Identification Of Disease Genes Research Articles

G-Protein Signaling in Alzheimer's Disease: Spatial Expression Validation of Semi-supervised Deep Learning-Based Computational Framework.

Systemic study of pathogenic pathways and interrelationships underlying genes associated with Alzheimer's disease (AD) facilitates the identification of new targets for effective treatments. Recently available large-scale multiomics datasets provide opportunities to use computational approaches for such studies. Here, we devised a novel disease gene identification (digID) computational framework that consists of a semi-supervised deep learning classifier to predict AD-associated genes and a protein-protein interaction (PPI) network-based analysis to prioritize the importance of these predicted genes in AD. digID predicted 1,529 AD-associated genes and revealed potentially new AD molecular mechanisms and therapeutic targets including GNAI1 and GNB1, two G-protein subunits that regulate cell signaling, and KNG1, an upstream modulator of CDC42 small G-protein signaling and mediator of inflammation and candidate coregulator of amyloid precursor protein (APP). Analysis of mRNA expression validated their dysregulation in AD brains but further revealed the significant spatial patterns in different brain regions as well as among different subregions of the frontal cortex and hippocampi. Super-resolution STochastic Optical Reconstruction Microscopy (STORM) further demonstrated their subcellular colocalization and molecular interactions with APP in a transgenic mouse model of both sexes with AD-like mutations. These studies support the predictions made by digID while highlighting the importance of concurrent biological validation of computationally identified gene clusters as potential new AD therapeutic targets.

How exome sequencing improves the diagnostics and management of men with non-syndromic infertility.

Male infertility affects approximately 17% of all men and represents a complex disorder in which not only semen parameters such as sperm motility, morphology, and number of sperm are highly variable, but also testicular phenotypes range from normal spermatogenesis to complete absence of germ cells. Genetic factors significantly contribute to the disease but chromosomal aberrations, mostly Klinefelter syndrome, and microdeletions of the Y-chromosome have remained the only diagnostically and clinically considered genetic causes. Monogenic causes remain understudied and, thus, often unidentified, leaving the majority of the male factor couple infertility pathomechanistically unexplained. This has been changing mostly because of the introduction of exome sequencing that allows the analysis of multiple genes in large patient cohorts. As a result, pathogenic variants in single genes have been associated with non-syndromic forms of all aetiologic sub-categories in the last decade. This review highlights the contribution of exome sequencing to the identification of novel disease genes for isolated (non-syndromic) male infertility by presenting the results of a comprehensive literature search. Both, reduced sperm count in azoospermic and oligozoospermic patients, and impaired sperm motility and/or morphology, in asthenozoospermic and/or teratozoospermic patients are highly heterogeneous diseases with well over 100 different candidate genes described for each entity. Applying the standardized evaluation criteria of the ClinGen gene curation working group, 70 genes with at least moderate evidence to contribute to the disease are highlighted. The implementation of these valid disease genes in clinical exome sequencing is important to increase the diagnostic yield in male infertility and, thus, improve clinical decision-making and appropriate genetic counseling. Future advances in androgenetics will continue to depend on large-scale exome and genome sequencing studies of comprehensive international patient cohorts, which are the most promising approaches to identify additional disease genes and provide reliable data on the gene-disease relationship.

Application of multiple mosaic callers improves post-zygotic mutation detection from exome sequencing data

PurposeThe gold standard for identification of post-zygotic variants (PZVs) is droplet digital polymerase chain reaction or high-depth sequencing across multiple tissues types. These approaches are yet to be systematically implemented for monogenic disorders. We developed PZV detection pipelines for correct classification of de novo variants. MethodOur pipelines detect PZV in parents (gonosomal mosaicism [pGoM]) and children (somatic mosaicism, “M3”). We applied them to research exome sequencing (ES) data from the Australian Cerebral Palsy Biobank (n = 145 trios) and Simons Simplex Collection (n = 405 families). Candidate mosaic variants were validated using deep amplicon sequencing or droplet digital polymerase chain reaction. Results69.2% (M3trio), 63.9% (M3single), and 92.7% (pGoM) of detected variants were validated, with 48.6%, 56.7%, and 26.2% of variants, respectively, meeting strict criteria for mosaicism. In the Australian Cerebral Palsy Biobank, 16.6% of probands and 20.7% of parents had at least 1 true-positive somatic or pGoM variant, respectively. A large proportion of PZVs detected in Simons Simplex Collection parents (79.8%) and child (94.5%) were not previously reported. We reclassified 3.7% to 8.0% of germline de novo variants as mosaic. ConclusionMany PZVs were incorrectly classified as germline variants or missed by previous approaches. Systematic application of our pipelines could increase genetic diagnostic rate, improve estimates of recurrence risk in families, and benefit novel disease gene identification.

