phenotypic overlaps and genetic heterogeneity. Disorders of the anterior segment of the eye encompass a variety of clinical presentations including aniridia, Axenfeld and Rieger anomalies, primary congenital glaucoma, Peters anomaly, and there are a number of syndromal associations. Several genotype‐phenotype correlations are present, and genomic approaches have facilitated broadening of known phenotypes and led to identification of new anterior segment disease genes and pathways. Key disease genes include FOXC1, PITX2, CYP1B1, FOXE3, and PAX6, and there are a number of newly identified and less frequently reported disease genes including GJA8, CPAMD8, PXDN and COL4A1. Whole exome and whole genome sequencing approaches facilitate genetic diagnosis in these genetically heterogeneous disorders. These approaches, in combination with cellular and animal model studies, assist in elucidation of novel disease genes and molecular mechanisms contributing to glaucoma and anterior segment abnormalities.

Impact on Clinical Decision Making of Next-Generation Sequencing in Pediatric Epilepsy in a Tertiary Epilepsy Referral Center.

Background. Next-generation sequencing (NGS) describes new powerful techniques of nucleic acid analysis, which allow not only disease gene identification diagnostics but also applications for transcriptome/methylation analysis and meta-genomics. NGS helps identify many monogenic epilepsy syndromes. Pediatric epilepsy patients can be tested using NGS epilepsy panels to diagnose them, thereby influencing treatment choices. The primary objective of this study was to evaluate the impact of genetic testing on clinical decision making in pediatric epilepsy patients. Methods. We completed a single-center retrospective cohort study of 91 patients (43 male) aged 19 years or less undergoing NGS with epilepsy panels differing in size ranging from 5 to 434 genes from October 2013 to September 2017. Results. During a mean time of 3.6 years between symptom onset and genetic testing, subjects most frequently showed epileptic encephalopathy (40%), focal epilepsy (33%), and generalized epilepsy (18%). In 16 patients (18% of the study population), "pathogenic" or "likely pathogenic" results according to ACMG criteria were found. Ten of the 16 patients (63%) experienced changes in clinical management regarding their medication and avoidance of further diagnostic evaluation, that is, presurgical evaluation. Conclusion. NGS epilepsy panels contribute to the diagnosis of pediatric epilepsy patients and may change their clinical management with regard to both preventing unnecessary and potentially harmful diagnostic procedures and management. Thus, the present data support the early implementation in order to adopt clinical management in selected cases and prevent further invasive investigations. Given the relatively small sample size and heterogeneous panels a larger prospective study with more homogeneous panels would be helpful to further determine the impact of NGS on clinical decision making.

Benchmarking network propagation methods for disease gene identification.

In-silico identification of potential target genes for disease is an essential aspect of drug target discovery. Recent studies suggest that successful targets can be found through by leveraging genetic, genomic and protein interaction information. Here, we systematically tested the ability of 12 varied algorithms, based on network propagation, to identify genes that have been targeted by any drug, on gene-disease data from 22 common non-cancerous diseases in OpenTargets. We considered two biological networks, six performance metrics and compared two types of input gene-disease association scores. The impact of the design factors in performance was quantified through additive explanatory models. Standard cross-validation led to over-optimistic performance estimates due to the presence of protein complexes. In order to obtain realistic estimates, we introduced two novel protein complex-aware cross-validation schemes. When seeding biological networks with known drug targets, machine learning and diffusion-based methods found around 2-4 true targets within the top 20 suggestions. Seeding the networks with genes associated to disease by genetics decreased performance below 1 true hit on average. The use of a larger network, although noisier, improved overall performance. We conclude that diffusion-based prioritisers and machine learning applied to diffusion-based features are suited for drug discovery in practice and improve over simpler neighbour-voting methods. We also demonstrate the large impact of choosing an adequate validation strategy and the definition of seed disease genes.

Identification of candidate disease genes in patients with common variable immunodeficiency

BackgroundCommon variable immunodeficiency (CVID), the most prevalent form of primary immunodeficiency (PID), is characterized by hypogammaglobulinemia and recurrent infections. Understanding protein‐protein interaction (PPI) networks of CVID genes and identifying candidate CVID genes are critical steps in facilitating the early diagnosis of CVID. Here, the aim was to investigate PPI networks of CVID genes and identify candidate CVID genes using computation techniques.MethodsNetwork density and biological distance were used to study PPI data for CVID and PID genes obtained from the STRING database. Gene expression data of patients with CVID were obtained from the Gene Expression Omnibus, and then Pearson’s correlation coefficient, a PPI database, and Kyoto Encyclopedia of Genes and Genomes were used to identify candidate CVID genes. We then evaluated our predictions and identified differentially expressed CVID genes.ResultsThe majority of CVID genes are characterized by a high network density and small biological distance, whereas most PID genes are characterized by a low network density and large biological distance, indicating that CVID genes are more functionally similar to each other and closely interact with one other compared with PID genes. Subsequently, we identified 172 CVID candidate genes that have similar biological functions to known CVID genes, and eight genes were recently reported as CVID‐related genes. MYC, a candidate gene, was down‐regulated in CVID duodenal biopsies, but up‐regulated in blood samples compared with levels in healthy controls.ConclusionOur findings will aid in a better understanding of the complex of CVID genes, possibly further facilitating the early diagnosis of CVID.

Trim71 Links an Ancient MicroRNA Pathway to Neural Stem Cell Development and Human Congenital Hydrocephalus

Abstract INTRODUCTION Congenital hydrocephalus (CH), the most common brain malformation that affects 1:1000 live births, remains poorly treated owing to incomplete understanding of disease pathogenesis. Identification of novel CH disease genes is a powerful approach to understanding the molecular genetics of CH. Whole exome sequencing in humans has identified TRIM71 as a bona fide CH disease gene. Trim71 is the major target of the ancient let-7 pathway that famously regulates cell differentiation and organismal development via microRNA-mediated gene silencing in Caenorhabditis elegans. Previous works have implicated Trim71 in the regulation of embryonic neural stem cell (NSC) fate, yet its cellular and molecular function in NSCs remain poorly understood. METHODS To gain a better understanding of Trim71 as a paradigm of NSC involvement in CH, we have generated a mouse model that harbors the point mutation in Trim71 that results in human CH (R595H) using CRISPR-Cas9. RESULTS We found that a subset of mice heterozygous for the Trim71 R595H mutant allele developed severe communicating hydrocephalus with accompanying macrocephaly by 4 wk of age. CONCLUSION Investigation into the function of Trim71 and its CH-causing mutations using our novel mouse models presents outstanding opportunities to increase understanding of human NSC regulation, brain development, and CH pathogenesis.

Comprehensive evaluation of a prospective Australian patient cohort with suspected genetic kidney disease undergoing clinical genomic testing: a study protocol

IntroductionRecent advances in genomic technology have allowed better delineation of renal conditions, the identification of new kidney disease genes and subsequent targets for therapy. To date, however, the utility of...