Exploiting interactions among polymorphisms contributing to complex disease traits with boosted generative modeling.

Although there has been great success in identifying disease genes for simple, monogenic Mendelian traits, deciphering the genetic mechanisms involved in complex diseases remains challenging. One major approach is to identify configurations of interacting factors such as single nucleotide polymorphisms (SNPs) that confer susceptibility to disease. Traditional methods, such as the multiple dimensional reduction method and the combinatorial partitioning method, provide good tools to decipher such interactions amid a disease population with a single genetic cause. However, these traditional methods have not managed to resolve the issue of genetic heterogeneity, which is believed to be a very common phenomenon in complex diseases. There is rarely prior knowledge of the genetic heterogeneity of a disease, and traditional methods based on estimation over the entire population are unlikely to succeed in the presence of heterogeneity. We present a novel Boosted Generative Modeling (BGM) approach for structure-model the interactions leading to diseases in the context of genetic heterogeneity. Our BGM method bridges the ensemble and generative modeling approaches to genetic association studies under a case-control design. Generative modeling is employed to model the interaction network configuration and the causal relationships, while boosting is used to address the genetic heterogeneity problem. We perform our method on simulation data of complex diseases. The results indicate that our method is capable of modeling the structure of interaction networks among disease-susceptible loci and of addressing genetic heterogeneity issues where the traditional methods, such as multiple dimensional reduction method, fail to apply. Our BGM method provides an exploratory tool that identifies the variables (e.g., disease-susceptible loci) that are likely to correlate and contribute to the disease.     

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.     

Evidence for common autoimmune disease genes controlling onset, severity, and chronicity based on experimental models for multiple sclerosis and rheumatoid arthritis

The pathogenicity of multiple sclerosis is still poorly understood, but identification of susceptibility genes using the animal model experimental allergic encephalomyelitis (EAE) could provide leads. Certain genes may be shared between different autoimmune diseases, and identification of such genes is of obvious importance. To locate gene regions involved in the control of EAE and to compare the findings with the susceptibility loci recently identified in a model for rheumatoid arthritis (pristane-induced arthritis), we made crosses between the encephalomyelitis- and arthritis-susceptible rat strain DA and the resistant E3 strain. Genetic analysis of animals produced in a F2 intercross identified 11 loci associated with specific EAE-associated traits. Interestingly, five of these loci were situated at the same position as major loci controlling pristane-induced arthritis and showed similarities in inheritance pattern and subphenotype associations. Our results show that different phases of EAE are controlled by different sets of genes and that common genes are likely to be involved in different autoimmune diseases.     

Mouse models and the interpretation of human GWAS in type 2 diabetes and obesity

Within the last 3 years, genome-wide association studies (GWAS) have had unprecedented success in identifying loci that are involved in common diseases. For example, more than 35 susceptibility loci have been identified for type 2 diabetes and 32 for obesity thus far. However, the causal gene and variant at a specific linkage disequilibrium block is often unclear. Using a combination of different mouse alleles, we can greatly facilitate the understanding of which candidate gene at a particular disease locus is associated with the disease in humans, and also provide functional analysis of variants through an allelic series, including analysis of hypomorph and hypermorph point mutations, and knockout and overexpression alleles. The phenotyping of these alleles for specific traits of interest, in combination with the functional analysis of the genetic variants, may reveal the molecular and cellular mechanism of action of these disease variants, and ultimately lead to the identification of novel therapeutic strategies for common human diseases. In this Commentary, we discuss the progress of GWAS in identifying common disease loci for metabolic disease, and the use of the mouse as a model to confirm candidate genes and provide mechanistic insights.     

BAG2 structure,function and involvement in disease

Bcl2-associated athanogene 2 (BAG2) shares a similar molecular structure and function with other BAG family members. Functioning as a co-chaperone, it interacts with the ATPase domain of the heat shock protein 70 (dHsp70) through its BAG domain. It also interacts with many other molecules and regulates various cellular functions. An increasing number of studies have indicated that BAG2 is involved in the pathogenesis of various diseases, including cancers and neurodegenerative diseases. This paper is a comprehensive review of the structure, functions, and protein interactions of BAG2. We also discuss its roles in diseases, including cancer, Alzheimer’s disease, Parkinson’s disease and spinocerebellar ataxia type-3. Further research on BAG2 could lead to an understanding of the pathogenesis of these disorders or even to novel therapeutic approaches.     

