Genome-wide analysis of human disease alleles reveals that their locations are correlated in paralogous proteins

The millions of mutations and polymorphisms that occur in human populations are potential predictors of disease, of our reactions to drugs, of predisposition to microbial infections, and of age-related conditions such as impaired brain and cardiovascular functions. However, predicting the phenotypic consequences and eventual clinical significance of a sequence variant is not an easy task. Computational approaches have found perturbation of conserved amino acids to be a useful criterion for identifying variants likely to have phenotypic consequences. To our knowledge, however, no study to date has explored the potential of variants that occur at homologous positions within paralogous human proteins as a means of identifying polymorphisms with likely phenotypic consequences. In order to investigate the potential of this approach, we have assembled a unique collection of known disease-causing variants from OMIM and the Human Genome Mutation Database (HGMD) and used them to identify and characterize pairs of sequence variants that occur at homologous positions within paralogous human proteins. Our analyses demonstrate that the locations of variants are correlated in paralogous proteins. Moreover, if one member of a variant-pair is disease-causing, its partner is likely to be disease-causing as well. Thus, information about variant-pairs can be used to identify potentially disease-causing variants, extend existing procedures for polymorphism prioritization, and provide a suite of candidates for further diagnostic and therapeutic purposes.     

National Center for Biomedical Ontology: advancing biomedicine through structured organization of scientific knowledge

The National Center for Biomedical Ontology is a consortium that comprises leading informaticians, biologists, clinicians, and ontologists, funded by the National Institutes of Health (NIH) Roadmap, to develop innovative technology and methods that allow scientists to record, manage, and disseminate biomedical information and knowledge in machine-processable form. The goals of the Center are (1) to help unify the divergent and isolated efforts in ontology development by promoting high quality open-source, standards-based tools to create, manage, and use ontologies, (2) to create new software tools so that scientists can use ontologies to annotate and analyze biomedical data, (3) to provide a national resource for the ongoing evaluation, integration, and evolution of biomedical ontologies and associated tools and theories in the context of driving biomedical projects (DBPs), and (4) to disseminate the tools and resources of the Center and to identify, evaluate, and communicate best practices of ontology development to the biomedical community. Through the research activities within the Center, collaborations with the DBPs, and interactions with the biomedical community, our goal is to help scientists to work more effectively in the e-science paradigm, enhancing experiment design, experiment execution, data analysis, information synthesis, hypothesis generation and testing, and understand human disease.     

Concept mapping One-Carbon Metabolism to model future ontologies for nutrient–gene–phenotype interactions

Advances in the development of bioinformatic tools continue to improve investigators’ ability to interrogate, organize, and derive knowledge from large amounts of heterogeneous information. These tools often require advanced technical skills not possessed by life scientists. User-friendly, low-barrier-to-entry methods of visualizing nutrigenomics information are yet to be developed. We utilized concept mapping software from the Institute for Human and Machine Cognition to create a conceptual model of diet and health-related data that provides a foundation for future nutrigenomics ontologies describing published nutrient–gene/polymorphism–phenotype data. In this model, maps containing phenotype, nutrient, gene product, and genetic polymorphism interactions are visualized as triples of two concepts linked together by a linking phrase. These triples, or “knowledge propositions,” contextualize aggregated data and information into easy-to-read knowledge maps. Maps of these triples enable visualization of genes spanning the One-Carbon Metabolism (OCM) pathway, their sequence variants, and multiple literature-mined associations including concepts relevant to nutrition, phenotypes, and health. The concept map development process documents the incongruity of information derived from pathway databases versus literature resources. This conceptual model highlights the importance of incorporating information about genes in upstream pathways that provide substrates, as well as downstream pathways that utilize products of the pathway under investigation, in this case OCM. Other genes and their polymorphisms, such as TCN2 and FUT2, although not directly involved in OCM, potentially alter OCM pathway functionality. These upstream gene products regulate substrates such as B12. Constellations of polymorphisms affecting the functionality of genes along OCM, together with substrate and cofactor availability, may impact resultant phenotypes. These conceptual maps provide a foundational framework for development of nutrient–gene/polymorphism–phenotype ontologies and systems visualization.     

