Candidate Genes For Human Diseases Research Articles

Multiplexed Genome Editing for Efficient Phenotypic Screening in Zebrafish.

Zebrafish are widely used to investigate candidate genes for human diseases. While the emergence of CRISPR-Cas9 technology has revolutionized gene editing, the use of individual guide RNAs limits the efficiency and application of this technology in functional genetics research. Multiplexed genome editing significantly enhances the efficiency and scope of gene editing. Herein, we describe an efficient multiplexed genome editing strategy to generate zebrafish mutants. Following behavioural tests and histological examination, we identified one new candidate gene (tmem183a) for hearing loss. This study provides a robust genetic platform to quickly obtain zebrafish mutants and to identify candidate genes by phenotypic readouts.

DGLinker: flexible knowledge-graph prediction of disease-gene associations.

As a result of the advent of high-throughput technologies, there has been rapid progress in our understanding of the genetics underlying biological processes. However, despite such advances, the genetic landscape of human diseases has only marginally been disclosed. Exploiting the present availability of large amounts of biological and phenotypic data, we can use our current understanding of disease genetics to train machine learning models to predict novel genetic factors associated with the disease. To this end, we developed DGLinker, a webserver for the prediction of novel candidate genes for human diseases given a set of known disease genes. DGLinker has a user-friendly interface that allows non-expert users to exploit biomedical information from a wide range of biological and phenotypic databases, and/or to upload their own data, to generate a knowledge-graph and use machine learning to predict new disease-associated genes. The webserver includes tools to explore and interpret the results and generates publication-ready figures. DGLinker is available at https://dglinker.rosalind.kcl.ac.uk. The webserver is free and open to all users without the need for registration.

Phenolyzer: phenotype-based prioritization of candidate genes for human diseases.

Prior biological knowledge and phenotype information may help to identify disease genes from human whole-genome and whole-exome sequencing studies. We developed Phenolyzer (http://phenolyzer.usc.edu), a tool that uses prior information to implicate genes involved in diseases. Phenolyzer exhibits superior performance over competing methods for prioritizing Mendelian and complex disease genes, based on disease or phenotype terms entered as free text.

Nuclear Envelope Protein Lem2 is Required for Mouse Development and Regulates MAP and AKT Kinases

The nuclear lamina, along with associated nuclear membrane proteins, is a nexus for regulating signaling in the nucleus. Numerous human diseases arise from mutations in lamina proteins, and experimental models for these disorders have revealed aberrant regulation of various signaling pathways. Previously, we reported that the inner nuclear membrane protein Lem2, which is expressed at high levels in muscle, promotes the differentiation of cultured myoblasts by attenuating ERK signaling. Here, we have analyzed mice harboring a disrupted allele for the Lem2 gene (Lemd2). No gross phenotypic defects were seen in heterozygotes, although muscle regeneration induced by cardiotoxin was delayed. By contrast, homozygous Lemd2 knockout mice died by E11.5. Although many normal morphogenetic hallmarks were observed in E10.5 knockout embryos, most tissues were substantially reduced in size. This was accompanied by activation of multiple MAP kinases (ERK1/2, JNK, p38) and AKT. Knockdown of Lem2 expression in C2C12 myoblasts also led to activation of MAP kinases and AKT. These findings indicate that Lemd2 plays an essential role in mouse embryonic development and that it is involved in regulating several signaling pathways. Since increased MAP kinase and AKT/mTORC signaling is found in other animal models for diseases linked to nuclear lamina proteins, LEMD2 should be considered to be another candidate gene for human disease.

Models to explore genetics of human aging.

Genetic studies have bestowed insight into the biological mechanisms underlying inter-individual differences in susceptibility to (or resistance to) organisms’ aging. Recent advances in molecular and genetic epidemiology provide tools to explore the genetic sources of the variability in biological aging in humans. To be successful, the genetic study of a complex condition such as aging requires the clear definition of essential traits that can characterize the aging process phenotypically. Phenotypes of human aging have long relied on mortality rate or exceptional longevity. Genome-wide association studies (GWAS) have been shown to present an unbiased approach to the identification of new candidate genes for human diseases. The GWAS approach can also be used for positive health phenotypes such as longevity or a delay in age-related chronic disease, as well as for other age related changes such as loss of telomere length or lens transparency. Sequencing, either in targeted regions or across the whole genome can further identify rare variation that may contribute to the biological aging mechanisms. To date, the results of the GWAS for longevity are rather disappointing, possibly in part due to the small number of individuals with GWAS data who have reached advanced old age.Human aging phenotypes are needed that can be assessed prior to death, and should be both heritable and validated as predictors of longevity. Potentially, phenotypes that focus on “successful” or “healthy” aging will be more powerful as they can be measured in large numbers of people and also are clinically relevant.We postulate that construction of an integrated phenotype of aging can be achieved capitalizing on multiple traits that may have weak correlations, but a shared underlying genetic architecture. This is based on a hypothesis that convergent results from multiple individual aging-related traits will point out the pleiotropic signals responsible for the overall rate of aging of the organism. An approach would be to investigate traits that are linked to the state of many vital functions, disability, and ultimately survival rates, to identify common biological pathways that govern aging processes in humans. New composite aging phenotypes must be validated by predicting all-cause mortality, major chronic disease and disability late in life.

