Candidate Disease Genes Research Articles

Game theoretic centrality: a novel approach to prioritize disease candidate genes by combining biological networks with the Shapley value

BackgroundComplex human health conditions with etiological heterogeneity like Autism Spectrum Disorder (ASD) often pose a challenge for traditional genome-wide association study approaches in defining a clear genotype to phenotype model. Coalitional game theory (CGT) is an exciting method that can consider the combinatorial effect of groups of variants working in concert to produce a phenotype. CGT has been applied to associate likely-gene-disrupting variants encoded from whole genome sequence data to ASD; however, this previous approach cannot take into account for prior biological knowledge. Here we extend CGT to incorporate a priori knowledge from biological networks through a game theoretic centrality measure based on Shapley value to rank genes by their relevance–the individual gene’s synergistic influence in a gene-to-gene interaction network. Game theoretic centrality extends the notion of Shapley value to the evaluation of a gene’s contribution to the overall connectivity of its corresponding node in a biological network.ResultsWe implemented and applied game theoretic centrality to rank genes on whole genomes from 756 multiplex autism families. Top ranking genes with the highest game theoretic centrality in both the weighted and unweighted approaches were enriched for pathways previously associated with autism, including pathways of the immune system. Four of the selected genes HLA-A, HLA-B, HLA-G, and HLA-DRB1–have also been implicated in ASD and further support the link between ASD and the human leukocyte antigen complex.ConclusionsGame theoretic centrality can prioritize influential, disease-associated genes within biological networks, and assist in the decoding of polygenic associations to complex disorders like autism.

An integrated approach to uncover quality markers of Traditional Chinese medicine underlying chemical profiling, network target selection and metabolomics approach: Guan-Xin-Jing capsule as a model

The “multi-component and multi-target” property of traditional Chinese medicine (TCM) makes the quality control more difficult, which remains a key concern hindering the space of progress in its modernization and globalization. This study aims to provide a new strategy to discover quality markers related to the efficacy and stability of TCM based on the bioactive markers finding and differential markers finding, Guan-Xin-Jing capsule (GXJC) was used as a model. Firstly, the UHPLC-QTOF-MS/MS was performed to identify the chemical profiling of GXJC and 148 compounds were unequivocally or tentatively identified. Subsequently, the compounds with relatively low intensity, low oral bioavailability and unsuited drug likeness were removed. In addition, a network based approach by integrating candidate compound genes and disease genes was applied to further filter out the potential active compounds against coronary heart disease (CHD) and finally 7 compounds were identified as the potential bioactive markers. Moreover, to ensure the batch-to-batch consistency of GXJC, an untargeted UHPLC-QTOF-MS/MS based metabolomics approach and multivariate statistical analysis was performed and 5 compounds were identified as the potential differential markers to illustrate the discrepancy among 27 batches of GXJC. Using these strategies above, a total of 11 chemical compounds in GXJC were screened out as quality markers(Q-markers). The information generated with our strategy should be more helpful to guide the selection of the Q-markers and lay the foundation for quality control of TCM.

PrGeFNE: Predicting disease-related genes by fast network embedding.

Identifying disease-related genes is of importance for understanding of molecule mechanisms of diseases, as well as diagnosis and treatment of diseases. Many computational methods have been proposed to predict disease-related genes, but how to make full use of multi-source biological data to enhance the ability of disease-gene prediction is still challenging. In this paper, we proposed a novel method for predicting disease-related genes by using fast network embedding (PrGeFNE), which can integrate multiple types of associations related to diseases and genes. Specifically, we first constructed a heterogeneous network by using phenotype-disease, disease-gene, protein-protein and gene-GO associations; and low-dimensional representation of nodes is extracted from the network by using a fast network embedding algorithm. Then, a dual-layer heterogeneous network was reconstructed by using the low-dimensional representation, and a network propagation was applied to the dual-layer heterogeneous network to predict disease-related genes. Through cross-validation and newly added-association validation, we displayed the important roles of different types of association data in enhancing the ability of disease-gene prediction, and confirmed the excellent performance of PrGeFNE by comparing to state-of-the-art algorithms. Furthermore, we developed a web tool that can facilitate researchers to search for candidate genes of different diseases predicted by PrGeFNE, along with the enrichment analysis of GO and pathway on candidate gene set. This may be useful for investigation of diseases' molecular mechanisms as well as their experimental validations. The web tool is available at http://bioinformatics.csu.edu.cn/prgefne/.

