The ubiquitous nature of epistasis in determining susceptibility to common human diseases

There is increasing awareness that epistasis or gene-gene interaction plays a role in susceptibility to common human diseases. In this paper, we formulate a working hypothesis that epistasis is a ubiquitous component of the genetic architecture of common human diseases and that complex interactions are more important than the independent main effects of any one susceptibility gene. This working hypothesis is based on several bodies of evidence. First, the idea that epistasis is important is not new. In fact, the recognition that deviations from Mendelian ratios are due to interactions between genes has been around for nearly 100 years. Second, the ubiquity of biomolecular interactions in gene regulation and biochemical and metabolic systems suggest that relationship between DNA sequence variations and clinical endpoints is likely to involve gene-gene interactions. Third, positive results from studies of single polymorphisms typically do not replicate across independent samples. This is true for both linkage and association studies. Fourth, gene-gene interactions are commonly found when properly investigated. We review each of these points and then review an analytical strategy called multifactor dimensionality reduction for detecting epistasis. We end with ideas of how hypotheses about biological epistasis can be generated from statistical evidence using biochemical systems models. If this working hypothesis is true, it suggests that we need a research strategy for identifying common disease susceptibility genes that embraces, rather than ignores, the complexity of the genotype to phenotype relationship.     

Rational inferences about departures from Hardy-Weinberg equilibrium

Previous studies have explored the use of departure from Hardy-Weinberg equilibrium (DHW) for fine mapping Mendelian disorders and for general fine mapping. Other studies have used Hardy-Weinberg tests for genotyping quality control. To enable investigators to make rational decisions about whether DHW is due to genotyping error or to underlying biology, we developed an analytic framework and software to determine the parameter values for which DHW might be expected for common diseases. We show analytically that, for a general disease model, the difference between population and Hardy-Weinberg expected genotypic frequencies (delta) at the susceptibility locus is a function of the susceptibility-allele frequency (q), heterozygote relative risk (beta), and homozygote relative risk (gamma). For unaffected control samples, is a function of risk in nonsusceptible homozygotes (alpha), the population prevalence of disease (KP), q, beta, and gamma. We used these analytic functions to calculate and the number of cases or controls needed to detect DHW for a range of genetic models consistent with common diseases (1.1 < or = gamma < or = 10 and 0.005 < or = KP < or = 0.2). Results suggest that significant DHW can be expected in relatively small samples of patients over a range of genetic models. We also propose a goodness-of-fit test to aid investigators in determining whether a DHW observed in the context of a case-control study is consistent with a genetic disease model. We illustrate how the analytic framework and software can be used to help investigators interpret DHW in the context of association studies of common diseases.     

Human genetic susceptibility to infectious disease

Recent genome-wide studies have reported novel associations between common polymorphisms and susceptibility to many major infectious diseases in humans. In parallel, an increasing number of rare mutations underlying susceptibility to specific phenotypes of infectious disease have been described. Together, these developments have highlighted a key role for host genetic variation in determining the susceptibility to infectious disease. They have also provided insights into the genetic architecture of infectious disease susceptibility and identified immune molecules and pathways that are directly relevant to the human host defence.     

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.     

Genome-wide association studies: a new window into immune-mediated diseases

Given the recent explosion of genetic discoveries, 2007 is becoming known to human geneticists as the year of genome-wide association studies. In fact, more genetic risk factors for common diseases were identified in this one year than had been collectively reported before 2007. In particular, 2007 witnessed the discovery of many genes that influence susceptibility to individual immune-mediated diseases, as well as other genes that are associated with susceptibility to more than one disease. Although much work remains to be done, in this Review we discuss what effect these studies are having on our understanding of disease pathogenesis and their potential impact on future immunology studies.     

营养与病毒感染性疾病动物模型

当前新现病毒性疾病的研究最大的＂瓶颈＂在于没有胜任动物模型。建立在非人灵长类动物基础上的模型,虽然可以部分复制人类疾病特征,但其经济性欠佳且与动物权益的保护有所冲突;而啮齿类动物对新现病毒的易感性往往较低,也不能很好地复制人类疾病。本文对营养、免疫及疾病易感性关系研究的进展进行文献回顾,以发现解决当前难题的线索。     

Evolutionary determinants of genetic variation in susceptibility to infectious diseases in humans

Although genetic variation among humans in their susceptibility to infectious diseases has long been appreciated, little focus has been devoted to identifying patterns in levels of variation in susceptibility to different diseases. Levels of genetic variation in susceptibility associated with 40 human infectious diseases were assessed by a survey of studies on both pedigree-based quantitative variation, as well as studies on different classes of marker alleles. These estimates were correlated with pathogen traits, epidemiological characteristics, and effectiveness of the human immune response. The strongest predictors of levels of genetic variation in susceptibility were disease characteristics negatively associated with immune effectiveness. High levels of genetic variation were associated with diseases with long infectious periods and for which vaccine development attempts have been unsuccessful. These findings are consistent with predictions based on theoretical models incorporating fitness costs associated with the different types of resistance mechanisms. An appreciation of these observed patterns will be a valuable tool in directing future research given that genetic variation in disease susceptibility has large implications for vaccine development and epidemiology.     

