Genetik intermediärer Phänotypen

The development of complex diseases such as coronary heart disease and diabetes mellitus is influenced by numerous genes. However, the contribution of a single gene is relatively small. The identification of genetic variants associated with complex diseases therefore requires large efforts and well-characterized groups of patients and controls. Alternatively, investigation of intermediate phenotypes instead of these complex endpoints seems to be more promising. An intermediate phenotype is usually already well known to be associated with the investigated disease, is heritable, and represents one aspect among others in the pathogenesis of the complex disease. This results in an accentuation of the phenotype and reduction of genetic heterogeneity. Investigating the genetics of the intermediate phenotype instead of the genetics of the end phenotype allows elucidation of this aspect of the disease. Optimal intermediate phenotypes are quantitative, easy-to-measure biochemical parameters. This results in an increased statistical power in contrast to qualitative phenotypes.     

Network‐based global inference of human disease genes

Deciphering the genetic basis of human diseases is an important goal of biomedical research. On the basis of the assumption that phenotypically similar diseases are caused by functionally related genes, we propose a computational framework that integrates human protein–protein interactions, disease phenotype similarities, and known gene–phenotype associations to capture the complex relationships between phenotypes and genotypes. We develop a tool named CIPHER to predict and prioritize disease genes, and we show that the global concordance between the human protein network and the phenotype network reliably predicts disease genes. Our method is applicable to genetically uncharacterized phenotypes, effective in the genome‐wide scan of disease genes, and also extendable to explore gene cooperativity in complex diseases. The predicted genetic landscape of over 1000 human phenotypes, which reveals the global modular organization of phenotype–genotype relationships. The genome‐wide prioritization of candidate genes for over 5000 human phenotypes, including those with under‐characterized disease loci or even those lacking known association, is publicly released to facilitate future discovery of disease genes.     

Co-clustering phenome�Cgenome for phenotype classification and disease gene discovery

Understanding the categorization of human diseases is critical for reliably identifying disease causal genes. Recently, genome-wide studies of abnormal chromosomal locations related to diseases have mapped >2000 phenotype–gene relations, which provide valuable information for classifying diseases and identifying candidate genes as drug targets. In this article, a regularized non-negative matrix tri-factorization (R-NMTF) algorithm is introduced to co-cluster phenotypes and genes, and simultaneously detect associations between the detected phenotype clusters and gene clusters. The R-NMTF algorithm factorizes the phenotype–gene association matrix under the prior knowledge from phenotype similarity network and protein–protein interaction network, supervised by the label information from known disease classes and biological pathways. In the experiments on disease phenotype–gene associations in OMIM and KEGG disease pathways, R-NMTF significantly improved the classification of disease phenotypes and disease pathway genes compared with support vector machines and Label Propagation in cross-validation on the annotated phenotypes and genes. The newly predicted phenotypes in each disease class are highly consistent with human phenotype ontology annotations. The roles of the new member genes in the disease pathways are examined and validated in the protein–protein interaction subnetworks. Extensive literature review also confirmed many new members of the disease classes and pathways as well as the predicted associations between disease phenotype classes and pathways.     

New Roads to FA/BRCA Pathway: H2AX

We have recently described an involvement of H2AX into the Fanconi anemia (FA) BRCA pathway through recruitment of FA protein FANCD2 to the sites of stalled replication forks. We showed that BRCA1 mediates the recruitment of FANCD2 by γH2AX to damaged chromatin and cells deficient or depleted of H2AX exhibit an FA-like phenotype, including an excess of chromatid-type chromosomal aberrations and hypersensitivity to MMC. Here, we discuss a model for the FA pathway and how it could partially explain the common phenotypes of H2AX, BRCA2 and FA deficiencies.     

