Common variants in mendelian kidney disease genes and their association with renal function.

Many common genetic variants identified by genome-wide association studies for complex traits map to genes previously linked to rare inherited Mendelian disorders. A systematic analysis of common single-nucleotide polymorphisms (SNPs) in genes responsible for Mendelian diseases with kidney phenotypes has not been performed. We thus developed a comprehensive database of genes for Mendelian kidney conditions and evaluated the association between common genetic variants within these genes and kidney function in the general population. Using the Online Mendelian Inheritance in Man database, we identified 731 unique disease entries related to specific renal search terms and confirmed a kidney phenotype in 218 of these entries, corresponding to mutations in 258 genes. We interrogated common SNPs (minor allele frequency >5%) within these genes for association with the estimated GFR in 74,354 European-ancestry participants from the CKDGen Consortium. However, the top four candidate SNPs (rs6433115 at LRP2, rs1050700 at TSC1, rs249942 at PALB2, and rs9827843 at ROBO2) did not achieve significance in a stage 2 meta-analysis performed in 56,246 additional independent individuals, indicating that these common SNPs are not associated with estimated GFR. The effect of less common or rare variants in these genes on kidney function in the general population and disease-specific cohorts requires further research.

Differences in the evolutionary history of disease genes affected by dominant or recessive mutations

Background: Global analyses of human disease genes by computational methods have yielded important advances in the understanding of human diseases. Generally these studies have treated the group of disease genes uniformly, thus ignoring the type of disease-causing mutations (dominant or recessive). In this report we present a comprehensive study of the evolutionary history of autosomal disease genes separated by mode of inheritance.Results: We examine differences in protein and coding sequence conservation between dominant and recessive human disease genes. Our analysis shows that disease genes affected by dominant mutations are more conserved than those affected by recessive mutations. This could be a consequence of the fact that recessive mutations remain hidden from selection while heterozygous. Furthermore, we employ functional annotation analysis and investigations into disease severity to support this hypothesis. Conclusion: This study elucidates important differences between dominantly- and recessively-acting disease genes in terms of protein and DNA sequence conservation, paralogy and essentiality. We propose that the division of disease genes by mode of inheritance will enhance both understanding of the disease process and prediction of candidate disease genes in the future.

Pseudoachondroplasia and multiple epiphyseal dysplasia: a 7-year comprehensive analysis of the known disease genes identify novel and recurrent mutations and provides an accurate assessment of their relative contribution.

Pseudoachondroplasia (PSACH) and multiple epiphyseal dysplasia (MED) are relatively common skeletal dysplasias resulting in short-limbed dwarfism, joint pain, and stiffness. PSACH and the largest proportion of autosomal dominant MED (AD-MED) results from mutations in cartilage oligomeric matrix protein (COMP); however, AD-MED is genetically heterogenous and can also result from mutations in matrilin-3 (MATN3) and type IX collagen (COL9A1, COL9A2, and COL9A3). In contrast, autosomal recessive MED (rMED) appears to result exclusively from mutations in sulphate transporter solute carrier family 26 (SLC26A2). The diagnosis of PSACH and MED can be difficult for the nonexpert due to various complications and similarities with other related diseases and often mutation analysis is requested to either confirm or exclude the diagnosis. Since 2003, the European Skeletal Dysplasia Network (ESDN) has used an on-line review system to efficiently diagnose cases referred to the network prior to mutation analysis. In this study, we present the molecular findings in 130 patients referred to ESDN, which includes the identification of novel and recurrent mutations in over 100 patients. Furthermore, this study provides the first indication of the relative contribution of each gene and confirms that they account for the majority of PSACH and MED.

The KMDB/MutationView: a mutation database for human disease genes

The KMDB/MutationView is a graphical database of mutations in human disease-causing genes and its current version consists of nine category-based sub-databases including diseases of eye, heart, ear, brain, cancer, syndrome, autoimmunity, muscle and blood. The KMDB/MutationView stores mutation data of 97 genes involved in 87 different disease and is accessible through http://mutview.dmb.med.keio.ac.jp.