Quantitative analysis of proteins which are members of the same protein complex but cause locus heterogeneity in disease

It is still largely unknown how mutations in different genes cause similar diseases – a condition known as locus heterogeneity. A likely explanation is that the different proteins encoded by the locus heterogeneity genes participate in the same biological function and, specifically, that they belong to the same protein complex. Here we report that, in up to 30% of the instances of locus heterogeneity, the disease-causing proteins are indeed members of the same protein complex. Moreover, we observed that, in many instances, the diseases and protein complexes only partially intersect. Among the possible explanations, we surmised that some genes that encode proteins in the complex have not yet been reported as causing disease and are therefore candidate disease genes. Mutations of known human disease genes and murine orthologs of candidate disease genes that encode proteins in the same protein complex do in fact often cause similar phenotypes in humans and mice. Furthermore, we found that the disease-complex intersection is not only incomplete but also non-univocal, with many examples of one disease intersecting more than one protein complex or one protein complex intersecting more than one disease. These limits notwithstanding, this study shows that action on proteins in the same complex is a widespread pathogenic mechanism underlying numerous instances of locus heterogeneity.

Genetic factors underlying the bidirectional relationship between autoimmune and mental disorders – Findings from a Danish population-based study

BackgroundPrevious studies have indicated the bidirectionality between autoimmune and mental disorders. However, genetic studies underpinning the co-occurrence of the two disorders have been lacking. In this study, we examined the potential genetic contribution to the association between autoimmune and mental disorders and investigated the genetic basis of overall autoimmune disease. MethodsWe used diagnostic information from patients with seven autoimmune diseases and six mental disorders from the Danish population-based case-cohort sample (iPSYCH2012). We explored the epidemiological association using survival analysis and modelled the effect of polygenic risk scores (PRSs) on autoimmune and mental diseases. Genetic factors were investigated using GWAS and imputed HLA alleles in the iPSYCH cohort. ResultsOf 64,039 individuals, a total of 43,902 (68.6%) were diagnosed with mental disorders and 1383 (2.2%) with autoimmune diseases. There was a significant comorbidity between the two disease classes (P = 2.67 × 10−7, OR = 1.38, 95%CI = 1.22–1.56), with an overall bidirectional association, wherein individuals with autoimmune diseases had an increased risk of subsequent mental disorders (HR = 1.13, 95%CI: 1.07–1.21, P = 7.95 × 10−5) and vice versa (HR = 1.27, 95%CI = 1.16–1.39, P = 8.77 × 10−15). Adding PRSs to these adjustment models did not have an impact on the associations. PRSs for autoimmune diseases were only slightly associated with increased risk of mental disorders (HR = 1.01, 95%CI: 1.00–1.02, p = 0.038), whereas PRSs for mental disorders were not associated with autoimmune diseases overall. Our GWAS highlighted 12 loci on chromosome 6 (minimum P = 2.74 × 10−36, OR = 1.80, 95% CI: 1.64–1.96), which were implicated in gene regulation through bioinformatic functional analyses, thereby identifying new candidate genes for overall autoimmune disease. Moreover, we observed 20 human leukocyte antigen (HLA) alleles strongly associated, either positively or negatively, with overall autoimmune disease, but we did not find significant evidence of their associations with overall mental disorders. A GWAS of a comorbid diagnosis of an autoimmune disease and a mental disorder identified a genome-wide significant locus on chromosome 7 as well (P = 1.43 × 10−8, OR = 10.65, 95%CI = 3.21–35.36). ConclusionsOur findings confirm the overall comorbidity and bidirectionality between autoimmune diseases and mental disorders and identify HLA genes which are significantly associated with overall autoimmune disease. Additionally, we identified several new candidate genes for overall autoimmune disease and ranked them based on their association with the investigated diseases.