From genotype to phenotype: genetics and medical practice in the new millennium

The completion of the human genome project will provide a vast amount of information about human genetic diversity. One of the major challenges for the medical sciences will be to relate genotype to phenotype. Over recent years considerable progress has been made in relating the molecular pathology of monogenic diseases to the associated clinical phenotypes. Studies of the inherited disorders of haemoglobin, notably the thalassaemias, have shown how even in these, the simplest of monogenic diseases, there is remarkable complexity with respect to their phenotypic expression. Although studies of other monogenic diseases are less far advanced, it is clear that the same level of complexity will exist. This information provides some indication of the difficulties that will be met when trying to define the genes that are involved in common multigenic disorders and, in particular, in trying to relate disease phenotypes to the complex interactions between many genes and multiple environmental factors.     

Genome shuffling: Progress and applications for phenotype improvement

Although rational method and global technique have been successfully applied in strain improvement respectively, the demand for engineering complex phenotypes required combinatorial approach. The technology of genome shuffling has been presented as a novel whole genome engineering approach for the rapid improvement of cellular phenotypes. This approach using recursive protoplast fusion with multi-parental strains offers the advantage of recombination throughout the entire genome without the necessity for genome sequence data or network information. Genome shuffling has been demonstrated as an effective method, which is not only for producing improved strain but also for providing information on complex phenotype. In this review we attempt to present the advantage of genome shuffling, introduce the procedure of this technology, summarize the applications of this approach for phenotype improvement and then give perspective on the development of this method in the future.     

Linkage studies on Gilles de la Tourette syndrome: what is the strategy of choice?

For a linkage study it is important to ascertain family material that is sufficiently informative. The statistical power of a linkage sample can be determined via computer simulation. For complex traits uncertain parameters such as incomplete penetrance, frequency of phenocopies, gene frequency and variable expression have to be taken into account. One can either include only the most severe phenotype in the analysis or apply multiple linkage tests for a gradually broadened disease phenotype. Gilles de la Tourette syndrome (GTS) is a chronic neurological disorder characterized by multiple, intermittent motor and vocal tics. Segregation analyses suggest that GTS and milder phenotypes are caused by a single dominant gene. We report here the results of an extensive simulation study on a large set of families. We compared the effectiveness of linkage tests with only the GTS phenotype versus multiple tests that included various milder phenotypes and different gene frequencies. The scenario of multiple tests yielded superior power. Our results show that computer simulation can indicate the strategy of choice in linkage studies of multiple, complex phenotypes.     

Genetic Association Analysis of Complex Diseases Incorporating Intermediate Phenotype Information

Genetic researchers often collect disease related quantitative traits in addition to disease status because they are interested in understanding the pathophysiology of disease processes. In genome-wide association (GWA) studies, these quantitative phenotypes may be relevant to disease development and serve as intermediate phenotypes or they could be behavioral or other risk factors that predict disease risk. Statistical tests combining both disease status and quantitative risk factors should be more powerful than case-control studies, as the former incorporates more information about the disease. In this paper, we proposed a modified inverse-variance weighted meta-analysis method to combine disease status and quantitative intermediate phenotype information. The simulation results showed that when an intermediate phenotype was available, the inverse-variance weighted method had more power than did a case-control study of complex diseases, especially in identifying susceptibility loci having minor effects. We further applied this modified meta-analysis to a study of imputed lung cancer genotypes with smoking data in 1154 cases and 1137 matched controls. The most significant SNPs came from the CHRNA3-CHRNA5-CHRNB4 region on chromosome 15q24–25.1, which has been replicated in many other studies. Our results confirm that this CHRNA region is associated with both lung cancer development and smoking behavior. We also detected three significant SNPs—rs1800469, rs1982072, and rs2241714—in the promoter region of the TGFB1 gene on chromosome 19 (p = 1.46×10⁻⁵, 1.18×10⁻⁵, and 6.57×10⁻⁶, respectively). The SNP rs1800469 is reported to be associated with chronic obstructive pulmonary disease and lung cancer in cigarette smokers. The present study is the first GWA study to replicate this result. Signals in the 3q26 region were also identified in the meta-analysis. We demonstrate the intermediate phenotype can potentially enhance the power of complex disease association analysis and the modified meta-analysis method is robust to incorporate intermediate phenotype or other quantitative risk factor in the analysis.     