Integrating multiple protein-protein interaction networks to prioritize disease genes: a Bayesian regression approach

BackgroundThe identification of genes responsible for human inherited diseases is one of the most challenging tasks in human genetics. Recent studies based on phenotype similarity and gene proximity have demonstrated great success in prioritizing candidate genes for human diseases. However, most of these methods rely on a single protein-protein interaction (PPI) network to calculate similarities between genes, and thus greatly restrict the scope of application of such methods. Meanwhile, independently constructed and maintained PPI networks are usually quite diverse in coverage and quality, making the selection of a suitable PPI network inevitable but difficult.MethodsWe adopt a linear model to explain similarities between disease phenotypes using gene proximities that are quantified by diffusion kernels of one or more PPI networks. We solve this model via a Bayesian approach, and we derive an analytic form for Bayes factor that naturally measures the strength of association between a query disease and a candidate gene and thus can be used as a score to prioritize candidate genes. This method is intrinsically capable of integrating multiple PPI networks.ResultsWe show that gene proximities calculated from PPI networks imply phenotype similarities. We demonstrate the effectiveness of the Bayesian regression approach on five PPI networks via large scale leave-one-out cross-validation experiments and summarize the results in terms of the mean rank ratio of known disease genes and the area under the receiver operating characteristic curve (AUC). We further show the capability of our approach in integrating multiple PPI networks.ConclusionsThe Bayesian regression approach can achieve much higher performance than the existing CIPHER approach and the ordinary linear regression method. The integration of multiple PPI networks can greatly improve the scope of application of the proposed method in the inference of disease genes.

How pleiotropic genetics of the musculoskeletal system can inform genomics and phenomics of aging.

Genetic study can provide insight into the biologic mechanisms underlying inter-individual differences in susceptibility to (or resistance to) organisms' aging. Recent advances in molecular genetics and genetic epidemiology provide the necessary tools to perform a study of the genetic sources of biological aging. However, to be successful, the genetic study of a complex condition requires a heritable phenotype to be developed and validated. Genome-wide association studies offer an unbiased approach to identify new candidate genes for human diseases. It is hypothesized that convergent results from multiple aging-related traits will point out the genes responsible for the general aging of the organism. This perspective focuses on the musculoskeletal aging as an example of an approach to identify a downstream common pathway that summarizes aging processes. Since the musculoskeletal traits are linked to the state of many vital functions, disability, and ultimately survival rates, we postulate that there is significance in studying musculoskeletal aging. Construction of an integrated phenotype of aging can be achieved based on shared genetics among multiple musculoskeletal biomarkers. Valid biomarkers from other systems of the organism should be similarly explored. The new composite aging score needs to be validated by determining whether it predicts all-cause mortality, incidences of major chronic diseases, and disability late in life. Comprehensive databases on biomarkers of musculoskeletal aging in multiple large cohort studies, along with information on various health outcomes, are needed to validate the proposed measure of biological aging.

Syndrome to gene (S2G): in-silico identification of candidate genes for human diseases

The identification of genomic loci associated with human genetic syndromes has been significantly facilitated through the generation of high density SNP arrays. However, optimal selection of candidate genes from within such loci is still a tedious labor-intensive bottleneck. Syndrome to Gene (S2G) is based on novel algorithms which allow an efficient search for candidate genes in a genomic locus, using known genes whose defects cause phenotypically similar syndromes. S2G (http://fohs.bgu.ac.il/s2g/index.html) includes two components: a phenotype Online Mendelian Inheritance in Man (OMIM)-based search engine that alleviates many of the problems in the existing OMIM search engine (negation phrases, overlapping terms, etc.). The second component is a gene prioritizing engine that uses a novel algorithm to integrate information from 18 databases. When the detailed phenotype of a syndrome is inserted to the web-based software, S2G offers a complete improved search of the OMIM database for similar syndromes. The software then prioritizes a list of genes from within a genomic locus, based on their association with genes whose defects are known to underlie similar clinical syndromes. We demonstrate that in all 30 cases of novel disease genes identified in the past year, the disease gene was within the top 20% of candidate genes predicted by S2G, and in most cases--within the top 10%. Thus, S2G provides clinicians with an efficient tool for diagnosis and researchers with a candidate gene prediction tool based on phenotypic data and a wide range of gene data resources. S2G can also serve in studies of polygenic diseases, and in finding interacting molecules for any gene of choice.

An expression atlas of connexin genes in the mouse

Connexin genes are involved in several human diseases such as hearing and dermatological and peripheral nerve disorders. Connexins are protein units of gap junctions and form homotypic, heterotypic, or heteromeric complexes known as connexons. Data on the expression patterns of members of this family are partial and fragmentary. We therefore cloned all the identifiable murine homologs of human CONNEXIN genes and analyzed their expression patterns in embryonic and neonatal mouse tissues. We found that connexins are preferentially expressed in tissues derived from ectoderm and/or endoderm. Our data provide a comprehensive and detailed atlas of expression of connexin genes and in some cases suggest possible interactions of proteins that are coexpressed in the same tissue. Knowledge of temporal and spatial distribution of connexins also allows the identification of candidate genes for human diseases and provides important insight into mechanisms that lead to human disorders due to mutations in CONNEXIN genes.