In silico prediction of human genes as potential targets for rice miRNAs

BackgroundExogenous microRNAs (miRNAs) enter the human body through food, and their effects on metabolic processes can be considerable. It is important to determine which miRNAs from plants affect the expression of human genes and the extent of their influence. MethodThe binding sites of 738Oryza sativa miRNAs (osa-miRNAs) that interact with 17 508 mRNAs of human genes were determined using the MirTarget program. ResultThe characteristics of the binding of 46 single osa-miRNAs to 86 mRNAs of human genes with a value of free energy (ΔG) interaction equal 94%–100% from maximum ΔG were established. The findings showed that osa-miR2102-5p, osa-miR5075-3p, osa-miR2097-5p, osa-miR2919 targeted the largest number of genes at 38, 36, 23, 19 sites, respectively. mRNAs of 86 human genes were identified as targets for 93 osa-miRNAs of all family osa-miRNAs with ΔG values equal 94%–98% from maximum ΔG. Each miRNA of the osa-miR156-5p, osa-miR164-5p, osa-miR168-5p, osa-miR395-3p, osa-miR396-3p, osa-miR396-5p, osa-miR444-3p, osa-miR529-3p, osa-miR1846-3p, osa-miR2907-3p families had binding sites in mRNAs of several human target genes. The binding sites of osa-miRNAs in mRNAs of the target genes for each family of osa-miRNAs were conserved when compared to flanking nucleotide sequences. ConclusionTarget mRNA human genes of osa-miRNAs are also candidate genes of cancer, cardiovascular and neurodegenerative diseases.

Exome sequencing in 38 patients with intracranial aneurysms and subarachnoid hemorrhage

ObjectiveGenetic risk factors for unruptured intracranial aneurysms (UIA) and aneurysmal subarachnoid hemorrhage (aSAH) are poorly understood. We aimed to verify recently reported risk genes and to identify novel sequence variants involved in the etiology of UIA/aSAH.MethodsWe performed exome sequencing (ES) in 35 unrelated individuals and 3 family members, each with a history of UIA and/or aSAH. We searched for sequence variants with minor allele frequency (MAF) ≤ 5% in the reported risk genes ADAMTS15, ANGPTL6, ARHGEF17, LOXL2, PCNT, RNF213, THSD1 and TMEM132B. To identify novel putative risk genes we looked for unknown (MAF = 0) variants shared by the three relatives.ResultsWe identified 20 variants with MAF ≤ 5% in 18 individuals: 9 variants in PCNT (9 patients), 4 in RNF213 (3 patients), 3 in THSD1 (6 patients), 2 in ANGPTL6 (3 patients), 1 in ADAMTS15 (1 patient) and 1 in TMEM132B (1 patient). In the affected family, prioritization of shared sequence variants yielded five novel putative risk genes. Based on predicted pathogenicity of identified variants, population genetics data and a high functional relevance for vascular biology, EDIL3 was selected as top candidate and screened in additional 37 individuals with UIA and/or aSAH: a further very rare EDIL3 sequence variant in two unrelated sporadic patients was identified.ConclusionsOur data support a role of sequence variants in PCNT, RNF213 and THSD1 as susceptibility factors for cerebrovascular disease. The documented function in vascular wall integrity, the crucial localization of affected amino acids and gene/variant association tests suggest EDIL3 as a further valid candidate disease gene for UIA/aSAH.

Breakpoint Mapping of Symptomatic Balanced Translocations Links the EPHA6, KLF13 and UBR3 Genes to Novel Disease Phenotype.

De novo balanced chromosomal aberrations (BCAs), such as reciprocal translocations and inversions, are genomic aberrations that, in approximately 25% of cases, affect the human phenotype. Delineation of the exact structure of BCAs may provide a precise diagnosis and/or point to new disease loci. We report on six patients with de novo balanced chromosomal translocations (BCTs) and one patient with a de novo inversion, in whom we mapped breakpoints to a resolution of 1 bp, using shallow whole-genome mate pair sequencing. In all seven cases, a disruption of at least one gene was found. In two patients, the phenotypic impact of the disrupted genes is well known (NFIA, ATP7A). In five patients, the aberration damaged genes: PARD3, EPHA6, KLF13, STK24, UBR3, MLLT10 and TLE3, whose influence on the human phenotype is poorly understood. In particular, our results suggest novel candidate genes for retinal degeneration with anophthalmia (EPHA6), developmental delay with speech impairment (KLF13), and developmental delay with brain dysembryoplastic neuroepithelial tumor (UBR3). In conclusion, identification of the exact structure of symptomatic BCTs using next generation sequencing is a viable method for both diagnosis and finding novel disease candidate genes in humans.

Breast Cancer Candidate Gene Detection Through Integration of Subcellular Localization Data With Protein-Protein Interaction Networks.