Accelerated discovery of functional genomic variation in pigs

The genotype-phenotype link is a major research topic in the life sciences but remains highly complex to disentangle. Part of the complexity arises from the number of genes contributing to the observed phenotype. Despite the vast increase of molecular data, pinpointing the causal variant underlying a phenotype of interest is still challenging. In this study, we present an approach to map causal variation and molecular pathways underlying important phenotypes in pigs. We prioritize variation by utilizing and integrating predicted variant impact scores (pCADD), functional genomic information, and associated phenotypes in other mammalian species. We demonstrate the efficacy of our approach by reporting known and novel causal variants, of which many affect non-coding sequences. Our approach allows the disentangling of the biology behind important phenotypes by accelerating the discovery of novel causal variants and molecular mechanisms affecting important phenotypes in pigs. This information on molecular mechanisms could be applicable in other mammalian species, including humans.     

Pathway-Dependent Effectiveness of Network Algorithms for Gene Prioritization

A network-based approach has proven useful for the identification of novel genes associated with complex phenotypes, including human diseases. Because network-based gene prioritization algorithms are based on propagating information of known phenotype-associated genes through networks, the pathway structure of each phenotype might significantly affect the effectiveness of algorithms. We systematically compared two popular network algorithms with distinct mechanisms – direct neighborhood which propagates information to only direct network neighbors, and network diffusion which diffuses information throughout the entire network – in prioritization of genes for worm and human phenotypes. Previous studies reported that network diffusion generally outperforms direct neighborhood for human diseases. Although prioritization power is generally measured for all ranked genes, only the top candidates are significant for subsequent functional analysis. We found that high prioritizing power of a network algorithm for all genes cannot guarantee successful prioritization of top ranked candidates for a given phenotype. Indeed, the majority of the phenotypes that were more efficiently prioritized by network diffusion showed higher prioritizing power for top candidates by direct neighborhood. We also found that connectivity among pathway genes for each phenotype largely determines which network algorithm is more effective, suggesting that the network algorithm used for each phenotype should be chosen with consideration of pathway gene connectivity.     

Selective Genotyping and Phenotyping Strategies in a Complex Trait Context

Selective genotyping and phenotyping strategies are used to lower the cost of quantitative trait locus studies. Their efficiency has been studied primarily in simplified contexts—when a single locus contributes to the phenotype, and when the residual error (phenotype conditional on the genotype) is normally distributed. It is unclear how these strategies will perform in the context of complex traits where multiple loci, possibly linked or epistatic, may contribute to the trait. We also do not know what genotyping strategies should be used for nonnormally distributed phenotypes. For time-to-event phenotypes there is the additional question of choosing follow-up time duration. We use an information perspective to examine these experimental design issues in the broader context of complex traits and make recommendations on their use.     

The value of high quality protein-protein interaction networks for systems biology

Protein-protein interaction (PPI) networks contain a large amount of useful information for the functional characterization of proteins and promote the understanding of the complex molecular relationships that determine the phenotype of a cell. Recently, large human interaction maps have been generated with high throughput technologies such as the yeast two-hybrid system. However, they are static and incomplete and do not provide immediate clues about the cellular processes that convert genetic information into complex phenotypes. Refined multiple-aspect PPI screening and confirmation strategies will have to be put in place to increase the validity of interaction maps. Integration of interaction data with other qualitative and quantitative information (e.g. protein expression or localization data), will be required to construct networks of protein function that reflect dynamic processes in the cell. In this way, combined PPI networks can become valuable resources for a systems-level understanding of cellular processes and complex phenotypes.     

