Genetic mapping of common diseases: the challenges of manic-depressive illness and schizophrenia

Advances in genetic mapping of human diseases have led to the identification of single locus susceptibility for several common disorders. There have been a number of reports of linkage for the psychiatric disorders manic-depressive illness and schizophrenia, but none of these linkage reports is uncontested. Nonetheless, it appears promising to continue attempts to map these psychiatric disorders, since linkage can now be detected even when the inheritance is complex and includes genetic heterogeneity and variable penetrance.     

Rational design of complex phenotype via network models

We demonstrate a modeling and computational framework that allows for rapid screening of thousands of potential network designs for particular dynamic behavior. To illustrate this capability we consider the problem of hysteresis, a prerequisite for construction of robust bistable switches and hence a cornerstone for construction of more complex synthetic circuits. We evaluate and rank most three node networks according to their ability to robustly exhibit hysteresis where robustness is measured with respect to parameters over multiple dynamic phenotypes. Focusing on the highest ranked networks, we demonstrate how additional robustness and design constraints can be applied. We compare our results to more traditional methods based on specific parameterization of ordinary differential equation models and demonstrate a strong qualitative match at a small fraction of the computational cost.     

Design considerations for massively parallel sequencing studies of complex human disease

Massively Parallel Sequencing (MPS) allows sequencing of entire exomes and genomes to now be done at reasonable cost, and its utility for identifying genes responsible for rare Mendelian disorders has been demonstrated. However, for a complex disease, study designs need to accommodate substantial degrees of locus, allelic, and phenotypic heterogeneity, as well as complex relationships between genotype and phenotype. Such considerations include careful selection of samples for sequencing and a well-developed strategy for identifying the few "true" disease susceptibility genes from among the many irrelevant genes that will be found to harbor rare variants. To examine these issues we have performed simulation-based analyses in order to compare several strategies for MPS sequencing in complex disease. Factors examined include genetic architecture, sample size, number and relationship of individuals selected for sequencing, and a variety of filters based on variant type, multiple observations of genes and concordance of genetic variants within pedigrees. A two-stage design was assumed where genes from the MPS analysis of high-risk families are evaluated in a secondary screening phase of a larger set of probands with more modest family histories. Designs were evaluated using a cost function that assumes the cost of sequencing the whole exome is 400 times that of sequencing a single candidate gene. Results indicate that while requiring variants to be identified in multiple pedigrees and/or in multiple individuals in the same pedigree are effective strategies for reducing false positives, there is a danger of over-filtering so that most true susceptibility genes are missed. In most cases, sequencing more than two individuals per pedigree results in reduced power without any benefit in terms of reduced overall cost. Further, our results suggest that although no single strategy is optimal, simulations can provide important guidelines for study design.     

Efficient study designs for test of genetic association using sibship data and unrelated cases and controls

Linkage mapping of complex diseases is often followed by association studies between phenotypes and marker genotypes through use of case-control or family-based designs. Given fixed genotyping resources, it is important to know which study designs are the most efficient. To address this problem, we extended the likelihood-based method of Li et al., which assesses whether there is linkage disequilibrium between a disease locus and a SNP, to accommodate sibships of arbitrary size and disease-phenotype configuration. A key advantage of our method is the ability to combine data from different family structures. We consider scenarios for which genotypes are available for unrelated cases, affected sib pairs (ASPs), or only one sibling per ASP. We construct designs that use cases only and others that use unaffected siblings or unrelated unaffected individuals as controls. Different combinations of cases and controls result in seven study designs. We compare the efficiency of these designs when the number of individuals to be genotyped is fixed. Our results suggest that (1) when the disease is influenced by a single gene, the one sibling per ASP-control design is the most efficient, followed by the ASP-control design, and familial cases contribute more association information than singleton cases; (2) when the disease is influenced by multiple genes, familial cases provide more association information than singleton cases, unless the effect of the locus being tested is much smaller than at least one other untested disease locus; and (3) the case-control design can be useful for detecting genes with small effect in the presence of genes with much larger effect. Our findings will be helpful for researchers designing and analyzing complex disease-association studies and will facilitate genotyping resource allocation.     

Mathematical Modelling of the MAP Kinase Pathway Using Proteomic Datasets

The advances in proteomics technologies offer an unprecedented opportunity and valuable resources to understand how living organisms execute necessary functions at systems levels. However, little work has been done up to date to utilize the highly accurate spatio-temporal dynamic proteome data generated by phosphoprotemics for mathematical modeling of complex cell signaling pathways. This work proposed a novel computational framework to develop mathematical models based on proteomic datasets. Using the MAP kinase pathway as the test system, we developed a mathematical model including the cytosolic and nuclear subsystems; and applied the genetic algorithm to infer unknown model parameters. Robustness property of the mathematical model was used as a criterion to select the appropriate rate constants from the estimated candidates. Quantitative information regarding the absolute protein concentrations was used to refine the mathematical model. We have demonstrated that the incorporation of more experimental data could significantly enhance both the simulation accuracy and robustness property of the proposed model. In addition, we used the MAP kinase pathway inhibited by phosphatases with different concentrations to predict the signal output influenced by different cellular conditions. Our predictions are in good agreement with the experimental observations when the MAP kinase pathway was inhibited by phosphatase PP2A and MKP3. The successful application of the proposed modeling framework to the MAP kinase pathway suggests that our method is very promising for developing accurate mathematical models and yielding insights into the regulatory mechanisms of complex cell signaling pathways.     