Due to technological advances the quality and availability of biological data has increased dramatically in the last decade. Analysing protein-protein interaction networks (PPINs) in an integrated way, together with subcellular compartment data, provides such biological context, helps to fill in the gaps between a single type of biological data and genes causing diseases and can identify novel genes related to disease. In this study, we present BCCGD, a method for integrating subcellular localization data with PPINs that detects breast cancer candidate genes in protein complexes. We achieve this by defining the significance of the compartment, constructing edge-weighted PPINs, finding protein complexes with a non-negative matrix factorization approach, generating disease-specific networks based on the known disease genes, prioritizing disease candidate genes with a WDC method. As a case study, we investigate the breast cancer but the techniques described here are applicable to other disorders. For the top genes scored by BCCGD approach, we utilize the literature retrieving method to test the correlations of them with the breast cancer. The results show that BCCGD discover some novel breast cancer candidate genes which are valuable references for the biomedical scientists.

Biallelic GRM7 variants cause epilepsy, microcephaly, and cerebral atrophy.

ObjectiveDefects in ion channels and neurotransmitter receptors are implicated in developmental and epileptic encephalopathy (DEE). Metabotropic glutamate receptor 7 (mGluR7), encoded by GRM7, is a presynaptic G‐protein‐coupled glutamate receptor critical for synaptic transmission. We previously proposed GRM7 as a candidate disease gene in two families with neurodevelopmental disorders (NDDs). One additional family has been published since. Here, we describe three additional families with GRM7 biallelic variants and deeply characterize the associated clinical neurological and electrophysiological phenotype and molecular data in 11 affected individuals from six unrelated families.MethodsExome sequencing and family‐based rare variant analyses on a cohort of 220 consanguineous families with NDDs revealed three families with GRM7 biallelic variants; three additional families were identified through literature search and collaboration with a clinical molecular laboratory.ResultsWe compared the observed clinical features and variants of 11 affected individuals from the six unrelated families. Identified novel deleterious variants included two homozygous missense variants (c.2671G>A:p.Glu891Lys and c.1973G>A:p.Arg685Gln) and one homozygous stop‐gain variant (c.1975C>T:p.Arg659Ter). Developmental delay, neonatal‐ or infantile‐onset epilepsy, and microcephaly were universal. Three individuals had hypothalamic–pituitary–axis dysfunction without pituitary structural abnormality. Neuroimaging showed cerebral atrophy and hypomyelination in a majority of cases. Two siblings demonstrated progressive loss of myelination by 2 years in both and an acquired microcephaly pattern in one. Five individuals died in early or late childhood.ConclusionDetailed clinical characterization of 11 individuals from six unrelated families demonstrates that rare biallelic GRM7 pathogenic variants can cause DEEs, microcephaly, hypomyelination, and cerebral atrophy.

Bayesian integrative analysis of epigenomic and transcriptomic data identifies Alzheimer's disease candidate genes and networks.

Biomedical research studies have generated large multi-omic datasets to study complex diseases like Alzheimer’s disease (AD). An important aim of these studies is the identification of candidate genes that demonstrate congruent disease-related alterations across the different data types measured by the study. We developed a new method to detect such candidate genes in large multi-omic case-control studies that measure multiple data types in the same set of samples. The method is based on a gene-centric integrative coefficient quantifying to what degree consistent differences are observed in the different data types. For statistical inference, a Bayesian hierarchical model is used to study the distribution of the integrative coefficient. The model employs a conditional autoregressive prior to integrate a functional gene network and to share information between genes known to be functionally related. We applied the method to an AD dataset consisting of histone acetylation, DNA methylation, and RNA transcription data from human cortical tissue samples of 233 subjects, and we detected 816 genes with consistent differences between persons with AD and controls. The findings were validated in protein data and in RNA transcription data from two independent AD studies. Finally, we found three subnetworks of jointly dysregulated genes within the functional gene network which capture three distinct biological processes: myeloid cell differentiation, protein phosphorylation and synaptic signaling. Further investigation of the myeloid network indicated an upregulation of this network in early stages of AD prior to accumulation of hyperphosphorylated tau and suggested that increased CSF1 transcription in astrocytes may contribute to microglial activation in AD. Thus, we developed a method that integrates multiple data types and external knowledge of gene function to detect candidate genes, applied the method to an AD dataset, and identified several disease-related genes and processes demonstrating the usefulness of the integrative approach.

Genetic compensation in a stable slc25a46 mutant zebrafish: A case for using F0 CRISPR mutagenesis to study phenotypes caused by inherited disease.