De novo and inherited monoallelic variants in TUBA4A cause ataxia and spasticity.

Alpha-tubulin 4A encoding gene (TUBA4A) has been associated with familial amyotrophic lateral sclerosis (fALS) and fronto-temporal dementia (FTD), based on identification of likely pathogenic variants in patients from distinct ALS and FTD cohorts. By screening a multicentric French cohort of 448 unrelated probands presenting with cerebellar ataxia, we identified ultra-rare TUBA4A missense variants, all being absent from public databases and predicted pathogenic by multiple in-silico tools. In addition, gene burden analyses in the 100,000 genomes project (100KGP) showed enrichment of TUBA4A rare variants in the inherited ataxia group compared to controls (OR: 57.0847 [10.2- 576.7]; p = 4.02 x10-07). Altogether, we report 12 patients presenting with spasticity and/or cerebellar ataxia and harboring a predicted pathogenic TUBA4A missense mutation, including 5 confirmed de novo cases and a mutation previously reported in a large family presenting with spastic ataxia. Cultured fibroblasts from 3 patients harboring distinct TUBA4A missense showed significant alterations in microtubule organisation and dynamics, providing insight of TUBA4A variants pathogenicity. Our data confirm the identification of a hereditary spastic ataxia disease gene with variable age of onset, expanding the clinical spectrum of TUBA4A associated phenotypes.

ZSCAN10 deficiency causes a neurodevelopmental disorder with characteristic oto-facial malformations.

Neurodevelopmental disorders are major indications for genetic referral and have been linked to more than 1500 loci including genes encoding transcriptional regulators. The dysfunction of transcription factors often results in characteristic syndromic presentations; however, at least half of these patients lack a genetic diagnosis. The implementation of machine learning approaches has the potential to aid in the identification of new disease genes and delineate associated phenotypes. Next generation sequencing was performed in seven affected individuals with neurodevelopmental delay and dysmorphic features. Clinical characterization included reanalysis of available neuroimaging datasets and 2D portrait image analysis with GestaltMatcher. The functional consequences of ZSCAN10 loss were modelled in mouse embryonic stem cells (mESCs), including a knockout and a representative ZSCAN10 protein truncating variant. These models were characterized by gene expression and western blot analyses, chromatin immunoprecipitation and quantitative PCR (ChIP-qPCR) and immunofluorescence staining. Zscan10 knockout mouse embryos were generated and phenotyped. We prioritized bi-allelic ZSCAN10 loss-of-function variants in seven affected individuals from five unrelated families as the underlying molecular cause. RNA-sequencing analyses in Zscan10-/- mESCs indicated dysregulation of genes related to stem cell pluripotency. In addition, we established in mESCs the loss-of-function mechanism for a representative human ZSCAN10 protein truncating variant by showing alteration of its expression levels and subcellular localization, interfering with its binding to DNA enhancer targets. Deep phenotyping revealed global developmental delay, facial asymmetry and malformations of the outer ear as consistent clinical features. Cerebral MRI showed dysplasia of the semicircular canals as an anatomical correlate of sensorineural hearing loss. Facial asymmetry was confirmed as a clinical feature by GestaltMatcher and was recapitulated in the Zscan10 mouse model along with inner and outer ear malformations. Our findings provide evidence of a novel syndromic neurodevelopmental disorder caused by bi-allelic loss-of-function variants in ZSCAN10.