The Notch signalling system: recent insights into the complexity of a conserved pathway

Notch signalling links the fate of one cell to that of an immediate neighbour and consequently controls differentiation, proliferation and apoptotic events in multiple metazoan tissues. Perturbations in this pathway activity have been linked to several human genetic disorders and cancers. Recent genome-scale studies in Drosophila melanogaster have revealed an extraordinarily complex network of genes that can affect Notch activity. This highly interconnected network contrasts our traditional view of the Notch pathway as a simple linear sequence of events. Although we now have an unprecedented insight into the way in which such a fundamental signalling mechanism is controlled by the genome, we are faced with serious challenges in analysing the underlying molecular mechanisms of Notch signal control.     

A twin approach to unraveling epigenetics

The regulation of gene expression plays a pivotal role in complex phenotypes, and epigenetic mechanisms such as DNA methylation are essential to this process. The availability of next-generation sequencing technologies allows us to study epigenetic variation at an unprecedented level of resolution. Even so, our understanding of the underlying sources of epigenetic variability remains limited. Twin studies have played an essential role in estimating phenotypic heritability, and these now offer an opportunity to study epigenetic variation as a dynamic quantitative trait. High monozygotic twin discordance rates for common diseases suggest that unexplained environmental or epigenetic factors could be involved. Recent genome-wide epigenetic studies in disease-discordant monozygotic twins emphasize the power of this design to successfully identify epigenetic changes associated with complex traits. We describe how large-scale epigenetic studies of twins can improve our understanding of how genetic, environmental and stochastic factors impact upon epigenetics, and how such studies can provide a comprehensive understanding of how epigenetic variation affects complex traits.     

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.     

Conducting Studies with Fathers: Challenges and Opportunities

Traditionally, researchers interested in understanding father involvement in the lives of young children have relied on mothers as proxy respondents for fathers, yet recent research has made noteworthy strides in collecting data from fathers themselves yielding an unprecedented wealth of data on fathers' involvement in their children's lives. Despite this progress, there remain many methodological challenges in conducting studies with fathers and their children. Therefore, this article highlights several methodological challenges, including the identification of fathers, recruitment of fathers as participants, and retention of participants in small-scale studies with longitudinal designs, and discusses the advantages and disadvantages of several strategies our research team and others have used to collect data from fathers. The paper concludes with a set of suggestions for improving methodological approaches in fatherhood research, as well as remaining challenges in this area of study.     

EYE on bioinformatics: dissecting complex disease traits in silico

Bioinformatics has provided an unprecedented power and resource for us to decipher the enigma of complex diseases. It can reveal otherwise promiscuous information from the tremendous amount of data generated by the new, powerful and high-throughput technologies of genomics and proteomics. In this paper, we review the cutting edge developments in complex disease trait mapping, databases, computational gene recognition, gene function prediction, pathway reconstruction and disease classification by expression profiling, and computational modelling of living systems. Integration of all this knowledge and the different technologies, alongside cooperation between experts from different fields, will enhance our understanding of the molecular and mechanistic abnormalities in disease state, and greatly assist the rational development of effective therapies.     

A better prognosis for genetic association studies in mice

Although inbred mouse strains have been the premier model organism used in biomedical research, multiple studies and analyses have indicated that genome-wide association studies (GWAS) cannot be productively performed using inbred mouse strains. However, there is one type of GWAS in mice that has successfully identified the genetic basis for many biomedical traits of interest: haplotype-based computational genetic mapping (HBCGM). Here, we describe how the methodological basis for a HBCGM study significantly differs from that of a conventional murine GWAS, and how an integrative analysis of its output within the context of other 'omic' information can enable genetic discovery. Consideration of these factors will substantially improve the prognosis for the utility of murine genetic association studies for biomedical discovery.     

Bridging the resolution gap in structural modeling of 3D genome organization

Over the last decade, and especially after the advent of fluorescent in situ hybridization imaging and chromosome conformation capture methods, the availability of experimental data on genome three-dimensional organization has dramatically increased. We now have access to unprecedented details of how genomes organize within the interphase nucleus. Development of new computational approaches to leverage this data has already resulted in the first three-dimensional structures of genomic domains and genomes. Such approaches expand our knowledge of the chromatin folding principles, which has been classically studied using polymer physics and molecular simulations. Our outlook describes computational approaches for integrating experimental data with polymer physics, thereby bridging the resolution gap for structural determination of genomes and genomic domains.     