EvoTol: a protein-sequence based evolutionary intolerance framework for disease-gene prioritization

Methods to interpret personal genome sequences are increasingly required. Here, we report a novel framework (EvoTol) to identify disease-causing genes using patient sequence data from within protein coding-regions. EvoTol quantifies a gene''s intolerance to mutation using evolutionary conservation of protein sequences and can incorporate tissue-specific gene expression data. We apply this framework to the analysis of whole-exome sequence data in epilepsy and congenital heart disease, and demonstrate EvoTol''s ability to identify known disease-causing genes is unmatched by competing methods. Application of EvoTol to the human interactome revealed networks enriched for genes intolerant to protein sequence variation, informing novel polygenic contributions to human disease.     

Annotating DNA Variants Is the Next Major Goal for Human Genetics

Clinical genetic testing has undergone a dramatic transformation in the past two decades. Diagnostic laboratories that previously tested for well-established disease-causing DNA variants in a handful of genes have evolved into sequencing factories identifying thousands of variants of known and unknown medical consequence. Sorting out what does and does not cause disease in our genomes is the next great challenge in making genetics a central feature of healthcare. I propose that closing the gap in our ability to interpret variation responsible for Mendelian disorders provides a grand and unprecedented opportunity for geneticists. Human geneticists are well placed to coordinate a systematic evaluation of variants in collaboration with basic scientists and clinicians. Sharing of knowledge, data, methods, and tools will aid both researchers and healthcare workers in achieving their common goal of defining the pathogenic potential of variants. Generation of variant annotations will inform genetic testing and will deepen our understanding of gene and protein function, thereby aiding the search for molecular targeted therapies.     

A spectrum of ABCC6 mutations is responsible for pseudoxanthoma elasticum

To better understand the pathogenetics of pseudoxanthoma elasticum (PXE), we performed a mutational analysis of ATP-binding cassette subfamily C member 6 (ABCC6) in 122 unrelated patients with PXE, the largest cohort of patients yet studied. Thirty-six mutations were characterized, and, among these, 28 were novel variants (for a total of 43 PXE mutations known to date). Twenty-one alleles were missense variants, six were small insertions or deletions, five were nonsense, two were alleles likely to result in aberrant mRNA splicing, and two were large deletions involving ABCC6. Although most mutations appeared to be unique variants, two disease-causing alleles occurred frequently in apparently unrelated individuals. R1141X was found in our patient cohort at a frequency of 18.8% and was preponderant in European patients. ABCC6del23-29 occurred at a frequency of 12.9% and was prevalent in patients from the United States. These results suggested that R1141X and ABCC6del23-29 might have been derived regionally from founder alleles. Putative disease-causing mutations were identified in approximately 64% of the 244 chromosomes studied, and 85.2% of the 122 patients were found to have at least one disease-causing allele. Our results suggest that a fraction of the undetected mutant alleles could be either genomic rearrangements or mutations occurring in noncoding regions of the ABCC6 gene. The distribution pattern of ABCC6 mutations revealed a cluster of disease-causing variants within exons encoding a large C-terminal cytoplasmic loop and in the C-terminal nucleotide-binding domain (NBD2). We discuss the potential structural and functional significance of this mutation pattern within the context of the complex relationship between the PXE phenotype and the function of ABCC6.     

Advancing science through mining libraries, ontologies, and communities

Life scientists today cannot hope to read everything relevant to their research. Emerging text-mining tools can help by identifying topics and distilling statements from books and articles with increased accuracy. Researchers often organize these statements into ontologies, consistent systems of reality claims. Like scientific thinking and interchange, however, text-mined information (even when accurately captured) is complex, redundant, sometimes incoherent, and often contradictory: it is rooted in a mixture of only partially consistent ontologies. We review work that models scientific reason and suggest how computational reasoning across ontologies and the broader distribution of textual statements can assess the certainty of statements and the process by which statements become certain. With the emergence of digitized data regarding networks of scientific authorship, institutions, and resources, we explore the possibility of accounting for social dependences and cultural biases in reasoning models. Computational reasoning is starting to fill out ontologies and flag internal inconsistencies in several areas of bioscience. In the not too distant future, scientists may be able to use statements and rich models of the processes that produced them to identify underexplored areas, resurrect forgotten findings and ideas, deconvolute the spaghetti of underlying ontologies, and synthesize novel knowledge and hypotheses.     