Petri net modeling of high-order genetic systems using grammatical evolution

Understanding how DNA sequence variations impact human health through a hierarchy of biochemical and physiological systems is expected to improve the diagnosis, prevention, and treatment of common, complex human diseases. We have previously developed a hierarchical dynamic systems approach based on Petri nets for generating biochemical network models that are consistent with genetic models of disease susceptibility. This modeling approach uses an evolutionary computation approach called grammatical evolution as a search strategy for optimal Petri net models. We have previously demonstrated that this approach routinely identifies biochemical network models that are consistent with a variety of genetic models in which disease susceptibility is determined by nonlinear interactions between two DNA sequence variations. In the present study, we evaluate whether the Petri net approach is capable of identifying biochemical networks that are consistent with disease susceptibility due to higher order nonlinear interactions between three DNA sequence variations. The results indicate that our model-building approach is capable of routinely identifying good, but not perfect, Petri net models. Ideas for improving the algorithm for this high-dimensional problem are presented.     

Selecting SNPs for association studies based on population frequencies: a novel interactive tool and its application to polygenic diseases

Common complex polygenic diseases as autoimmune diseases have not been completely understood on a molecular level. While many genes are known to be involved in the pathways responsible for the phenotype, explicit causes for the susceptibility of the disease remain to be elucidated. The susceptibility to disease is thought to be the result of genetic epistatic interactions between common polymorphic genes. This polymorphism is mostly caused by single nucleotide polymorphisms (SNPs). Human subpopulations are known to differ in the susceptibility to the diseases and generally in the distribution of single nucleotide polymorphisms. The here presented approach retrieves SNPs with the most divergent frequencies for selected human subpopulations to help defining properties for the experimental verification of SNPs within defined regions. A web-accessible program implementing this approach was evaluated for multiple sclerosis (MS), a common human polygenic disease. A link to a summary of data from "The SNP Consortium" (TSC) with sex-dependencies of SNPs is available. Associations of SNPs to genes, genetic markers and chromosomal loci are retrieved from the Ensembl project. This tool is recommended to be used in conjunction with microarray analyses or marker association studies that link genes or chromosomal loci to particular diseases.     

Identifying host targets for drug development with knowledge from genome-wide studies: lessons from HIV-AIDS

Advances in human genomics are now being effectively applied to the search for host factors underlying susceptibility to common diseases. From the steady stream of studies showing association of host genetic factors with viral diseases, it has become clear that host factors contribute substantially to the variability of viral infections in humans. Candidate gene studies that seek to show associations between single-nucleotide polymorphisms (SNPs) with a disease outcome have predominated, but whole-genome association studies (GWAS) have recently appeared. A major goal of these studies is to understand how human genetic variation contributes to individual differences in susceptibility and to exploit this knowledge for targeted drug development.     

Pathway analysis of seven common diseases assessed by genome-wide association

Recent genome-wide association studies (GWAS) have identified DNA sequence variations that exhibit unequivocal statistical associations with many common chronic diseases. However, the vast majority of these studies identified variations that explain only a very small fraction of disease burden in the population at large, suggesting that other factors, such as multiple rare or low-penetrance variations and interacting environmental factors, are major contributors to disease susceptibility. Identifying multiple low-penetrance variations (or "polygenes") contributing to disease susceptibility will be difficult. We present a pathway analysis approach to characterizing the likely polygenic basis of seven common diseases using the Wellcome Trust Case Control Consortium (WTCCC) GWAS results. We identify numerous pathways implicated in disease predisposition that would have not been revealed using standard single-locus GWAS statistical analysis criteria. Many of these pathways have long been assumed to contain polymorphic genes that lead to disease predisposition. Additionally, we analyze the genetic relationships between the seven diseases, and based upon similarities with respect to the associated genes and pathways affected in each, propose a new way of categorizing the diseases.     