Genetic approaches to common diseases

Combined molecular and epidemiological studies are advancing our understanding of the genetic basis of multifactorial diseases. Several of the results obtained during the past year highlight methodological issues associated with these approaches. For example, the affected sib-pair method has been applied successfully to detect linkage between the angiotensinogen gene and susceptibility to hypertension, and a large multi-centre epidemiological study has demonstrated association of a polymorphism of the angiotensin-converting enzyme gene with increased risk of myocardial infarction. The study of Mendelian forms of multifactorial diseases has also led to many new results. These include the characterization of mutations in the glucokinase gene in maturity onset diabetes of the young, localization to chromosome 2 of a gene involved in familial colon cancer, and localization to chromosome 19 of a gene responsible for hemiplegic migraine. New insights have been provided into the genetics of multifactorial disorders such as diabetes and hypertension through the study of animal models. Localization of susceptibility loci in such models has recently led to the identification of new candidate genes that may be implicated in disease.     

Thematic review series: systems biology approaches to metabolic and cardiovascular disorders. Multi-organ whole-genome measurements and reverse engineering to uncover gene networks underlying complex traits

Together with computational analysis and modeling, the development of whole-genome measurement technologies holds the potential to fundamentally change research on complex disorders such as coronary artery disease. With these tools, the stage has been set to reveal the full repertoire of biological components (genes, proteins, and metabolites) in complex diseases and their interplay in modules and networks. Here we review how network identification based on reverse engineering, as applied to whole-genome datasets from simpler organisms, is now being adapted to more complex settings such as datasets from human cell lines and organs in relation to physiological and pathological states. Our focus is on the use of a systems biological approach to identify gene networks in coronary atherosclerosis. We also address how gene networks will probably play a key role in the development of early diagnostics and treatments for complex disorders in the coming era of individualized medicine.     

Finding the molecular basis of complex genetic variation in humans and mice

I survey the state of the art in complex trait analysis, including the use of new experimental and computational technologies and resources becoming available, and the challenges facing us. I also discuss how the prospects of rodent model systems compare with association mapping in humans.     

Forward-time simulations of human populations with complex diseases

Due to the increasing power of personal computers, as well as the availability of flexible forward-time simulation programs like simuPOP, it is now possible to simulate the evolution of complex human diseases using a forward-time approach. This approach is potentially more powerful than the coalescent approach since it allows simulations of more than one disease susceptibility locus using almost arbitrary genetic and demographic models. However, the application of such simulations has been deterred by the lack of a suitable simulation framework. For example, it is not clear when and how to introduce disease mutants—especially those under purifying selection—to an evolving population, and how to control the disease allele frequencies at the last generation. In this paper, we introduce a forward-time simulation framework that allows us to generate large multi-generation populations with complex diseases caused by unlinked disease susceptibility loci, according to specified demographic and evolutionary properties. Unrelated individuals, small or large pedigrees can be drawn from the resulting population and provide samples for a wide range of study designs and ascertainment methods. We demonstrate our simulation framework using three examples that map genes associated with affection status, a quantitative trait, and the age of onset of a hypothetical cancer, respectively. Nonadditive fitness models, population structure, and gene–gene interactions are simulated. Case-control, sibpair, and large pedigree samples are drawn from the simulated populations and are examined by a variety of gene-mapping methods.     

Recent Developments in Human Behavioral Genetics: Past Accomplishments and Future Directions

The field of behavioral genetics has enormous potential to uncover both genetic and environmental influences on normal and deviant behavior. Behavioral-genetic methods are based on a solid foundation of theories and methods that successfully have delineated components of complex traits in plants and animals. New resources are now available to dissect the genetic component of these complex traits. As specific genes are identified, we can begin to explore how these interact with environmental factors in development. How we interpret such findings, how we ask new questions, how we celebrate the knowledge, and how we use or misuse this knowledge are all important considerations. These issues are pervasive in all areas of human research, and they are especially salient in human behavioral genetics.     

Genetics of Crohn disease, an archetypal inflammatory barrier disease

Chronic inflammatory disorders such as Crohn disease, atopic eczema, asthma and psoriasis are triggered by hitherto unknown environmental factors that function on the background of some polygenic susceptibility. Recent technological advances have allowed us to unravel the genetic aetiology of these and other complex diseases. Using Crohn disease as an example, we show how the discovery of susceptibility genes furthers our understanding of the underlying disease mechanisms and how it will, ultimately, give rise to new therapeutic developments. The long-term goal of such endeavours is to develop targeted prophylactic strategies. These will probably target the molecular interaction on the mucosal surface between the products of the genome and the microbial metagenome of a patient.