Dual-specificity MAP kinase phosphatases in health and disease

It is well established that a family of dual-specificity MAP kinase phosphatases (MKPs) play key roles in the regulated dephosphorylation and inactivation of MAP kinase isoforms in mammalian cells and tissues. MKPs provide a mechanism of spatiotemporal feedback control of these key signalling pathways, but can also mediate crosstalk between distinct MAP kinase cascades and facilitate interactions between MAP kinase pathways and other key signalling modules. As our knowledge of the regulation, substrate specificity and catalytic mechanisms of MKPs has matured, more recent work using genetic models has revealed key physiological functions for MKPs and also uncovered potentially important roles in regulating the pathophysiological outcome of signalling with relevance to human diseases. These include cancer, diabetes, inflammatory and neurodegenerative disorders. It is hoped that this understanding will reveal novel therapeutic targets and biomarkers for disease, thus contributing to more effective diagnosis and treatment for these debilitating and often fatal conditions.     

Isolated populations and complex disease gene identification

The utility of genetically isolated populations (population isolates) in the mapping and identification of genes is not only limited to the study of rare diseases; isolated populations also provide a useful resource for studies aimed at improved understanding of the biology underlying common diseases and their component traits. Well characterized human populations provide excellent study samples for many different genetic investigations, ranging from genome-wide association studies to the characterization of interactions between genes and the environment.     

Progress and promise in understanding the genetic basis of common diseases

Susceptibility to common human diseases is influenced by both genetic and environmental factors. The explosive growth of genetic data, and the knowledge that it is generating, are transforming our biological understanding of these diseases. In this review, we describe the technological and analytical advances that have enabled genome-wide association studies to be successful in identifying a large number of genetic variants robustly associated with common disease. We examine the biological insights that these genetic associations are beginning to produce, from functional mechanisms involving individual genes to biological pathways linking associated genes, and the identification of functional annotations, some of which are cell-type-specific, enriched in disease associations. Although most efforts have focused on identifying and interpreting genetic variants that are irrefutably associated with disease, it is increasingly clear that—even at large sample sizes—these represent only the tip of the iceberg of genetic signal, motivating polygenic analyses that consider the effects of genetic variants throughout the genome, including modest effects that are not individually statistically significant. As data from an increasingly large number of diseases and traits are analysed, pleiotropic effects (defined as genetic loci affecting multiple phenotypes) can help integrate our biological understanding. Looking forward, the next generation of population-scale data resources, linking genomic information with health outcomes, will lead to another step-change in our ability to understand, and treat, common diseases.     

Genetic susceptibility in Parkinson's disease

It is hoped that an understanding of the genetic basis of Parkinson's disease (PD) will lead to an appreciation of the molecular pathogenesis of disease, which in turn will highlight potential points of therapeutic intervention. It is also hoped that such an understanding will allow identification of individuals at risk for disease prior to the onset of motor symptoms. A large amount of work has already been performed in the identification of genetic risk factors for PD and some of this work, particularly those efforts that focus on genes implicated in monogenic forms of PD, have been successful, although hard won. A new era of gene discovery has begun, with the application of genome wide association studies; these promise to facilitate the identification of common genetic risk loci for complex genetic diseases. This is the first of several high throughput technologies that promise to shed light on the (likely) myriad genetic factors involved in this complex, late-onset neurodegenerative disorder.     

Traversing the conceptual divide between biological and statistical epistasis: systems biology and a more modern synthesis

Epistasis plays an important role in the genetic architecture of common human diseases and can be viewed from two perspectives, biological and statistical, each derived from and leading to different assumptions and research strategies. Biological epistasis is the result of physical interactions among biomolecules within gene regulatory networks and biochemical pathways in an individual such that the effect of a gene on a phenotype is dependent on one or more other genes. In contrast, statistical epistasis is defined as deviation from additivity in a mathematical model summarizing the relationship between multilocus genotypes and phenotypic variation in a population. The goal of this essay is to review definitions and examples of biological and statistical epistasis and to explore the relationship between the two. Specifically, we present and discuss the following two questions in the context of human health and disease. First, when does statistical evidence of epistasis in human populations imply underlying biomolecular interactions in the etiology of disease? Second, when do biomolecular interactions produce patterns of statistical epistasis in human populations?Answers to these two reciprocal questions will provide an important framework for using genetic information to improve our ability to diagnose, prevent and treat common human diseases. We propose that systems biology will provide the necessary information for addressing these questions and that model systems such as bacteria, yeast and digital organisms will be a useful place to start. BioEssays 27:637–646, 2005. © 2005 Wiley Periodicals, Inc.     