Semi-supervised methods to predict patient survival from gene expression data

An important goal of DNA microarray research is to develop tools to diagnose cancer more accurately based on the genetic profile of a tumor. There are several existing techniques in the literature for performing this type of diagnosis. Unfortunately, most of these techniques assume that different subtypes of cancer are already known to exist. Their utility is limited when such subtypes have not been previously identified. Although methods for identifying such subtypes exist, these methods do not work well for all datasets. It would be desirable to develop a procedure to find such subtypes that is applicable in a wide variety of circumstances. Even if no information is known about possible subtypes of a certain form of cancer, clinical information about the patients, such as their survival time, is often available. In this study, we develop some procedures that utilize both the gene expression data and the clinical data to identify subtypes of cancer and use this knowledge to diagnose future patients. These procedures were successfully applied to several publicly available datasets. We present diagnostic procedures that accurately predict the survival of future patients based on the gene expression profile and survival times of previous patients. This has the potential to be a powerful tool for diagnosing and treating cancer.     

PhenomeNET: a whole-phenome approach to disease gene discovery

Phenotypes are investigated in model organisms to understand and reveal the molecular mechanisms underlying disease. Phenotype ontologies were developed to capture and compare phenotypes within the context of a single species. Recently, these ontologies were augmented with formal class definitions that may be utilized to integrate phenotypic data and enable the direct comparison of phenotypes between different species. We have developed a method to transform phenotype ontologies into a formal representation, combine phenotype ontologies with anatomy ontologies, and apply a measure of semantic similarity to construct the PhenomeNET cross-species phenotype network. We demonstrate that PhenomeNET can identify orthologous genes, genes involved in the same pathway and gene-disease associations through the comparison of mutant phenotypes. We provide evidence that the Adam19 and Fgf15 genes in mice are involved in the tetralogy of Fallot, and, using zebrafish phenotypes, propose the hypothesis that the mammalian homologs of Cx36.7 and Nkx2.5 lie in a pathway controlling cardiac morphogenesis and electrical conductivity which, when defective, cause the tetralogy of Fallot phenotype. Our method implements a whole-phenome approach toward disease gene discovery and can be applied to prioritize genes for rare and orphan diseases for which the molecular basis is unknown.     

Automation of a primer design and evaluation pipeline for subsequent sequencing of the coding regions of all human Refseq genes

Screening for mutations in human disease-causing genes in a molecular diagnostic environment demands simplicity with a view to allowing high throughput approaches. In order to advance these requirements, we have developed and applied a primer design program, termed BatchPD, to achieve the PCR amplification of coding exons of all known human Refseq genes. Primer design, in silico PCR checks and formatted primer information for subsequent web-based interrogation are queried from existing online tools. BatchPD acts as an intermediate to automate queries and results processing and provides exon-specific information that is summarised in a spreadsheet format.     

Mutation@A Glance: An Integrative Web Application for Analysing Mutations from Human Genetic Diseases

Although mutation analysis serves as a key part in making a definitive diagnosis about a genetic disease, it still remains a time-consuming step to interpret their biological implications through integration of various lines of archived information about genes in question. To expedite this evaluation step of disease-causing genetic variations, here we developed Mutation@A Glance (http://rapid.rcai.riken.jp/mutation/), a highly integrated web-based analysis tool for analysing human disease mutations; it implements a user-friendly graphical interface to visualize about 40 000 known disease-associated mutations and genetic polymorphisms from more than 2600 protein-coding human disease-causing genes. Mutation@A Glance locates already known genetic variation data individually on the nucleotide and the amino acid sequences and makes it possible to cross-reference them with tertiary and/or quaternary protein structures and various functional features associated with specific amino acid residues in the proteins. We showed that the disease-associated missense mutations had a stronger tendency to reside in positions relevant to the structure/function of proteins than neutral genetic variations. From a practical viewpoint, Mutation@A Glance could certainly function as a ‘one-stop’ analysis platform for newly determined DNA sequences, which enables us to readily identify and evaluate new genetic variations by integrating multiple lines of information about the disease-causing candidate genes.     