ClinGen guidance for use of the PP1/BS4 co-segregation and PP4 phenotype specificity criteria for sequence variant pathogenicity classification

The 2015 American College of Medical Genetics and Genomics and the Association for Molecular Pathology variant classification publication established a standard employed internationally to guide laboratories in variant assessment. Those recommendations included both pathogenic (PP1) and benign (BS4) criteria for evaluating the inheritance patterns of variants, but details of how to apply those criteria at appropriate evidence levels were sparse. Several publications have since attempted to provide additional guidance, but anecdotally, this issue is still challenging. Additionally, it is not clear that those prior efforts fully distinguished disease-gene identification considerations from variant pathogenicity considerations nor did they address autosomal-recessive and X-linked inheritance. Here, we have taken a mixed inductive and deductive approach to this problem using real diseases as examples. We have developed a practical heuristic for genetic co-segregation evidence and have also determined that the specific phenotype criterion (PP4) is inseparably coupled to the co-segregation criterion. We have also determined that negative evidence at one locus constitutes positive evidence for other loci for disorders with locus heterogeneity. Finally, we provide a points-based system for evaluating phenotype and co-segregation as evidence types to support or refute a locus and show how that can be integrated into the Bayesian framework now used for variant classification and consistent with the 2015 guidelines.

Biallelic SOX8 Variants Associated With Novel Syndrome With Myopathy, Skeletal Deformities, Intellectual Disability, and Ovarian Dysfunction.

The human genome contains ∼20,000 genes, each of which has its own set of complex regulatory systems to govern precise expression in each developmental stage and cell type. Here, we report a female patient with congenital weakness, respiratory failure, skeletal dysplasia, contractures, short stature, intellectual delay, respiratory failure, and amenorrhea who presented to Medical Genetics service with no known cause for her condition. Whole-exome and whole-genome sequencing were conducted, as well as investigational functional studies to assess the effect of SOX8 variant. The patient was found to have biallelic SOX8 variants (NM_014587.3:c.422+5G>C; c.583dup p.(His195ProfsTer11)). SOX8 is a transcriptional regulator, which is predicted to be imprinted (expressed from only one parental allele), but this has not yet been confirmed. We provide evidence that while SOX8 was maternally expressed in adult-derived fibroblasts and lymphoblasts, it was biallelically expressed in other cell types and therefore suggest that biallelic variants are associated with this recessive condition. Functionally, we showed that the paternal variant had the capacity to affect mRNA splicing while the maternal variant resulted in low levels of a truncated protein, which showed decreased binding at and altered expression of SOX8 targets. Our findings associate SOX8 variants with this novel condition, highlight how complex genome regulation can complicate novel disease-gene identification, and provide insight into the molecular pathogenesis of this disease.

Deep structured learning for variant prioritization in Mendelian diseases

Effective computer-aided or automated variant evaluations for monogenic diseases will expedite clinical diagnostic and research efforts of known and novel disease-causing genes. Here we introduce MAVERICK: a Mendelian Approach to Variant Effect pRedICtion built in Keras. MAVERICK is an ensemble of transformer-based neural networks that can classify a wide range of protein-altering single nucleotide variants (SNVs) and indels and assesses whether a variant would be pathogenic in the context of dominant or recessive inheritance. We demonstrate that MAVERICK outperforms all other major programs that assess pathogenicity in a Mendelian context. In a cohort of 644 previously solved patients with Mendelian diseases, MAVERICK ranks the causative pathogenic variant within the top five variants in over 95% of cases. Seventy-six percent of cases were solved by the top-ranked variant. MAVERICK ranks the causative pathogenic variant in hitherto novel disease genes within the first five candidate variants in 70% of cases. MAVERICK has already facilitated the identification of a novel disease gene causing a degenerative motor neuron disease. These results represent a significant step towards automated identification of causal variants in patients with Mendelian diseases.

Multi-View Kernel Learning for Identification of Disease Genes.