Genetic studies in rat models: insights into cardiovascular disease

PURPOSE OF REVIEW: Limited to 2003-2004 publications, this review focuses on 'big picture' concepts learned from rat genetic studies of cardiovascular disease. RECENT DEVELOPMENTS: Analysis reveals insights into pathogenic paradigms, as well as experimental perspectives into rat-based systems of analyses of complex cardiovascular disease. Key concepts are forwarded. Multiple susceptibility genes underlie several quantitative trait loci for blood pressure suggesting a 'quantitative trait loci cluster' concept; hypertension end-organ disease quantitative trait loci are distinct from blood pressure quantitative trait loci indicating differential susceptibility paradigms for hypertension and each complication (stroke, renal disease, cardiac hypertrophy); distinct blood pressure quantitative trait loci are found in males and females indicating gender-specific susceptibility; and genetic subtypes comprise polygenic hypertension in rat models suggesting a genetic basis for clinical heterogeneity of human essential hypertension. Gender specific genetic susceptibility plays a key role in coronary artery disease susceptibility; multiple distinct quantitative trait loci underlie hyperlipidemia and type-2 diabetes, indicating multiple susceptibilities in risk factors for cardiovascular disease. Studies in transgenic inbred rat-strain models demonstrate value for serial, complex, cardiovascular pathophysiological analyses within a genetic context. SUMMARY: Cognizant of the limitations of animal model studies, observations from rat genetic studies provide insight into respective modeled human cardiovascular diseases and risk factor susceptibility, as well as systematically dissect the multifaceted complexities apparent in human complex cardiovascular disease. Given the recapitulation of many features of human cardiovascular disease, the value of rat model-based genetic studies for complex cardiovascular disease is unequivocal, thus mandating the expansion of resources for maximization of rat-based genetic studies.     

The value of animal models in predicting genetic susceptibility to complex diseases such as rheumatoid arthritis

For a long time, genetic studies of complex diseases were most successfully conducted in animal models. However, the field of genetics is now rapidly evolving, and human genetics has also started to produce strong candidate genes for complex diseases. This raises the question of how to continue gene-finding attempts in animals and how to use animal models to enhance our understanding of gene function. In this review we summarize the uses and advantages of animal studies in identification of disease susceptibility genes, focusing on rheumatoid arthritis. We are convinced that animal genetics will remain a valuable tool for the identification and investigation of pathways that lead to disease, well into the future.     

Modelling cacao pod growth: implications for disease control

Cacao trees are affected by diseases that attack either their vegetative parts, their fruits or both. In cacao pod diseases, several factors are involved in disease susceptibility, such as the fruiting cycle, fruit size, age, position on the tree and cacao genotype. To gain a clearer understanding of how these characteristics influence cacao pod diseases, four models describing pod growth in several cacao genotypes were evaluated. Three models used to estimate pod volume or surface area were also compared. Observed pod growth was of a sigmoid form and fitted best to the Richards model, well to the Logistic and Beta growth models, and least to the Gompertz model. Pod volume and probably pod surface area were best estimated using a prolate spheroid model. Pod growth models can help improve pod disease management and thereby cacao production. They can help to predict yield, as well as provide information for the timing and frequency of control operations. Information on pod size, surface area and susceptibility will help to improve dose transfer and spray deposit studies intended to optimise control efficiency.     

Why genetic selection to reduce the prevalence of infectious diseases is way more promising than currently believed

Genetic selection for improved disease resistance is an important part of strategies to combat infectious diseases in agriculture. Quantitative genetic analyses of binary disease status, however, indicate low heritability for most diseases, which restricts the rate of genetic reduction in disease prevalence. Moreover, the common liability threshold model suggests that eradication of an infectious disease via genetic selection is impossible because the observed-scale heritability goes to zero when the prevalence approaches zero. From infectious disease epidemiology, however, we know that eradication of infectious diseases is possible, both in theory and practice, because of positive feedback mechanisms leading to the phenomenon known as herd immunity. The common quantitative genetic models, however, ignore these feedback mechanisms. Here, we integrate quantitative genetic analysis of binary disease status with epidemiological models of transmission, aiming to identify the potential response to selection for reducing the prevalence of endemic infectious diseases. The results show that typical heritability values of binary disease status correspond to a very substantial genetic variation in disease susceptibility among individuals. Moreover, our results show that eradication of infectious diseases by genetic selection is possible in principle. These findings strongly disagree with predictions based on common quantitative genetic models, which ignore the positive feedback effects that occur when reducing the transmission of infectious diseases. Those feedback effects are a specific kind of Indirect Genetic Effects; they contribute substantially to the response to selection and the development of herd immunity (i.e., an effective reproduction ratio less than one).     

Differential susceptibility to actively induced experimental allergic encephalomyelitis and experimental allergic orchitis among BALB/c substrains

Experimental allergic orchitis (EAO) and experimental allergic encephalomyelitis (EAE) are animal models of organ-specific autoimmune disease. In this study, BALB/cByJ and BALB/cAnNCr mice were susceptible to both autoimmune diseases whereas BALB/cJ subline mice were resistant. Disease resistance in BALB/cJ mice did not appear to be a reflection of either (i) a nonspecific generalized impairment of cellular immunity or (ii) an alteration in the phenotypic expression of Bordetella pertussis-induced histamine sensitization, a phenotype which has been shown to be associated with susceptibility to both diseases. Susceptibility to both EAE and EAO was inherited as a dominant trait in F1 hybrid animals. Segregation analysis in a (BALB/cByJ X BALB/cJ) X BALB/cJ backcross population suggested that disease resistance may be associated with a single genotypic difference in a common regulatory gene affecting susceptibility to both diseases. Linkage analysis of the backcross population failed to demonstrate an association of disease resistance with the mutant raf-1b allele carried by BALB/cJ mice. The results of these studies support previous observations that multiple genotypic differences may in fact exist in mice of the BALB/cJ subline and that such differences play a significant role in the genetic control of susceptibility to EAE and EAO.     