Using multiple ontologies to integrate complex biological data

The strength of the rat as a model organism lies in its utility in pharmacology, biochemistry and physiology research. Data resulting from such studies is difficult to represent in databases and the creation of user-friendly data mining tools has proved difficult. The Rat Genome Database has developed a comprehensive ontology-based data structure and annotation system to integrate physiological data along with environmental and experimental factors, as well as genetic and genomic information. RGD uses multiple ontologies to integrate complex biological information from the molecular level to the whole organism, and to develop data mining and presentation tools. This approach allows RGD to indicate not only the phenotypes seen in a strain but also the specific values under each diet and atmospheric condition, as well as gender differences. Harnessing the power of ontologies in this way allows the user to gather and filter data in a customized fashion, so that a researcher can retrieve all phenotype readings for which a high hypoxia is a factor. Utilizing the same data structure for expression data, pathways and biological processes, RGD will provide a comprehensive research platform which allows users to investigate the conditions under which biological processes are altered and to elucidate the mechanisms of disease.     

GIGANTEA acts in blue light signaling and has biochemically separable roles in circadian clock and flowering time regulation

Circadian clocks are widespread in nature. In higher plants, they confer a selective advantage, providing information regarding not only time of day but also time of year. Forward genetic screens in Arabidopsis (Arabidopsis thaliana) have led to the identification of many clock components, but the functions of most of these genes remain obscure. To identify both new constituents of the circadian clock and new alleles of known clock-associated genes, we performed a mutant screen. Using a clock-regulated luciferase reporter, we isolated new alleles of ZEITLUPE, LATE ELONGATED HYPOCOTYL, and GIGANTEA (GI). GI has previously been reported to function in red light signaling, central clock function, and flowering time regulation. Characterization of this and other GI alleles has helped us to further define GI function in the circadian system. We found that GI acts in photomorphogenic and circadian blue light signaling pathways and is differentially required for clock function in constant red versus blue light. Gene expression and epistasis analyses show that TIMING OF CHLOROPHYLL A/B BINDING PROTEIN1 (TOC1) expression is not solely dependent upon GI and that GI expression is only indirectly affected by TOC1, suggesting that GI acts both in series with and in parallel to TOC1 within the central circadian oscillator. Finally, we found that the GI-dependent promotion of CONSTANS expression and flowering is intact in a gi mutant with altered circadian regulation. Thus GI function in the regulation of a clock output can be biochemically separated from its role within the circadian clock.     

A Simple Cell-Based Assay Reveals That Diverse Neuropsychiatric Risk Genes Converge on Primary Cilia

Human genetic studies are beginning to identify a large number of genes linked to neuropsychiatric disorders. It is increasingly evident that different genes contribute to risk for similar syndromes and, conversely, the same genes or even the same alleles cross over traditional diagnostic categories. A current challenge is to understand the cellular biology of identified risk genes. However, most genes associated with complex neuropsychiatric phenotypes are not related through a known biochemical pathway, and many have an entirely unknown cellular function. One possibility is that diverse disease-linked genes converge at a higher-level cellular structure. The synapse is already known to be one such convergence, and emerging evidence suggests the primary cilium as another. Because many genes associated with neuropsychiatric illness are expressed also outside the nervous system, as are cilia, we tested the hypothesis that such genes affect conserved features of the primary cilium. Using RNA interference to test 41 broadly expressed candidate genes associated with schizophrenia, bipolar affective disorder, autism spectrum disorder and intellectual disability, we found 20 candidates that reduce ciliation in NIH3T3 cells when knocked down, and three whose manipulation increases cilia length. Three of the candidate genes were previously implicated in cilia formation and, altogether, approximately half of the candidates tested produced a ciliary phenotype. Our results support the hypothesis that primary cilia indeed represent a conserved cellular structure at which the effects of diverse neuropsychiatric risk genes converge. More broadly, they suggest a relatively simple cell-based approach that may be useful for exploring the complex biological underpinnings of neuropsychiatric disease.     