Gene expression data sets and protein-protein interaction (PPI) networks are two heterogeneous data sources that have been extensively studied, due to their ability to capture the co-expression patterns among genes and their topological connections. Although they depict different traits of the data, both of them tend to group co-functional genes together. This phenomenon agrees with the basic assumption of multi-view kernel learning, according to which different views of the data contain a similar inherent cluster structure. Based on this inference, a new multi-view kernel learning based disease gene identification algorithm, termed as DiGId, is put forward. A novel multi-view kernel learning approach is proposed that aims to learn a consensus kernel, which efficiently captures the heterogeneous information of individual views as well as depicts the underlying inherent cluster structure. Some low-rank constraints are imposed on the learned multi-view kernel, so that it can effectively be partitioned into k or fewer clusters. The learned joint cluster structure is used to curate a set of potential disease genes. Moreover, a novel approach is put forward to quantify the importance of each view. In order to demonstrate the effectiveness of the proposed approach in capturing the relevant information depicted by individual views, an extensive analysis is performed on four different cancer-related gene expression data sets and PPI network, considering different similarity measures.

NIAPU: network-informed adaptive positive-unlabeled learning for disease gene identification.

Gene-disease associations are fundamental for understanding disease etiology and developing effective interventions and treatments. Identifying genes not yet associated with a disease due to a lack of studies is a challenging task in which prioritization based on prior knowledge is an important element. The computational search for new candidate disease genes may be eased by positive-unlabeled learning, the machine learning (ML) setting in which only a subset of instances are labeled as positive while the rest of the dataset is unlabeled. In this work, we propose a set of effective network-based features to be used in a novel Markov diffusion-based multi-class labeling strategy for putative disease gene discovery. The performances of the new labeling algorithm and the effectiveness of the proposed features have been tested on 10 different disease datasets using three ML algorithms. The new features have been compared against classical topological and functional/ontological features and a set of network- and biological-derived features already used in gene discovery tasks. The predictive power of the integrated methodology in searching for new disease genes has been found to be competitive against state-of-the-art algorithms. The source code of NIAPU can be accessed at https://github.com/AndMastro/NIAPU. The source data used in this study are available online on the respective websites. Supplementary data are available at Bioinformatics online.

Mapping the common gene networks that underlie related diseases.

A longstanding goal of biomedicine is to understand how alterations in molecular and cellular networks give rise to the spectrum of human diseases. For diseases with shared etiology, understanding the common causes allows for improved diagnosis of each disease, development of new therapies and more comprehensive identification of disease genes. Accordingly, this protocol describes how to evaluate the extent to which two diseases, each characterized by a set of mapped genes, are colocalized in a reference gene interaction network. This procedure uses network propagation to measure the network 'distance' between gene sets. For colocalized diseases, the network can be further analyzed to extract common gene communities at progressive granularities. In particular, we show how to: (1) obtain input gene sets and a reference gene interaction network; (2) identify common subnetworks of genes that encompass or are in close proximity to all gene sets; (3) use multiscale community detection to identify systems and pathways represented by each common subnetwork to generate a network colocalized systems map; (4) validate identified genes and systems using a mouse variant database; and (5) visualize and further investigate select genes, interactions and systems for relevance to phenotype(s) of interest. We demonstrate the utility of this approach by identifying shared biological mechanisms underlying autism and congenital heart disease. However, this protocol is general and can be applied to any gene sets attributed to diseases or other phenotypes with suspected joint association. A typical NetColoc run takes less than an hour. Software and documentation are available at https://github.com/ucsd-ccbb/NetColoc .

The frequency of somatic mutations in cancer predicts the phenotypic relevance of germline mutations.

Genomic sequence mutations can be pathogenic in both germline and somatic cells. Several authors have observed that often the same genes are involved in cancer when mutated in somatic cells and in genetic diseases when mutated in the germline. Recent advances in high-throughput sequencing techniques have provided us with large databases of both types of mutations, allowing us to investigate this issue in a systematic way. Hence, we applied a machine learning based framework to this problem, comparing multiple models. The models achieved significant predictive power as shown by both cross-validation and their application to recently discovered gene/phenotype associations not used for training. We found that genes characterized by high frequency of somatic mutations in the most common cancers and ancient evolutionary age are most likely to be involved in abnormal phenotypes and diseases. These results suggest that the combination of tolerance for mutations at the cell viability level (measured by the frequency of somatic mutations in cancer) and functional relevance (demonstrated by evolutionary conservation) are the main predictors of disease genes. Our results thus confirm the deep relationship between pathogenic mutations in somatic and germline cells, provide new insight into the common origin of cancer and genetic diseases, and can be used to improve the identification of new disease genes.