Evidence and implications for multiplicative interactions among loci predisposing to human common disease

OBJECTIVE: The aim of this study was to utilize information on monozygotic twin concordance rates and linkage studies results for common diseases to predict the likely mode of interaction between susceptibility loci. METHODS: We calculated combinations of allele frequency and genotypic relative risk (GRR) that would generate linkage results typically observed in common human diseases. Given these single locus effects, we calculated the expected monozygotic twin concordance assuming different numbers of loci under different interaction models. RESULTS: We demonstrate that, for disorders like schizophrenia, a purely additive model of interaction among loci is not consistent with the available evidence. Instead there are likely significant multiplicative or stronger interactions. Given these interactions, we show that in a diagnostic test based on a subset of predisposing loci, the marginal increase of predictive value rises with each additional locus that is discovered. Our model was consistent with susceptibility alleles being common or rare. CONCLUSIONS: Evidence from monozygotic twin concordance rates and linkage results point to a significant degree of multiplicative interaction among loci.     

Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology

Genome-wide association studies (GWAS) have defined over 150 genomic regions unequivocally containing variation predisposing to immune-mediated disease. Inferring disease biology from these observations, however, hinges on our ability to discover the molecular processes being perturbed by these risk variants. It has previously been observed that different genes harboring causal mutations for the same Mendelian disease often physically interact. We sought to evaluate the degree to which this is true of genes within strongly associated loci in complex disease. Using sets of loci defined in rheumatoid arthritis (RA) and Crohn's disease (CD) GWAS, we build protein-protein interaction (PPI) networks for genes within associated loci and find abundant physical interactions between protein products of associated genes. We apply multiple permutation approaches to show that these networks are more densely connected than chance expectation. To confirm biological relevance, we show that the components of the networks tend to be expressed in similar tissues relevant to the phenotypes in question, suggesting the network indicates common underlying processes perturbed by risk loci. Furthermore, we show that the RA and CD networks have predictive power by demonstrating that proteins in these networks, not encoded in the confirmed list of disease associated loci, are significantly enriched for association to the phenotypes in question in extended GWAS analysis. Finally, we test our method in 3 non-immune traits to assess its applicability to complex traits in general. We find that genes in loci associated to height and lipid levels assemble into significantly connected networks but did not detect excess connectivity among Type 2 Diabetes (T2D) loci beyond chance. Taken together, our results constitute evidence that, for many of the complex diseases studied here, common genetic associations implicate regions encoding proteins that physically interact in a preferential manner, in line with observations in Mendelian disease.     

Hybrid mouse diversity panel: a panel of inbred mouse strains suitable for analysis of complex genetic traits

We have developed an association-based approach using classical inbred strains of mice in which we correct for population structure, which is very extensive in mice, using an efficient mixed-model algorithm. Our approach includes inbred parental strains as well as recombinant inbred strains in order to capture loci with effect sizes typical of complex traits in mice (in the range of 5?% of total trait variance). Over the last few years, we have typed the hybrid mouse diversity panel (HMDP) strains for a variety of clinical traits as well as intermediate phenotypes and have shown that the HMDP has sufficient power to map genes for highly complex traits with resolution that is in most cases less than a megabase. In this essay, we review our experience with the HMDP, describe various ongoing projects, and discuss how the HMDP may fit into the larger picture of common diseases and different approaches.     

Evolution, revolution and heresy in the genetics of infectious disease susceptibility

Infectious pathogens have long been recognized as potentially powerful agents impacting on the evolution of human genetic diversity. Analysis of large-scale case-control studies provides one of the most direct means of identifying human genetic variants that currently impact on susceptibility to particular infectious diseases. For over 50 years candidate gene studies have been used to identify loci for many major causes of human infectious mortality, including malaria, tuberculosis, human immunodeficiency virus/acquired immunodeficiency syndrome, bacterial pneumonia and hepatitis. But with the advent of genome-wide approaches, many new loci have been identified in diverse populations. Genome-wide linkage studies identified a few loci, but genome-wide association studies are proving more successful, and both exome and whole-genome sequencing now offer a revolutionary increase in power. Opinions differ on the extent to which the genetic component to common disease susceptibility is encoded by multiple high frequency or rare variants, and the heretical view that most infectious diseases might even be monogenic has been advocated recently. Review of findings to date suggests that the genetic architecture of infectious disease susceptibility may be importantly different from that of non-infectious diseases, and it is suggested that natural selection may be the driving force underlying this difference.     