Molecular biology of Fanconi anaemia--an old problem,a new insight

Fanconi anaemia (FA) comprises a group of autosomal recessive disorders resulting from mutations in one of eight genes (FANCA, FANCB, FANCC, FANCD1, FANCD2, FANCE, FANCF and FANCG). Although caused by relatively simple mutations, the disease shows a complex phenotype, with a variety of features including developmental abnormalities and ultimately severe anaemia and/or leukemia leading to death in the mid teens. Since 1992 all but two of the genes have been identified, and molecular analysis of their products has revealed a complex mode of action. Many of the proteins form a nuclear multisubunit complex that appears to be involved in the repair of double-strand DNA breaks. Additionally, at least one of the proteins, FANCC, influences apoptotic pathways in response to oxidative damage. Further analysis of the FANC proteins will provide vital information on normal cell responses to damage and allow therapeutic strategies to be developed that will hopefully supplant bone marrow transplantation.     

Epigenetic Alterations in Fanconi Anaemia: Role in Pathophysiology and Therapeutic Potential

Fanconi anaemia (FA) is an inherited disorder characterized by chromosomal instability. The phenotype is variable, which raises the possibility that it may be affected by other factors, such as epigenetic modifications. These play an important role in oncogenesis and may be pharmacologically manipulated. Our aim was to explore whether the epigenetic profiles in FA differ from non-FA individuals and whether these could be manipulated to alter the disease phenotype. We compared expression of epigenetic genes and DNA methylation profile of tumour suppressor genes between FA and normal samples. FA samples exhibited decreased expression levels of genes involved in epigenetic regulation and hypomethylation in the promoter regions of tumour suppressor genes. Treatment of FA cells with histone deacetylase inhibitor Vorinostat increased the expression of DNM3Tβ and reduced the levels of CIITA and HDAC9, PAK1, USP16, all involved in different aspects of epigenetic and immune regulation. Given the ability of Vorinostat to modulate epigenetic genes in FA patients, we investigated its functional effects on the FA phenotype. This was assessed by incubating FA cells with Vorinostat and quantifying chromosomal breaks induced by DNA cross-linking agents. Treatment of FA cells with Vorinostat resulted in a significant reduction of aberrant cells (81% on average). Our results suggest that epigenetic mechanisms may play a role in oncogenesis in FA. Epigenetic agents may be helpful in improving the phenotype of FA patients, potentially reducing tumour incidence in this population.     

Metagenomics and personalized medicine

The microbiome is a complex community of Bacteria, Archaea, Eukarya, and viruses that infect humans and live in our tissues. It contributes the majority of genetic information to our metagenome and, consequently, influences our resistance and susceptibility to diseases, especially common inflammatory diseases, such as type 1 diabetes, ulcerative colitis, and Crohn's disease. Here we discuss how host-gene-microbial interactions are major determinants for the development of these multifactorial chronic disorders and, thus, for the relationship between genotype and phenotype. We also explore how genome-wide association studies (GWAS) on autoimmune and inflammatory diseases are uncovering mechanism-based subtypes for these disorders. Applying these emerging concepts will permit a more complete understanding of the etiologies of complex diseases and underpin the development of both next-generation animal models and new therapeutic strategies for targeting personalized disease phenotypes.     

Complex Genetic Architecture of Cardiac Disease in a Wild Type Inbred Strain of Drosophila melanogaster

Natural populations of the fruit fly, Drosophila melanogaster, segregate genetic variation that leads to cardiac disease phenotypes. One nearly isogenic line from a North Carolina peach orchard, WE70, is shown to harbor two genetically distinct heart phenotypes: elevated incidence of arrhythmias, and a dramatically constricted heart diameter in both diastole and systole, with resemblance to restrictive cardiomyopathy in humans. Assuming the source to be rare variants of large effect, we performed Bulked Segregant Analysis using genomic DNA hybridization to Affymetrix chips to detect single feature polymorphisms, but found that the mutant phenotypes are more likely to have a polygenic basis. Further mapping efforts revealed a complex architecture wherein the constricted cardiomyopathy phenotype was observed in individual whole chromosome substitution lines, implying that variants on both major autosomes are sufficient to produce the phenotype. A panel of 170 Recombinant Inbred Lines (RIL) was generated, and a small subset of mutant lines selected, but these each complemented both whole chromosome substitutions, implying a non-additive (epistatic) contribution to the “disease” phenotype. Low coverage whole genome sequencing was also used to attempt to map chromosomal regions contributing to both the cardiomyopathy and arrhythmia, but a polygenic architecture had to be again inferred to be most likely. These results show that an apparently simple rare phenotype can have a complex genetic basis that would be refractory to mapping by deep sequencing in pedigrees. We present this as a cautionary tale regarding assumptions related to attempts to map new disease mutations on the assumption that probands carry a single causal mutation.     