Extragenic Suppressors of the Arabidopsis Det1 Mutant Identify Elements of Flowering-Time and Light-Response Regulatory Pathways

Light regulation of seedling morphogenesis is mediated by photoreceptors that perceive red, far-red, blue and UV light. Photomorphogenetic mutants of Arabidopsis have identified several of the primary photoreceptors, as well as a set of negative regulators of seedling photomorphogenesis, including DET1, that appear to act downstream of the photoreceptors. To study the regulatory context in which DET1 acts to repress photomorphogenesis, we used a simple morphological screen to isolate extragenic mutations in six loci, designated ted (for reversal of the det phenotype), that partially or fully suppress the seedling morphological phenotype of det1-1. Genetic analyses indicate that mutations in the ted4 and ted5 loci identify new alleles of the previously described photomorphogenetic loci hy1 and hy5, respectively. Molecular analyses indicate that the ted mutations partially suppress the dark-grown gene expression phenotype of det1-1, and that the mechanism of suppression does not involve direct remediation of the splicing defect caused by the det1-1 mutation. The ted mutations also partially suppress the light-grown morphological phenotype of mature det1-1 plants, and ted1 and ted2 suppress a daylength insensitivity phenotype of det1. TED1, TED2 and TED3 are newly described genes, whose function appears closely associated with that of DET1. In addition, alleles of ted1 are associated with a moderate late-flowering phenotype, suggesting that TED1 plays a role in the pathways that regulate both seedling morphogenesis and the initiation of flowering.     

Genetic interaction of BBS1 mutations with alleles at other BBS loci can result in non-Mendelian Bardet-Biedl syndrome

Bardet-Biedl syndrome is a genetically and clinically heterogeneous disorder caused by mutations in at least seven loci (BBS1-7), five of which are cloned (BBS1, BBS2, BBS4, BBS6, and BBS7). Genetic and mutational analyses have indicated that, in some families, a combination of three mutant alleles at two loci (triallelic inheritance) is necessary for pathogenesis. To date, four of the five known BBS loci have been implicated in this mode of oligogenic disease transmission. We present a comprehensive analysis of the spectrum, distribution, and involvement in non-Mendelian trait transmission of mutant alleles in BBS1, the most common BBS locus. Analyses of 259 independent families segregating a BBS phenotype indicate that BBS1 participates in complex inheritance and that, in different families, mutations in BBS1 can interact genetically with mutations at each of the other known BBS genes, as well as at unknown loci, to cause the phenotype. Consistent with this model, we identified homozygous M390R alleles, the most frequent BBS1 mutation, in asymptomatic individuals in two families. Moreover, our statistical analyses indicate that the prevalence of the M390R allele in the general population is consistent with an oligogenic rather than a recessive model of disease transmission. The distribution of BBS oligogenic alleles also indicates that all BBS loci might interact genetically with each other, but some genes, especially BBS2 and BBS6, are more likely to participate in triallelic inheritance, suggesting a variable ability of the BBS proteins to interact genetically with each other.     

Contribution of arylsulfatase A mutations located on the same allele to enzyme activity reduction and metachromatic leukodystrophy severity

The occurrence of different mutations on the same arylsulfatase A allele is not uncommon, due to the high frequency of several variants, among which the pseudodeficiency mutations are particularly important. We identified a late infantile metachromatic leukodystrophy patient carrying on one allele the new E253K mutation and the known T391S polymorphism, and on the other allele the common P426L mutation, usually associated with the adult or juvenile form of the disease, and the N350S and *96A>G pseudodeficiency mutations. To analyze the contribution of mutations located on the same allele to enzyme activity reduction, as well as the possible phenotype implications, we performed transient expression experiments using arylsulfatase A cDNAs carrying the identified mutations separately and in combination. Our results indicate that mutants containing multiple mutations cause a greater reduction of ARSA activity than do the corresponding single mutants, the total deficiency likely corresponding to the sum of the reductions attributed to each mutation. Consequently, each mutation may contribute to ARSA activity reduction, and, therefore, to the degree of disease severity. This is particularly important for the alleles containing a disease-causing mutation and the pseudodeficiency mutations: in these alleles pseudodeficiency could play a role in affecting the clinical phenotype.