Integrated Methods to Solve the Biological Basis of Common Diseases

Biological variations influence population variations of many common traits. Identification of the biological basis of many common diseases has been particularly difficult, but new reagents and analytical tools will greatly facilitate this process. The goal of this review is to discuss how to identify the biological basis of common traits by using mouse models. No single method will work for all traits. Understanding complex problems will require broadly based holistic approaches that use a wide array of tools and resources. A multiplicity of developed methods together provide the tools needed to identify the biological basis of any common trait. These tools, whole-genome linkage maps, maps of expressed genes, and statistical methods, deal with the complexities of multiple loci or correlated traits. This review provides some criteria for making choices about the likely productive approaches at each stage in the process of finding genes that influence common traits.     

神经变性构象病及其分子基础

构象病的概念被广泛用于命名与蛋白质的构象异常相关的疾病。随着生命科学的进步,人们对神经变性疾病发病的分子机制有了较好的认识,发现几乎所有的此类疾病,诸如阿尔采末病（AD）、帕金森病（PD）、亨廷顿病（HD）以及朊蛋白病（PrD）等都具有一个共同的特征,即病变细胞中蓄积有大量错误折叠并易于聚合的蛋白质,这符合构象病的特点,所以又派生了神经变性构象病的新概念。近年来,人们在神经变性构象病的蛋白质错误折叠和聚合以及其细胞毒性方面的认识越来越走向深入,这将对寻找有效的治疗方法起到极大的推动作用。     

Genome wide association studies and prion disease

Over the last decade remarkable advances in genotyping and sequencing technology have resulted in hundreds of novel gene associations with disease. These have typically involved high frequency alleles in common diseases and with the advent of next generation sequencing, disease causing recessive mutations in rare inherited syndromes. Here we discuss the impact of these advances and other gene discovery methods in the prion diseases. Several quantitative trait loci in mouse have been mapped and their human counterparts analysed (HECTD2, CPNE8); other candidate genes regions have been chosen for functional reasons (SPRN, CTSD). Human genome wide association has been done in variant Creutzfeldt-Jakob disease (CJD) and are ongoing in larger collections of sporadic CJD with findings around, but not clearly beyond, the levels of statistical significance required in these studies (THRB-RARB, STMN2). Future work will include closer integration of animal and human genetic studies, larger and combined genome wide association, analysis of structural genetic variantion and next generation sequencing studies involving the entire coding exome or genome.     

mTOR signaling in polycystic kidney disease

Polycystic kidney diseases (PKDs) comprise a large group of genetic disorders characterized by formation of cysts in the kidneys and other organs, ultimately leading to end-stage renal disease. Although PKDs can be caused by mutations in different genes, they converge on a set of common molecular mechanisms involved in cystogenesis and ciliary dysfunction, and can be qualified as ciliopathies. Recent advances in understanding the mechanisms regulating disease progression have led to the development of new therapies that are being tested in both preclinical and clinical trials. In this article, we briefly review a network of molecular pathways of cystogenesis that are regulated by ciliary functions. We discuss the mTOR pathway in depth, highlighting recent progress in understanding its role in PKD and the current results of clinical trials.     

Complex inheritance and localizing disease genes.

Most methods for localizing genes underlying complex traits work under the implicit or explicit assumption of a single disease gene with the possible exception of heterogeneity, that is, different disease genes in different families. We discuss current single-locus and multi-locus methods. Novel approaches are proposed that take into account all marker loci over the genome. A simple example is given for an unconventional statistic, i.e. the mean of allele sharing over all markers on a chromosome.     

Connective tissue responses in blacks in relation to disease: further observations

Additional information is presented in support of the hypothesis (Polednak: Am. J. Phys. Anthropol. 41:49-58, 1974) that in some black populations certain connective-tissue responses, which are involved in protection against infection and repair after injury, also may predispose to specific chronic diseases. These diseases include some autoimmune disorders (i.e., systemic lupus erythematosus, sarcoidosis, and scleroderma) and various benign and malignant tumors involving connective-tissue cells. Complex interactions between genetic factors (HLA and non-HLA loci) and environmental agents may be involved both in the etiology of these autoimmune diseases and in population differences in the incidence of these diseases. A framework is reviewed whereby cellular responses to infectious agents, involving chiefly immunoglobulin-producing cells and macrophages, may have consequences in terms of pathogenesis of specific chronic diseases more common in some black populations. The possible role of natural selection in maintaining some of these diseases is also considered, along with the need for involvement of biomedical anthropologists in their investigation.     