Distinct gene-specific mechanisms of arrhythmia revealed by cardiac gene transfer of two long QT disease genes, HERG and KCNE1

The long QT syndrome (LQTS) is a heritable disorder that predisposes to sudden cardiac death. LQTS is caused by mutations in ion channel genes including HERG and KCNE1, but the precise mechanisms remain unclear. To clarify this situation we injected adenoviral vectors expressing wild-type or LQT mutants of HERG and KCNE1 into guinea pig myocardium. End points at 48–72 h included electrophysiology in isolated myocytes and electrocardiography in vivo. HERG increased the rapid component, IKr, of the delayed rectifier current, thereby accelerating repolarization, increasing refractoriness, and diminishing beat-to-beat action potential variability. Conversely, HERG-G628S suppressed IKr without significantly delaying repolarization. Nevertheless, HERG-G628S abbreviated refractoriness and increased beat-to-beat variability, leading to early afterdepolarizations (EADs). KCNE1 increased the slow component of the delayed rectifier, IKs, without clear phenotypic sequelae. In contrast, KCNE1-D76N suppressed IKs and markedly slowed repolarization, leading to frequent EADs and electrocardiographic QT prolongation. Thus, the two genes predispose to sudden death by distinct mechanisms: the KCNE1 mutant flagrantly undermines cardiac repolarization, and HERG-G628S subtly facilitates the genesis and propagation of premature beats. Our ability to produce electrocardiographic long QT in vivo with a clinical KCNE1 mutation demonstrates the utility of somatic gene transfer in creating genotype-specific disease models.

A rescue factor abolishing neuronal cell death by a wide spectrum of familial Alzheimer's disease genes and Aβ

Through functional expression screening, we identified a gene, designated Humanin (HN) cDNA, which encodes a short polypeptide and abolishes death of neuronal cells caused by multiple different types of familial Alzheimer's disease genes and by Aβ amyloid, without effect on death by Q79 or superoxide dismutase-1 mutants. Transfected HN cDNA was transcribed to the corresponding polypeptide and then was secreted into the cultured medium. The rescue action clearly depended on the primary structure of HN. This polypeptide would serve as a molecular clue for the development of new therapeutics for Alzheimer's disease targeting neuroprotection.

Identification of "pathologs" (disease-related genes) from the RIKEN mouse cDNA dataset using human curation plus FACTS, a new biological information extraction system

Background: A major goal in the post-genomic era is to identify and characterise disease susceptibility genes and to apply this knowledge to disease prevention and treatment. Rodents and humans have remarkably similar genomes and share closely related biochemical, physiological and pathological pathways. In this work we utilised the latest information on the mouse transcriptome as revealed by the RIKEN FANTOM2 project to identify novel human disease-related candidate genes. We define a new term patholog to mean a homolog of a human disease-related gene encoding a product ( transcript, anti-sense or protein) potentially relevant to disease. Rather than just focus on Mendelian inheritance, we applied the analysis to all potential pathologs regardless of their inheritance pattern. Results: Bioinformatic analysis and human curation of 60,770 RIKEN full-length mouse cDNA clones produced 2,578 sequences that showed similarity ( 70 - 85% identity) to known human-disease genes. Using a newly developed biological information extraction and annotation tool ( FACTS) in parallel with human expert analysis of 17,051 MEDLINE scientific abstracts we identified 182 novel potential pathologs. Of these, 36 were identified by computational tools only, 49 by human expert analysis only and 97 by both methods. These pathologs were related to neoplastic ( 53%), hereditary ( 24%), immunological ( 5%), cardio-vascular (4%), or other (14%), disorders. Conclusions: Large scale genome projects continue to produce a vast amount of data with potential application to the study of human disease. For this potential to be realised we need intelligent strategies for data categorisation and the ability to link sequence data with relevant literature. This paper demonstrates the power of combining human expert annotation with FACTS, a newly developed bioinformatics tool, to identify novel pathologs from within large-scale mouse transcript datasets.

Entropy Based Approach to Finding Interacting Genes Responsible for Complex Human Disease

