From human genome epidemiology to systems epidemiology: current progress and future perspective

The recent progress in human genome epidemiology (HuGE) is already having a profound impact on the practice of medicine and public health. First, the success of genome-wide association studies has greatly expanded the direction and content of epidemiological researches, including revealing new genetic mechanisms of complex diseases, identifying new targets for therapeutic interventions, and improving application in early screening of high-risk populations. At the same time, large-scale genomic studies make it possible to efficiently explore the gene-environment interactions, which will help better understand the biological pathways of complex diseases and identify individuals who may be more susceptible to diseases. Additionally, the emergence of systems epidemiology aims to integrate multi-omics together with epidemiological data to create a systems network that can comprehensively characterize the diverse range of factors contributing to disease development. These progress will help to apply HuGE findings into practice to improve the health of individuals and populations.     

Concordance of genotypes in pre- and post-lung transplantation DNA samples

Genetic epidemiology studies of end-stage lung disease are potentially hindered by low numbers of participants due to early death of patients from the underlying disease, or due to exclusion from studies after patients have had lung transplants, because of concern about bias of genotype data due to chimerism. The number of participants enrolled in genetic studies of end-stage lung disease could be increased by including those individuals who have undergone lung transplant. We hypothesized that individuals who have had lung transplants can be included in genetic epidemiology studies that use single nucleotide polymorphism and short tandem repeat marker data, without confounding due to chimerism. Ten probands with severe, early-onset chronic obstructive pulmonary disease were included in this analysis. Pre- and post-lung transplant DNA samples were used in the investigation of concordance of genotype results for 12 short tandem repeat markers and 23 single nucleotide polymorphisms. Concordance was observed for all genotypes before and after lung transplant. We conclude that the risk of biasing genetic epidemiology studies due to donor lung-related DNA microchimerism is low, and that the inclusion of post-lung transplantation participants will allow for larger genetic epidemiology studies of individuals with end-stage lung disease.     

On the origins of complex immune-mediated disease: the example of rheumatoid arthritis

This essay discusses strategies for understanding the origins and outcomes of complex chronic inflammatory diseases using genetic and environmental determinants as tools for new definitions of disease subsets. Rheumatoid arthritis has been chosen as the prototype to illustrate these general concepts. Through recent data on two different disease subsets, it has been possible to devise a new molecular model for disease development by incorporating multiple genes and environmental agents which generate immune reactions that may eventually cause disease. These kinds of studies, aiming to integrate genetic epidemiology and molecular immunology, require close proximity between institutions for molecular medicine, clinical departments able to provide follow-up, careful surveillance of large patient groups and collaboration with experts in epidemiology and biostatistics.     

人类与医学遗传学群体与家系资料分析计算机系统的功能与菜单结构（Ⅰ）

本文介绍我们用Ｃ语言编制的人类与医学遗传学群体与家系资料分析计算机系统的菜单结构与各子系统的功能。该系统可用于基因频率与群体分化分析；相关与遗传度分析；疾病关联分析；遗传异质性分析；ＡＧＦＡＰ遗传方式分析。尚可用于通径分析；数量性状的混合分布分析；分离分析与连锁分析等。对人类与医学遗传学及遗传流行病学研究都很有实用价值。     

A review of the genetic epidemiology of resistance to parasitic disease and atopic asthma: common variants for common phenotypes?

PURPOSE OF REVIEW: An inverse relationship between resistance to certain parasitic diseases and measures of atopy and asthma has long been observed. A possible explanation is that genetic determinants which confer protection against detrimental worm burdens are the same determinants involved in atopic asthma. The focus of this review is to consider the potential candidate genes that have been elucidated as part of molecular, genomic and genetic studies of parasite biology, host-parasite interactions and classic genetic epidemiology studies on parasitic disease and allergic asthma. RECENT FINDINGS: Comparative studies of the Plasmodium and Schistosoma spp. genomes have revealed a number of proteins that are homologous to humans. A number of linkage and association studies on susceptibility/resistance to parasitic diseases, including malaria and schistosomiasis, overlap with associations that have been identified for susceptibility to atopy and asthma. SUMMARY: In response to parasitic approaches in maintaining survival, the human host has evolved genetic adaptations that minimize severe manifestations of disease, which conversely appear to contribute to allergic disease. A clearer understanding of this process will elucidate the complex pathways and mechanisms involved in these traits.     

Family, twin, and adoption studies of bipolar disorder

Family, twin, and adoption studies have been essential in defining the genetic epidemiology of bipolar disorder over the past several decades. Family studies have documented that first-degree relatives of affected individuals have an excess risk of the disorder, while twin studies (and to a lesser extent, adoption studies) suggest that genes are largely responsible for this familial aggregation. We review these studies, including the magnitude of familial risk and heritability estimates, efforts to identify familial subtypes of bipolar disorder, and the implications of family/genetic data for validating nosologic boundaries. Taken together, these studies indicate that bipolar disorder is phenotypically and genetically complex.     