The human genome project--some implications of extensive "reverse genetic" medicine

Impressive progress has been made during the past several decades in understanding the pathogenesis of human genetic disease. The tools of molecular biology have allowed the isolation of many disease-related genes by forward and a few by reverse genetics, and the imminent completion of a complete human genetic linkage map will accelerate the genetic characterization of many more genetic diseases. The major impacts of the molecular characterization of human genetic diseases will be 1. To increase markedly the number of human diseases that we recognize to have major genetic components. We already understand that genetic diseases are not rare medical curiosities with negligible societal impact, but rather constitute a wide spectrum of both rare and extremely common diseases responsible for an immense amount of suffering in all human societies. The characterization of the human genome will lead to the identification of genetic factors in many more human diseases, even those that now seem too multifactorial or polygenic for ready understanding. 2. To allow the development of powerful new approaches to diagnosis, detection, screening and even therapy of these disorders aimed directly at the mutant genes rather than at the gene products. This should eventually allow much more accurate and specific management of human genetic disease and the genetic factors in many human maladies. The preparation of a fine-structure physical map of the entire human genome together with an overlapping contiguous set of clones spanning entire chromosomes or large portions of chromosomes is rapidly becoming feasible, and the information that will flow from this effort promises eventually to affect the management of many important genetic diseases.(ABSTRACT TRUNCATED AT 250 WORDS)     

Potenzial und Herausforderungen der personalisierten Genomik und des 1000-Genom-Projekts

The ability to sequence entire individual human genomes has heralded a new era in human genetics. Such advances in sequencing technologies make it possible to address new questions such as the generation of a comprehensive map of common and rare genetic variants in humans. The 1000 Genome Project will analyze 2500 genomes and is expected to greatly expand our knowledge about genomic variation, both on single nucleotide polymorphisms and genomic structural variants in a number of human ethnic populations. Furthermore, the possibility to use these new sequencing technologies for such large scale projects will be evaluated. Finally, new bioinformatics solutions will be developed to efficiently store and process such large volumes of data for the scientific community. This catalogue of common and rare variations will facilitate the development of better methods for phenotype-genotype associations and help uncover the molecular bases for a variety of diseases in the near future.     

The derived FOXP2 variant of modern humans was shared with Neandertals

Although many animals communicate vocally, no extant creature rivals modern humans in language ability. Therefore, knowing when and under what evolutionary pressures our capacity for language evolved is of great interest. Here, we find that our closest extinct relatives, the Neandertals, share with modern humans two evolutionary changes in FOXP2, a gene that has been implicated in the development of speech and language. We furthermore find that in Neandertals, these changes lie on the common modern human haplotype, which previously was shown to have been subject to a selective sweep. These results suggest that these genetic changes and the selective sweep predate the common ancestor (which existed about 300,000-400,000 years ago) of modern human and Neandertal populations. This is in contrast to more recent age estimates of the selective sweep based on extant human diversity data. Thus, these results illustrate the usefulness of retrieving direct genetic information from ancient remains for understanding recent human evolution.     

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.     

The genetics of mammalian circadian order and disorder: implications for physiology and disease

Circadian cycles affect a variety of physiological processes, and disruptions of normal circadian biology therefore have the potential to influence a range of disease-related pathways. The genetic basis of circadian rhythms is well studied in model organisms and, more recently, studies of the genetic basis of circadian disorders has confirmed the conservation of key players in circadian biology from invertebrates to humans. In addition, important advances have been made in understanding how these molecules influence physiological functions in tissues throughout the body. Together, these studies set the scene for applying our knowledge of circadian biology to the understanding and treatment of a range of human diseases, including cancer and metabolic and behavioural disorders.     

Variation in recombination rate may bias human genetic disease mapping studies

The availability of the human genome sequence and variability information (as from the International HapMap project) will enhance our ability to map genetic disorders and choose targets for therapeutic intervention. However, several factors, such as regional variation in recombination rate, can bias conclusions from genetic mapping studies. Here, we examine the impact of regional variation in recombination rate across the human genome. Through computer simulations and literature surveys, we conclude that genetic disorders have been mapped to regions of low recombination more often than expected if such diseases were randomly distributed across the genome. This concentration in low recombination regions may be an artifact, and disorders appearing to be caused by a few genes of large effect may be polygenic. Future genetic mapping studies should be conscious of this potential complication by noting the regional recombination rate of regions implicated in diseases.     

The genome revolution and its role in understanding complex diseases

The completion of the human genome sequence in 2003 clearly marked the beginning of a new era for biomedical research. It spurred technological progress that was unprecedented in the life sciences, including the development of high-throughput technologies to detect genetic variation and gene expression. The study of genetics has become “big data science”. One of the current goals of genetic research is to use genomic information to further our understanding of common complex diseases. An essential first step made towards this goal was by the identification of thousands of single nucleotide polymorphisms showing robust association with hundreds of different traits and diseases. As insight into common genetic variation has expanded enormously and the technology to identify more rare variation has become available, we can utilize these advances to gain a better understanding of disease etiology. This will lead to developments in personalized medicine and P4 healthcare. Here, we review some of the historical events and perspectives before and after the completion of the human genome sequence. We also describe the success of large-scale genetic association studies and how these are expected to yield more insight into complex disorders. We show how we can now combine gene-oriented research and systems-based approaches to develop more complex models to help explain the etiology of common diseases. This article is part of a Special Issue entitled: From Genome to Function.     