2000 Mathematics Subject Classification: 62P10, 92D10, 92D30, 94A17, 62L10.

The prediction of disease risk in genomic medicine

In recent years, the phrase 'genomic medicine' has increasingly been used to describe a new development in medicine that holds great promise for human health. This new approach to health care uses the knowledge of an individual's genetic make-up to identify those that are at a higher risk of developing certain diseases and to intervene at an earlier stage to prevent these diseases. Identifying genes that are involved in disease aetiology will provide researchers with tools to develop better treatments and cures. A major role within this field is attributed to 'predictive genomic medicine', which proposes screening healthy individuals to identify those who carry alleles that increase their susceptibility to common diseases, such as cancers and heart disease. Physicians could then intervene even before the disease manifests and advise individuals with a higher genetic risk to change their behaviour - for instance, to exercise or to eat a healthier diet - or offer drugs or other medical treatment to reduce their chances of developing these diseases. These promises have fallen on fertile ground among politicians, health-care providers and the general public, particularly in light of the increasing costs of health care in developed societies. Various countries have established databases on the DNA and health information of whole populations as a first step towards genomic medicine. Biomedical research has also identified a large number of genes that could be used to predict someone's risk of developing a certain disorder. But it would be premature to assume that genomic medicine will soon become reality, as many problems remain to be solved. Our knowledge about most disease genes and their roles is far from sufficient to make reliable predictions about a patient’s risk of actually developing a disease. In addition, genomic medicine will create new political, social, ethical and economic challenges that will have to be addressed in the near future.

Genetics of Parkinson disease and essential tremor.

Purpose of review: Elucidating the genetic background of Parkinson disease and essential tremor is crucial to understand the pathogenesis and improve diagnostic and therapeutic strategies. Recent findings: A number of approaches have been applied including familial and association studies, and studies of gene expression profiles to identify genes involved in susceptibility to Parkinson disease. These studies have nominated a number of candidate Parkinson disease genes and novel loci including Omi/HtrA2, GIGYF2, FGF20, PDXK, EIF4G1 and PARK16. A recent notable finding has been the confirmation for the role of heterozygous mutations in glucocerebrosidase (GBA) as risk factors for Parkinson disease. Finally, association studies have nominated genetic variation in the leucine-rich repeat and Ig containing 1 gene (LINGO1) as a risk for both Parkinson disease and essential tremor, providing the first genetic evidence of a link between the two conditions. Summary: Although undoubtedly genes remain to be identified, considerable progress has been achieved in the understanding of the genetic basis of Parkinson disease. This same effort is now required for essential tremor. The use of next-generation high-throughput sequencing and genotyping technologies will help pave the way for future insight leading to advances in diagnosis, prevention and cure.

Mutational screening of splicing factor genes in cases with autosomal dominant retinitis pigmentosa.

PURPOSE: Mutations in genes encoding proteins from the tri-snRNP complex of the spliceosome account for more than 12% of cases of autosomal dominant retinitis pigmentosa (adRP). Although the exact mechanism by which splicing factor defects trigger photoreceptor death is not completely clear, their role in retinitis pigmentosa has been demonstrated by several genetic and functional studies. To test for possible novel associations between splicing factors and adRP, we screened four tri-snRNP splicing factor genes (EFTUD2, PRPF4, NHP2L1, and AAR2) as candidate disease genes. METHODS: We screened up to 303 patients with adRP from Europe and North America who did not carry known RP mutations. Exon-PCR and Sanger methods were used to sequence the NHP2L1 and AAR2 genes, while the sequences of EFTUD2 and PRPF4 were obtained by using long-range PCRs spanning coding and non-coding regions followed by next-generation sequencing. RESULTS: We detected novel missense changes in individual patients in the sequence of the genes PRPF4 and EFTUD2, but the role of these changes in relationship to disease could not be verified. In one other patient we identified a novel nucleotide substitution in the 5' untranslated region (UTR) of NHP2L1, which did not segregate with the disease in the family. CONCLUSIONS: The absence of clearly pathogenic mutations in the candidate genes screened in our cohort suggests that EFTUD2, PRPF4, NHP2L1, and AAR2 are either not involved in adRP or are associated with the disease in rare instances, at least as observed in this study in patients of European and North American origin.

Use of constant denaturant capillary electrophoresis of pooled blood samples to identify single-nucleotide polymorphisms in the genes (Scnn1a and Scnn1b) encoding the alpha and beta subunits of the epithelial sodium channel.