Current Cellular and Molecular Biology Techniques for the Orthopaedic Surgeon-Scientist.

The past decade has witnessed numerous advances in musculoskeletal medicine that are helping to propel the field forward. From disease gene identification, to the development of CRISPR-Cas9 and its many applications to treat heritable musculoskeletal conditions, to the refinement of single-cell RNA sequencing (scRNA-seq) and its ability to characterize previously undescribed cellular populations, bench research and translational medicine are evolving at a remarkable pace. In parallel with these laboratory techniques, there has been a rapid evolution of the use of biologics within orthopedic surgery, specifically tendon healing in shoulder surgery, exemplified by the use of platelet-rich plasma, scaffolds, and stem cells to improve tendon healing in the setting of rotator cuff repair.

Chapter 10 - Genetics of mitochondrial diseases: Current approaches for the molecular diagnosis

Mitochondrial diseases are a genetically and phenotypically variable set of monogenic disorders. The main characteristic of mitochondrial diseases is a defective oxidative phosphorylation. Both nuclear and mitochondrial DNA encode the approximately 1500 mitochondrial proteins. Since identification of the first mitochondrial disease gene in 1988 a total of 425 genes have been associated with mitochondrial diseases. Mitochondrial dysfunctions can be caused both by pathogenic variants in the mitochondrial DNA or the nuclear DNA. Hence, besides maternal inheritance, mitochondrial diseases can follow all modes of Mendelian inheritance. The maternal inheritance and tissue specificity distinguish molecular diagnostics of mitochondrial disorders from other rare disorders. With the advances made in the next-generation sequencing technology, whole exome sequencing and even whole-genome sequencing are now the established methods of choice for molecular diagnostics of mitochondrial diseases. They reach a diagnostic rate of more than 50% in clinically suspected mitochondrial disease patients. Moreover, next-generation sequencing is delivering a constantly growing number of novel mitochondrial disease genes. This chapter reviews mitochondrial and nuclear causes of mitochondrial diseases, molecular diagnostic methodologies, and their current challenges and perspectives.

A Review of Random Walk-Based Method for the Identification of Disease Genes and Disease Modules

A Review of Random Walk-Based Method for the Identification of Disease Genes and Disease Modules

Analysis of genome-wide knockout mouse database identifies candidate ciliopathy genes

We searched a database of single-gene knockout (KO) mice produced by the International Mouse Phenotyping Consortium (IMPC) to identify candidate ciliopathy genes. We first screened for phenotypes in mouse lines with both ocular and renal or reproductive trait abnormalities. The STRING protein interaction tool was used to identify interactions between known cilia gene products and those encoded by the genes in individual knockout mouse strains in order to generate a list of “candidate ciliopathy genes.” From this list, 32 genes encoded proteins predicted to interact with known ciliopathy proteins. Of these, 25 had no previously described roles in ciliary pathobiology. Histological and morphological evidence of phenotypes found in ciliopathies in knockout mouse lines are presented as examples (genes Abi2, Wdr62, Ap4e1, Dync1li1, and Prkab1). Phenotyping data and descriptions generated on IMPC mouse line are useful for mechanistic studies, target discovery, rare disease diagnosis, and preclinical therapeutic development trials. Here we demonstrate the effective use of the IMPC phenotype data to uncover genes with no previous role in ciliary biology, which may be clinically relevant for identification of novel disease genes implicated in ciliopathies.

A Knowledge Graph-Enhanced Tensor Factorisation Model for Discovering Drug Targets.