The HLA system and the analysis of multifactorial genetic disease

The human leukocyte antigen (HLA) system comprises closely linked genes controlling highly polymorphic proteins involved in the presentation of peptides to the T-cell receptor. Specific alleles at HLA loci are associated with diseases, often those suspected to be of autoimmune aetiology. Many of these associations result from linkage disequilibrium between the HLA gene studied and other HLA genes or non-HLA gebes close by. Owing to its high level of polymorphism and its candidate role in many diseases, HLA was the first system used in many techniques of genetic mapping, such as affected-sib-pair analysis and association (linkage disequilibrium) studies. Much remains unknown about the reasons why diseases are associated with HLA. Experience gained from HLA has, however, shown how other loci involved in complex traits can be identified by studying families with multiple affected cases or sib pairs, followed by linkage-disequilibrium mapping and then analysis of candidate genes.     

Genome wide association studies and prion disease

Over the last decade remarkable advances in genotyping and sequencing technology have resulted in hundreds of novel gene associations with disease. These have typically involved high frequency alleles in common diseases and with the advent of next generation sequencing, disease causing recessive mutations in rare inherited syndromes. Here we discuss the impact of these advances and other gene discovery methods in the prion diseases. Several quantitative trait loci in mouse have been mapped and their human counterparts analyzed (HECTD2, CPNE8); other candidate genes regions have been chosen for functional reasons (SPRN, CTSD). Human genome wide association has been done in variant Creutzfeldt-Jakob disease (CJD) and are ongoing in larger collections of sporadic CJD with findings around, but not clearly beyond, the levels of statistical significance required in these studies (THRB-RARB, STMN2). Future work will include closer integration of animal and human genetic studies, larger and combined genome wide association, analysis of structural genetic variation and next generation sequencing studies involving the entire exome or genome.Key words: prion, genetic, CJD, GWAS     

De novo mutations in human genetic disease

New mutations have long been known to cause genetic disease, but their true contribution to the disease burden can only now be determined using family-based whole-genome or whole-exome sequencing approaches. In this Review we discuss recent findings suggesting that de novo mutations play a prominent part in rare and common forms of neurodevelopmental diseases, including intellectual disability, autism and schizophrenia. De novo mutations provide a mechanism by which early-onset reproductively lethal diseases remain frequent in the population. These mutations, although individually rare, may capture a significant part of the heritability for complex genetic diseases that is not detectable by genome-wide association studies.     

The genetic basis of graves' disease

The presented comprehensive review of current knowledge about genetic factors predisposing to Graves' disease (GD) put emphasis on functional significance of observed associations. In particular, we discuss recent efforts aimed at refining diseases associations found within the HLA complex and implicating HLA class I as well as HLA-DPB1 loci. We summarize data regarding non-HLA genes such as PTPN22, CTLA4, CD40, TSHR and TG which have been extensively studied in respect to their role in GD. We review recent findings implicating variants of FCRL3 (gene for FC receptor-like-3 protein), SCGB3A2 (gene for secretory uteroglobin-related protein 1- UGRP1) as well as other unverified possible candidate genes for GD selected through their documented association with type 1 diabetes mellitus: Tenr-IL2-IL21, CAPSL (encoding calcyphosine-like protein), IFIH1(gene for interferon-induced helicase C domain 1), AFF3, CD226 and PTPN2. We also review reports on association of skewed X chromosome inactivation and fetal microchimerism with GD. Finally we discuss issues of genotype-phenotype correlations in GD.     

Power to detect risk alleles using genome-wide tag SNP panels

Advances in high-throughput genotyping and the International HapMap Project have enabled association studies at the whole-genome level. We have constructed whole-genome genotyping panels of over 550,000 (HumanHap550) and 650,000 (HumanHap650Y) SNP loci by choosing tag SNPs from all populations genotyped by the International HapMap Project. These panels also contain additional SNP content in regions that have historically been overrepresented in diseases, such as nonsynonymous sites, the MHC region, copy number variant regions and mitochondrial DNA. We estimate that the tag SNP loci in these panels cover the majority of all common variation in the genome as measured by coverage of both all common HapMap SNPs and an independent set of SNPs derived from complete resequencing of genes obtained from SeattleSNPs. We also estimate that, given a sample size of 1,000 cases and 1,000 controls, these panels have the power to detect single disease loci of moderate risk (λ ~ 1.8–2.0). Relative risks as low as λ ~ 1.1–1.3 can be detected using 10,000 cases and 10,000 controls depending on the sample population and disease model. If multiple loci are involved, the power increases significantly to detect at least one locus such that relative risks 20%–35% lower can be detected with 80% power if between two and four independent loci are involved. Although our SNP selection was based on HapMap data, which is a subset of all common SNPs, these panels effectively capture the majority of all common variation and provide high power to detect risk alleles that are not represented in the HapMap data.