BACKGROUND: The epithelial sodium channel (ENaC) is composed of three homologous subunits: alpha, beta, and gamma. Mutations in the Scnn1b and Scnn1g genes, which encode the beta and the gamma subunits of ENaC, cause a severe form of hypertension (Liddle syndrome). The contribution of genetic variants within the Scnn1a gene, which codes for the alpha subunit, has not been investigated. METHODS: We screened for mutations in the COOH termini of the alpha and beta subunits of ENaC. Blood from 184 individuals from 31 families participating in a study on the genetics of hypertension were analyzed. Exons 13 of Scnn1a and Scnn1b, which encode the second transmembrane segment and the COOH termini of alpha- and beta-ENaC, respectively, were amplified from pooled DNA samples of members of each family by PCR. Constant denaturant capillary electrophoresis (CDCE) was used to detect mutations in PCR products of the pooled DNA samples. RESULTS: The detection limit of CDCE for ENaC variants was 1%, indicating that all members of any family or up to 100 individuals can be analyzed in one CDCE run. CDCE profiles of the COOH terminus of alpha-ENaC in pooled family members showed that the 31 families belonged to four groups and identified families with genetic variants. Using this approach, we analyzed 31 rather than 184 samples. Individual CDCE analysis of members from families with different pooled CDCE profiles revealed five genotypes containing 1853G-->T and 1987A-->G polymorphisms. The presence of the mutations was confirmed by DNA sequencing. For the COOH terminus of beta-ENaC, only one family showed a different CDCE profile. Two members of this family (n = 5) were heterozygous at 1781C-->T (T594M). CONCLUSION: CDCE rapidly detects point mutations in these candidate disease genes.

Disease-Gene Association Using a Genetic Algorithm

Understanding the relationship between genetic diseases and the genes associated with them is an important problem regarding human health. The vast amount of data created from a large number of high-throughput experiments performed in the last few years has resulted in an unprecedented growth in computational methods to tackle the disease gene association problem. Nowadays, it is clear that a genetic disease is not a consequence of a defect in a single gene. Instead, the disease phenotype is a reflection of various genetic components interacting in a complex network. In fact, genetic diseases, like any other phenotype, occur as a result of various genes working in sync with each other in a single or several biological module(s). Using a genetic algorithm, our method tries to evolve communities containing the set of potential disease genes likely to be involved in a given genetic disease. Having a set of known disease genes, we first obtain a protein-protein interaction (PPI) network containing all the known disease genes. All the other genes inside the procured PPI network are then considered as candidate disease genes as they lie in the vicinity of the known disease genes in the network. Our method attempts to find communities of potential disease genes strongly working with one another and with the set of known disease genes. As a proof of concept, we tested our approach on 16 breast cancer genes and 15 Parkinson's Disease genes. We obtained comparable or better results than CIPHER, ENDEAVOUR and GPEC, three of the most reliable and frequently used disease-gene ranking frameworks.

Disease-Gene Association Using Genetic Programming

As a result of mutation in genes, which is a simple change in our DNA, we will have undesirable phenotypes which are known as genetic diseases or disorders. These small changes, which happen frequently, can have extreme results. Understanding and identifying these changes and associating these mutated genes with genetic diseases can play an important role in our health, by making us able to find better diagnosis and therapeutic strategies for these genetic diseases. As a result of years of experiments, there is a vast amount of data regarding human genome and different genetic diseases that they still need to be processed properly to extract useful information. This work is an effort to analyze some useful datasets and to apply different techniques to associate genes with genetic diseases. Two genetic diseases were studied here: Parkinson’s disease and breast cancer. Using genetic programming, we analyzed the complex network around known disease genes of the aforementioned diseases, and based on that we generated a ranking for genes, based on their relevance to these diseases. In order to generate these rankings, centrality measures of all nodes in the complex network surrounding the known disease genes of the given genetic disease were calculated. Using genetic programming, all the nodes were assigned scores based on the similarity of their centrality measures to those of the known disease genes. Obtained results showed that this method is successful at finding these patterns in centrality measures and the highly ranked genes are worthy as good candidate disease genes for being studied. Using standard benchmark tests, we tested our approach against ENDEAVOUR and CIPHER - two well known disease gene ranking frameworks - and we obtained comparable results.

What is next after the genes for autoimmunity?

Clinical pathologies draw us to envisage disease as either an independent entity or a diverse set of traits governed by common physiopathological mechanisms, prompted by environmental assaults throughout life. Autoimmune diseases are not an exception, given they represent a diverse collection of diseases in terms of their demographic profile and primary clinical manifestations. Although they are pleiotropic outcomes of non-specific disease genes underlying similar immunogenetic mechanisms, research generally focuses on a single disease. Drastic technologic advances are leading research to organize clinical genomic multidisciplinary approaches to decipher the nature of human biological systems. Once the currently costly omic-based technologies become universally accessible, the way will be paved for a cleaner picture to risk quantification, prevention, prognosis and diagnosis, allowing us to clearly define better phenotypes always ensuring the integrity of the individuals studied. However, making accurate predictions for most autoimmune diseases is an ambitious challenge, since the understanding of these pathologies is far from complete. Herein, some pitfalls and challenges of the genetics of autoimmune diseases are reviewed, and an approximation to the future of research in this field is presented.