The drug discovery and development process is a long and expensive one, costing over 1 billion USD on average per drug and taking 10-15 years. To reduce the high levels of attrition throughout the process, there has been a growing interest in applying machine learning methodologies to various stages of drug discovery and development in the recent decade, especially at the earliest stage - identification of druggable disease genes. In this paper, we have developed a new tensor factorisation model to predict potential drug targets (genes or proteins) for treating diseases. We created a three-dimensional data tensor consisting of 1,048 gene targets, 860 diseases and 230,011 evidence attributes and clinical outcomes connecting them, using data extracted from the Open Targets and PharmaProjects databases. We enriched the data with gene target representations learned from a drug discovery-oriented knowledge graph and applied our proposed method to predict the clinical outcomes for unseen gene target and disease pairs. We designed three evaluation strategies to measure the prediction performance and benchmarked several commonly used machine learning classifiers together with Bayesian matrix and tensor factorisation methods. The result shows that incorporating knowledge graph embeddings significantly improves the prediction accuracy and that training tensor factorisation alongside a dense neural network outperforms all other baselines. In summary, our framework combines two actively studied machine learning approaches to disease target identification, namely tensor factorisation and knowledge graph representation learning, which could be a promising avenue for further exploration in data-driven drug discovery.

Comment on: Disease gene identification strategies for exome sequencing by Gilissen et al. 2012.

Comment on: Disease gene identification strategies for exome sequencing by Gilissen et al. 2012.

Network approaches for identification of human genetic disease genes

The identification of genes causing a genetic disease is still an important issue in the biomedical field because the list of disease genes is still incomplete while it determines the early diagnosis and treatment of fatal genetic diseases such as autism, cancer, drug resistance, and secondary hypertension. Genes associated with a particular disease or similar diseases tend to reside in the same region in a biological network and their location on the network can be predicted. Many network analysis methods have been proposed to solve this problem so far. This review first helps readers access and master the basic concepts of biological networks, disease genes, and their properties. Then, the main content is devoted to the analysis and evaluation of analytical methods recently used to find disease genes on two networks: protein-protein interaction (PPI) and cellular signaling network (CSN). We reported typical problems of identification of primary genes that cause genetic diseases and modern techniques that were widely used for solving those problems. For each technique, we also represented key algorithms so that the audience can exactly implement them for their experiments. In particular, we evaluated the performance of these algorithms in prediction of disease genes and suggested the context for their usage. Finally, the implications of the methods are discussed and some future research directions are proposed. Taken together, disease genes can often be identified from network data by two approaches: network-based methods and machine learning-based methods, and the network-based approach

Identifying genes targeted by disease-associated non-coding SNPs with a protein knowledge graph.

Genome-wide association studies (GWAS) have identified many single nucleotide polymorphisms (SNPs) that play important roles in the genetic heritability of traits and diseases. With most of these SNPs located on the non-coding part of the genome, it is currently assumed that these SNPs influence the expression of nearby genes on the genome. However, identifying which genes are targeted by these disease-associated SNPs remains challenging. In the past, protein knowledge graphs have often been used to identify genes that are associated with disease, also referred to as “disease genes”. Here, we explore whether protein knowledge graphs can be used to identify genes that are targeted by disease-associated non-coding SNPs by testing and comparing the performance of six existing methods for a protein knowledge graph, four of which were developed for disease gene identification. We compare our performance against two baselines: (1) an existing state-of-the-art method that is based on guilt-by-association, and (2) the leading assumption that SNPs target the nearest gene on the genome. We test these methods with four reference sets, three of which were obtained by different means. Furthermore, we combine methods to investigate whether their combination improves performance. We find that protein knowledge graphs that include predicate information perform comparable to the current state of the art, achieving an area under the receiver operating characteristic curve (AUC) of 79.6% on average across all four reference sets. Protein knowledge graphs that lack predicate information perform comparable to our other baseline (genetic distance) which achieved an AUC of 75.7% across all four reference sets. Combining multiple methods improved performance to 84.9% AUC. We conclude that methods for a protein knowledge graph can be used to identify which genes are targeted by disease-associated non-coding SNPs.