Mouse models of type 1 and type 2 diabetes derived from the same closed colony: genetic susceptibility shared between two types of diabetes

Except for rare subtypes of diabetes, both type 1 and type 2 diabetes are multifactorial diseases in which genetic factors consisting of multiple susceptibility genes and environmental factors contribute to the disease development. Due to complex interaction among multiple susceptibility genes and between genetic and environmental factors, genetic analysis of multifactorial diseases is difficult in humans. Inbred animal models, in which the genetic background is homogeneous and environmental factors can be controlled, are therefore valuable in genetic dissection of multifactorial diseases. We are fortunate to have excellent animal models for both type 1 and type 2 diabetes--the nonobese diabetic (NOD) mouse and the Nagoya-Shibata-Yasuda (NSY) mouse, respectively. Congenic mapping of susceptibility genes for type 1 diabetes in the NOD mouse has revealed that susceptibility initially mapped as a single locus often consists of multiple components on the same chromosome, indicating the importance of congenic mapping in defining genes responsible for polygenic diseases. The NSY mouse is an inbred animal model of type 2 diabetes established from Jcl:ICR, from which the NOD mouse was also derived. We have recently mapped three major loci contributing to type 2 diabetes in the NSY mouse. Interestingly, support intervals where type 2 diabetes susceptibility genes were mapped in the NSY mouse overlapped the regions where type 1 diabetes susceptibility genes have been mapped in the NOD mouse. Although additional evidence is needed, it may be possible that some of the genes predisposing to diabetes are derived from a common ancestor contained in the original closed colony, contributing to type 1 diabetes in the NOD mouse and type 2 diabetes in the NSY mouse. Such genes, if they exist, will provide valuable information on etiological pathways common to both forms of diabetes, for the establishment of effective methods for prediction, prevention, and intervention in both type 1 and type 2 diabetes.     

Regulatory SNPs in complex diseases: their identification and functional validation

Finding the genetic causes for complex diseases is a challenge. Expression studies have shown that the level of expression of many genes is altered in disease compared with normal conditions, but what lies behind these changes? Linkage studies provide hints as to where in the genome the genetic triggers--the mutations--might be located. Fine-mapping and association studies can give yet more information about which genes, and which changes in the genes, are involved in the disease. Recent examples show that single-nucleotide polymorphisms (SNPs), which are variations at the single-nucleotide level within an individual's DNA, in the regulatory regions of some genes constitute susceptibility factors in many complex diseases. This article discusses the nature of regulatory SNPs (rSNPs) and techniques for their functional validation, and looks towards what rSNPs can tell us about complex diseases.     

Simulations provide support for the common disease-common variant hypothesis

The success of mapping genes involved in complex diseases, using association or linkage disequilibrium methods, depends heavily on the number and frequency of susceptibility alleles of these genes. These methods will be economically and statistically feasible if common diseases are usually influenced by one or a few susceptibility alleles at each locus (common disease-common variant, CDCV, hypothesis), but not so if there is a high degree of allelic heterogeneity. Here, we use forward-time population simulations to investigate the impact of various genetic and demographic factors on the allelic spectra of human diseases, on the basis of two models proposed by Reich and Lander and by Pritchard. Factors considered are more complex demographies, a finite-allele mutation model, population structure and migration, and interaction between disease susceptibility loci. The conclusion is that the CDCV hypothesis holds and that the phenomenon is caused by transient effects of demography (population expansion). As a result, we devise a multilocus generalization of the Reich and Lander model and demonstrate how interaction between loci with respect to their response to selection may lead to complex effects. We discuss the implications for mapping of complex diseases.     

Prediction of individual genetic risk of complex disease

Most common diseases are caused by multiple genetic and environmental factors. In the last 2 years, genome-wide association studies (GWAS) have identified polymorphisms that are associated with risk to common disease, but the effect of any one risk allele is typically small. By combining information from many risk variants, will it be possible to predict accurately each individual person's genetic risk for a disease? In this review we consider the lessons from GWAS and the implications for genetic risk prediction to common disease. We conclude that with larger GWAS sample sizes or by combining studies, accurate prediction of genetic risk will be possible, even if the causal mutations or the mechanisms by which they affect susceptibility are unknown.