A phenomenon of genetic compensation is commonly observed when an organism with a disease-bearing mutation shows incomplete penetrance of the disease phenotype. Such incomplete phenotypic penetrance, or genetic compensation, is more commonly found in stable knockout models, rather than transient knockdown models. As such, these incidents present a challenge for the disease modeling field, although a deeper understanding of genetic compensation may also hold the key for novel therapeutic interventions. In our study we created a knockout model of slc25a46 gene, which is a recently discovered important player in mitochondrial dynamics, and deleterious mutations in which are known to cause peripheral neuropathy, optic atrophy and cerebellar ataxia. We report a case of genetic compensation in a stable slc25a46 homozygous zebrafish mutant (hereafter referred as “mutant”), in contrast to a penetrant disease phenotype in the first generation (F0) slc25a46 mosaic mutant (hereafter referred as “crispant”), generated with CRISPR/Cas-9 technology. We show that the crispant phenotype is specific and rescuable. By performing mRNA sequencing, we define significant changes in slc25a46 mutant’s gene expression profile, which are largely absent in crispants. We find that among the most significantly altered mRNAs, anxa6 gene stands out as a functionally relevant player in mitochondrial dynamics. We also find that our genetic compensation case does not arise from mechanisms driven by mutant mRNA decay. Our study contributes to the growing evidence of the genetic compensation phenomenon and presents novel insights about Slc25a46 function. Furthermore, our study provides the evidence for the efficiency of F0 CRISPR screens for disease candidate genes, which may be used to advance the field of functional genetics.

Genetic basis of neurodevelopmental disorders in 103 Jordanian families.

We recruited 103 families from Jordan with neurodevelopmental disorders (NDD) and patterns of inheritance mostly suggestive of autosomal recessive inheritance. In each family, we investigated at least one affected individual using exome sequencing and an in-house diagnostic variant interpretation pipeline including a search for copy number variation. This approach led us to identify the likely molecular defect in established disease genes in 37 families. We could identify 25 pathogenic nonsense and 11 missense variants as well as 3 pathogenic copy number variants and 1 repeat expansion. Notably, 11 of the disease-causal variants occurred de novo. In addition, we prioritized a homozygous frameshift variant in PUS3 in two sisters with intellectual disability. To our knowledge, PUS3 has been postulated only recently as a candidate disease gene for intellectual disability in a single family with three affected siblings. Our findings provide additional evidence to establish loss of PUS3 function as a cause of intellectual disability.

Cigarette smoke exposure enhances transforming acidic coiled-coil-containing protein 2 turnover and thereby promotes emphysema.

Our integrative genomic and functional analysis identified transforming acidic coiled-coil-containing protein 2 (TACC2) as a chronic obstructive pulmonary disease (COPD) candidate gene. Here, we found that smokers with COPD exhibit a marked decrease in lung TACC2 protein levels relative to smokers without COPD. Single cell RNA sequencing reveals that TACC2 is expressed primarily in lung epithelial cells in normal human lungs. Furthermore, suppression of TACC2 expression impairs the efficiency of homologous recombination repair and augments spontaneous and cigarette smoke extract-induced (CSE-induced) DNA damage and cytotoxicity in immortalized human bronchial epithelial cells. By contrast, enforced expression of TACC2 attenuates the CSE effects. We also found that CSE enhances TACC2 degradation via the ubiquitin-proteasome system mediated by the ubiquitin E3 ligase subunit, F box L7. Furthermore, cellularly expressed TACC2 proteins harboring naturally occurring mutations exhibited altered protein lifespan coupled with modified DNA damage repair and cytotoxic responses. CS triggers emphysematous changes accompanied by accumulated DNA damage, apoptosis of alveolar epithelia, and lung inflammation in Tacc2-/- compared with Tacc2+/+ mice. Our results suggest that CS destabilizes TACC2 protein in lung epithelia by the ubiquitin proteasome system, leading to subsequent DNA damage, cytotoxicity, and emphysema.

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.

Pioneering Methods for Enhancing PPI and Phenotype Networks for Candidate Disease Prioritization