多基因遗传病基因研究的策略和方法

基因在决定个体表型方面起着决定性的作用。虽然单基因疾病的致病基因的克隆工作取得了显著的进展,但对于多基因疾病来说,仍然存在许多问题,同时也是巨大的挑战。本文综述了多基因疾病的遗传特点和多基因疾病易感基因识别、分离和克隆的一般步骤和存在的问题,介绍了人类基因组计划在此过程中的作用和单核苷酸多态性的应用前景,提出 了最有可能克隆出多基因疾病易感基因的策略和方法。     

Oxygen sensing: applications in humans.

Our concepts of oxygen sensing have been transformed over the years. We now appreciate that oxygen sensing is not a unique property limited to "chemoreceptors" but is a common property of tissues and that responses to changes in oxygen levels are not static but can change over time. Respiratory responses initiated at the carotid body are modified by the excitatory and depressant effects of hypoxia at the brain and on the pathways connecting the carotid body to the brain. Equally important is that we are beginning to use our understanding of the cellular and molecular pathways triggered by hypoxia and hyperoxia to identify therapeutic targets to treat diseases such as cancer. We also have a better understanding of the complexities of the human respiratory responses to hypoxia; however, major deficiencies remain in our ability to alter or even measure human ventilatory responses to oxygen deficiency.     

Genetic modifiers of neurological disease

Genetic modifiers make an important contribution to neurological disease phenotypes. Significant progress has been made by studying genetic modifiers in model organisms. The ability to study complex genetic interactions in model systems contributes to our understanding of the genetic factors that influence neurological disease. This will lead to the development of novel therapeutic strategies and personalized treatment based on genetic risk.     

Role for protein–protein interaction databases in human genetics

Proteomics and the study of protein–protein interactions are becoming increasingly important in our effort to understand human diseases on a system-wide level. Thanks to the development and curation of protein-interaction databases, up-to-date information on these interaction networks is accessible and publicly available to the scientific community. As our knowledge of protein–protein interactions increases, it is important to give thought to the different ways that these resources can impact biomedical research. In this article, we highlight the importance of protein–protein interactions in human genetics and genetic epidemiology. Since protein–protein interactions demonstrate one of the strongest functional relationships between genes, combining genomic data with available proteomic data may provide us with a more in-depth understanding of common human diseases. In this review, we will discuss some of the fundamentals of protein interactions, the databases that are publicly available and how information from these databases can be used to facilitate genome-wide genetic studies.     

Gene-history correlation and population structure

Correlation of gene histories in the human genome determines the patterns of genetic variation (haplotype structure) and is crucial to understanding genetic factors in common diseases. We derive closed analytical expressions for the correlation of gene histories in established demographic models for genetic evolution and show how to extend the analysis to more realistic (but more complicated) models of demographic structure. We identify two contributions to the correlation of gene histories in divergent populations: linkage disequilibrium, and differences in the demographic history of individuals in the sample. These two factors contribute to correlations at different length scales: the former at small, and the latter at large scales. We show that recent mixing events in divergent populations limit the range of correlations and compare our findings to empirical results on the correlation of gene histories in the human genome.     

Unified views on variant impact across many diseases

Genomic studies of human disorders are often performed by distinct research communities (i.e., focused on rare diseases, common diseases, or cancer). Despite underlying differences in the mechanistic origin of different disease categories, these studies share the goal of identifying causal genomic events that are critical for the clinical manifestation of the disease phenotype. Moreover, these studies face common challenges, including understanding the complex genetic architecture of the disease, deciphering the impact of variants on multiple scales, and interpreting noncoding mutations. Here, we highlight these challenges in depth and argue that properly addressing them will require a more unified vocabulary and approach across disease communities. Toward this goal, we present a unified perspective on relating variant impact to various genomic disorders.     

Modelling neurodegenerative diseases in Drosophila: a fruitful approach?

Human neurodegenerative diseases are characterized by the progressive loss of specific neuronal populations, resulting in substantial disability and early death. The identification of causative single-gene mutations in families with inherited neurodegenerative disorders has facilitated the modelling of these diseases in experimental organisms, including the fruitfly Drosophila melanogaster. Many neurodegenerative diseases have now been successfully modelled in Drosophila, and genetic analysis is under way in each of these models. Using fruitfly genetics to define the molecular pathways that underlie the neurodegenerative process is likely to improve substantially our understanding of the pathogenesis of the human diseases, and to provide new therapeutic targets.     

Polygenic disease: methods for mapping complex disease traits

Improved genotyping technology has made it feasible to use a genetic approach to map genes involved in the etiology of common human diseases. We discuss here recent developments in several different statistical approaches to linkage analysis of these traits, including affected-sib-pair methods, the affected-pedigree-member method, regressive models and linkage-disequilibrium-based approaches. We discuss advantages and disadvantages of the various approaches, as well as factors influencing study design and the ability to detect loci. Statistical methodology in this area is advancing rapidly and will help enable the mapping and cloning of loci involved in susceptibility to common multifactorial diseases.