From clinical description,to in vitro and animal studies,and backward to patients: Oxidative stress and mitochondrial dysfunction in Fanconi anemia

Fanconi anemia (FA) is a rare genetic disease associated with deficiencies in DNA repair pathways. A body of literature points to a pro-oxidant state in FA patients, along with evidence for oxidative stress (OS) in the FA phenotype reported by in vitro, molecular, and animal studies. A highlight arises from the detection of mitochondrial dysfunction (MDF) in FA cell lines of complementation groups A, C, D2, and G. As yet lacking, in vivo studies should focus on FA-associated MDF, which may help in the understanding of the mitochondrial basis of OS detected in cells and body fluids from FA patients. Beyond the in vitro and animal databases, the available analytical devices may prompt the direct observation of metabolic and mitochondrial alterations in FA patients. These studies should evaluate a set of MDF-related endpoints, to be related to OS endpoints. The working hypothesis is raised that, parallel to OS, nitrosative stress might be another, so far unexplored, hallmark of the FA phenotype. The expected results may shed light on the FA pathogenesis and might provide grounds for pilot chemoprevention trials using mitochondrial nutrients.     

The Fanconi anemia core complex forms four complexes of different sizes in different subcellular compartments

Fanconi anemia (FA) is an autosomal recessive disease marked by congenital defects, bone marrow failure, and cancer susceptibility. FA cells exhibit a characteristic hypersensitivity to DNA crosslinking agents such as mitomycin C. The molecular mechanism for the disease remains elusive, but at least 6 FA proteins are known to be part of what is termed the FA core complex. We used affinity pulldown of FLAG-FANCA to pull down the FA complex from whole-cell extracts. Mass spectroscopy detected previously reported FA-binding proteins, including FANCA, FANCC, FANCG, cdc2, and GRP94, thus validating the approach. We further describe a method of purification of the FA core complex in an effort to find novel complex components and biochemical activity to define the function of the complex. By using conventional chromatographic fractionation of subcellular preparations, we report: (i) the FA core complex exists in a cytoplasmic form at 500-600 kDa; (ii) a larger, 750-kDa cytoplasmic form is seen only at mitosis; (iii) a nuclear form achieves a size of 2 megaDaltons; and (iv) a distinct 1-megaDalton FA core complex exists bound to chromatin that contains phosphorylated FANCA after undergoing DNA damage. We are continuing our analysis using mass spectroscopy in an effort to characterize novel binding proteins. These data will help define the biochemical role of the FA core complex in normal cell physiology as well as in the development of the FA disease state.     

Integrated analysis of genetic, genomic and proteomic data

The rapid expansion of methods for measuring biological data ranging from DNA sequence variations to mRNA expression and protein abundance presents the opportunity to utilize multiple types of information jointly in the study of human health and disease. Organisms are complex systems that integrate inputs at myriad levels to arrive at an observable phenotype. Therefore, it is essential that questions concerning the etiology of phenotypes as complex as common human diseases take the systemic nature of biology into account, and integrate the information provided by each data type in a manner analogous to the operation of the body itself. While limited in scope, the initial forays into the joint analysis of multiple data types have yielded interesting results that would not have been reached had only one type of data been considered. These early successes, along with the aforementioned theoretical appeal of data integration, provide impetus for the development of methods for the parallel, high-throughput analysis of multiple data types. The idea that the integrated analysis of multiple data types will improve the identification of biomarkers of clinical endpoints, such as disease susceptibility, is presented as a working hypothesis.