The physical contacts of high-specificity between two or more protein molecules constitute Protein-Protein Interactions (PPIs). PPI networks are modeled through graphs where node denotes proteins and edges denote interaction between proteins. The PPI network plays an important role to identify the interesting disease gene candidates. But, the PPI network usually contains false interactions. Many techniques have been proposed to reconstruct PPI network to remove false interactions and improve ranking of candidate disease. Random Walk with Restart on Diffusion profile (RWRDP) and Random Walk on a Reliable Heterogeneous Network (RWRHN) was two among them. In these methods, Gene topological similarity was incorporated with original PPI network to reconstruct new PPI network. Phenotype network was constructed by calculating similarity between gene phenotypes. The reconstructed network and phenotype networks were combined to rank candidate disease genes. However, the PPI reconstruction was fully related with the quality of protein interaction data. In order to enhance the reconstruction of PPI, a Piecewise Linear Regression (PLR) based protein sequence similarity measure and Bat Algorithm based gene expression similarity were proposed with RHN. In this paper, additional measure called Interaction Level Sub cellular Localization Score (ILSLS) is proposed to further reduce the false interaction in the reconstruction of PPI network. ILSLS is the combination of Normalized Sub cellular Localization score (NSL) and Protein Multiple Location Prediction score (PMLP). The proposed work is named as Random Walker on Optimized Trustworthy Heterogeneous Sub Cellular localization aware Network (RW-OTHSN). In order to enhance the ranking of RW-OTHSN, phenotype structure is considered while construction phenotype network to rank the candidate disease genes. The phenotype structure is characterized based on h*-sequence model which identify highly discriminative signatures with only a small number of genes. This proposed work is named as Random Walker on Optimized Trustworthy Heterogeneous Sub Cellular localization and Phenotype structure aware Network (RW-OTHSPN). The efficiency of the proposed methods are evaluated on PPI network database in terms of Average degree, Relative Frequency for PPI reconstruction, Number of successful predictions, precision and recall for candidate disease gene ranking.

GeVIR is a continuous gene-level metric that uses variant distribution patterns to prioritize disease candidate genes.

With large-scale population sequencing projects gathering pace, there is a need for strategies that advance disease gene prioritization1,2. Metrics that provide information about a gene and its ability to tolerate protein-altering variation can aid in clinical interpretation of human genomes and can advance disease gene discovery1-4. Previous reported methods analyzed the total variant load in a gene1-4, but did not analyze the distribution pattern of variants within a gene. Using data from 138,632 exome and genome sequences2, we developed gene variation intolerance rank (GeVIR), a continuous gene-level metric for 19,361 genes that is able to prioritize both dominant and recessive Mendelian disease genes5, that outperforms missense constraint metrics3 and that is comparable-but complementary-to loss-of-function (LOF) constraint metrics2. GeVIR is also able to prioritize short genes, for which LOF constraint cannot be estimated with confidence2. The majority of the most intolerant genes identified here have no defined phenotype and are candidates for severe dominant disorders.

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.

A meta-analysis to identify novel diagnostic and therapeutic targets for Dupuytren's disease.

The aim of this study was to determine novel candidate genes for Dupuytren's disease by performing a meta-analysis. We identified 261 genes (111 up-regulated and 150 down-regulated) that were consistently expressed differentially in Dupuytren's disease across the studies. We performed functional enrichment on total sets of the identified 261 genes and confirmed that most of the genes were closely related to common processes of diseases in general. From the integrated studies of the gene-correlation network and the protein-protein interaction network, we identified three functional modules in the gene co-expression network and four hub gene clusters in the protein-protein interaction network that shared the same genes and represented similar biological functions, implying that the seven groups identified in the systematic analysis of these two networks might be involved in the pathogenesis of Dupuytren's disease. This work demonstrates potential in developing experimental and clinical strategies for understanding and treating Dupuytren's disease.

Computational/in silico methods in drug target and lead prediction.

Drug-like compounds are most of the time denied approval and use owing to the unexpected clinical side effects and cross-reactivity observed during clinical trials. These unexpected outcomes resulting in significant increase in attrition rate centralizes on the selected drug targets. These targets may be disease candidate proteins or genes, biological pathways, disease-associated microRNAs, disease-related biomarkers, abnormal molecular phenotypes, crucial nodes of biological network or molecular functions. This is generally linked to several factors, including incomplete knowledge on the drug targets and unpredicted pharmacokinetic expressions upon target interaction or off-target effects. A method used to identify targets, especially for polygenic diseases, is essential and constitutes a major bottleneck in drug development with the fundamental stage being the identification and validation of drug targets of interest for further downstream processes. Thus, various computational methods have been developed to complement experimental approaches in drug discovery. Here, we present an overview of various computational methods and tools applied in predicting or validating drug targets and drug-like molecules. We provide an overview on their advantages and compare these methods to identify effective methods which likely lead to optimal results. We also explore major sources of drug failure considering the challenges and opportunities involved. This review might guide researchers on selecting the most efficient approach or technique during the computational drug discovery process.