Multiple Hits during Early Embryonic Development: Digenic Diseases and Holoprosencephaly

Multiple genetic and environmental factors likely play a role in the phenotypic expression of many Mendelian disorders. Understanding the biologic role of the genes associated with human disease may guide future explorations of additional modifier genes. For example, with HPE, there are several possible categories of additional candidate genes and potential sites of interaction. First, there are genes encoding proteins that are in the signaling pathways for identified disease genes (e.g., Shh and nodal signaling). Second, genes involved in the relevant developmental processes are potential candidate genes for human diseases (dorsal-ventral patterning of the forebrain, other processes in midline forebrain development, prechordal mesoderm induction). Third, genes involved in synthesis or metabolism of teratogenic agents (cholesterol biosynthesis, retinoic acid metabolism) could also be potential genetic contributors. Multiple genetic hits or environmental exposures may be required for clinical expression of many Mendelian disorders. Conceptions of a disease being the result of mutations in a single gene should take into account the overlap between Mendelian and multifactorial disorders. Indeed, as more detail emerges demonstrating the impact of genomic and environmental variability on phenotype, we may reconceptualize classic Mendelian and complex multifactorial disorders as two ends of a continuum of disease causation, with the gradient reflecting the relative independence of a given mutation in producing the disease phenotype. It may be useful to remember that “genomes speak biochemistry, not phenotype” (Plasterk 1999). Future studies will focus both on the identification of additional genes involved with these human diseases and on the understanding of their biologic interactions. Such information will lend greater insight into the complex genetic and environmental influences that lead to phenotypic expression of a trait.

Development and diseases of the pancreas.

The pancreas is a vital gland of exocrine and endocrine function. It is the target of two main affections: diabetes and pancreatic cancer. We describe the tissue interactions, signaling pathways and intracellular targets that are involved in the emergence of the pancreas primordium and its proliferation, morphogenesis and differentiation. It appears that several genes of developmental relevance have an adult function and are involved in pancreas affections. Embryological experimentation in animals contributed to provide candidate genes for human disease and holds promise for future treatments.

Toward an etiological classification of developmental disorders of the kidney and upper urinary tract

Toward an etiological classification of developmental disorders of the kidney and upper urinary tract. There are a large number of developmental disorders and syndromes that affect the kidney and upper urinary tract. These have generally been classified according to morphological criteria established decades ago. Although these classifications have been useful, they are incomplete, including some developmental disorders while excluding others. Here, basic cellular and molecular biology studies of kidney and upper urinary tract development in both rodents and humans are utilized to suggest the basis of a new etiologic, if still tentative, classification scheme. This classification may help to identify candidate genes for human diseases by correlating morphology with pathogenetic mechanisms.

A practical guide to orient yourself in the labyrinth of genome databases.

The identification of genes involved in human inherited disorders has been revolutionized by the resources produced by the Human Genome Project. In particular, the generation of >1 000 000 human expressed sequence tags (ESTs) has led to the partial identification of a significant percentage of all human genes. In the next 7 years, we will witness another revolution when sequencing of the human genome is complete. The generation of large amounts of genomic data must be accompanied by parallel efforts to make the information easily accessible. Efforts towards this goal have already started, but retrieval of information from genomic databases still remains an arduous task. With practical examples, we will try to show how the currently available information can be exploited usefully, in particular to identify candidate genes for human diseases.

Identification and Mapping of Human Histone Acetylation Modifier Gene Homologues

The products of histone acetyltransferase and deacetyltransferase genes regulate histone acetylation in eukaryotes, thereby regulating access of transcription factors to chromatin and modulating gene expression. Histone acetylation modifiers have been found to participate as cofactors in diverse mammalian transcriptional complexes involved in regulation of cellular proliferation and differentiation. A role for histone acetylase has been implicated in leukemias and developmental disorders. To gain insight into a role of additional potential histone acetylation modifier genes in human disease, we identified six histone acetyltransferase or deacetyltransferase homologues using the dbEST database, and we mapped, using high-resolution FISH, a total of five family members to 1p34.3, 6q21–q22, 5q31, 3p24, and 17q21. We then identified human genetic disorders for which candidate genes are not yet known and that have been mapped to the same chromosomal regions as the histone acetylation modifiers. This analysis may help identify new candidate genes for human diseases that involve disturbances of histone acetylation.

EST! EST!! EST!!!

A recently published study has identified a set of candidate genes for human diseases based on findings from Drosophila. Each human expressed sequence tag (EST) in a large database was compared with all known Drosophila genes. After eliminating matches between genes of already known function, the remaining sequences were mapped in the human genome. In each region, the phenotypes of all known human diseases were compared with the phenotypes of known Drosophila mutations in order to identify candidate genes for the human diseases. Are the correspondences real or coincidental?