Search for new thrombosis-related genes through intermediate phenotypes. Genetic and household effects

As a complex disease, thrombosis is determined by environmental and genetic factors and by the interaction of these factors. One of the objectives of modern epidemiology is to understand the underlying complexity in complex diseases by means of disentangling the genetic from the environmental factors and quantifying the relative influence of each factor on the disease. Often it is easier and more fruitful to study intermediate phenotypes than the diseases themselves. The first step of these analyses is to determine the relative contributions of genes (heritability), measured environmental factors that are specific to an individual, and environmental factors that are shared by members of a household (household effects), to variation in the complex phenotype. Currently there are published data on 56 phenotypes involved in these kind of genetic studies of hemostasis and potentially related to thromboembolic disease. The main conclusion of these studies is that genetic effects (as measured by heritabilities) are major contributors to the phenotypic variability. Only in 16 of the 56 (29%) of the studied phenotypes, were household effects reported. These results are primordial in the design of further studies consisting in wide-genome explorations in the search for the underlying genes.     

Model-Based Multifactor Dimensionality Reduction to detect epistasis for quantitative traits in the presence of error-free and noisy data

Detecting gene-gene interactions or epistasis in studies of human complex diseases is a big challenge in the area of epidemiology. To address this problem, several methods have been developed, mainly in the context of data dimensionality reduction. One of these methods, Model-Based Multifactor Dimensionality Reduction, has so far mainly been applied to case-control studies. In this study, we evaluate the power of Model-Based Multifactor Dimensionality Reduction for quantitative traits to detect gene-gene interactions (epistasis) in the presence of error-free and noisy data. Considered sources of error are genotyping errors, missing genotypes, phenotypic mixtures and genetic heterogeneity. Our simulation study encompasses a variety of settings with varying minor allele frequencies and genetic variance for different epistasis models. On each simulated data, we have performed Model-Based Multifactor Dimensionality Reduction in two ways: with and without adjustment for main effects of (known) functional SNPs. In line with binary trait counterparts, our simulations show that the power is lowest in the presence of phenotypic mixtures or genetic heterogeneity compared to scenarios with missing genotypes or genotyping errors. In addition, empirical power estimates reduce even further with main effects corrections, but at the same time, false-positive percentages are reduced as well. In conclusion, phenotypic mixtures and genetic heterogeneity remain challenging for epistasis detection, and careful thought must be given to the way important lower-order effects are accounted for in the analysis.     

Is rheumatoid arthritis a consequence of natural selection for enhanced tuberculosis resistance?

Although the bubonic plague or "Black Death" is notorious for the toll it took on the population of Europe in the middle ages, another epidemic, the "White Death" of tuberculosis is responsible for millions of deaths worldwide over the past 300 years. With one in four deaths due to tuberculosis in Western Europe and the United States in the 19th century, this disease undoubtedly acted as a powerful genetic selective force. The epidemiology of modern day rheumatoid arthritis (RA) is strikingly similar to the epidemiology of tuberculosis 100-200 years ago, suggesting the possibility that genetic factors that enhanced survival in tuberculosis epidemics are now influencing susceptibility to RA. Recent advances in the analysis of genetic polymorphisms associated with disease have identified several genes linked to RA susceptibility that encode proteins involved in the immune response to Mycobacterium tuberculosis infection, including TNF-alpha, NRAMP1, PARP-1, HLA-DRB1, and PADI4. These results suggest that rheumatoid arthritis, and possibly other autoimmune diseases, are modern day manifestations of the genetic selective pressure exerted by tuberculosis epidemics of the recent past.     

Genetic control of vectorial competence in Aedes mosquitoes

The transmission of pathogens by arthropods is dependent on the relationships that exist between the pathogen, the invertebrate host (the vector) and the vertebrate host, each of which is influenced by environmental variations. Particular attention is given to the knowledge of intrinsic factors and the mechanisms controlling the ability of vectors to transmit pathogens (viruses or parasites). Polymorphism in the expression of susceptibility to oral infection has been shown to occur among geographical samples of mosquitoes. It has been proven that intraspecific variations in vector competence are controlled by one or more genes and expressed in variable proportions within a mosquito population. Recent advances in molecular biology have facilitated accessibility of nucleic acid sequence data. These new techniques allow one to analyse the genotype distribution within and among populations. Population genetic studies are currently used to understand the evolution of species differentiation and provide indications on genetic relationship among field vector populations. Estimations of gene flow with respect to vector capacity have provided rich insight into vector species complexes. Knowledge of intraspecies variation is important for the understanding of vector transmission, disease epidemiology and disease control. In this article, two examples are presented to illustrate the contribution of population genetic studies to the understanding of epidemiology of arthropod-borne diseases: Aedes polyneniensis, a vector of human lymphatic filariasis and Aedes aegypti, the vector of dengue viruses.     

Perennial impression of an emerging arbovirus on the epidemiology of rheumatic diseases in south India: insights from the COPCORD study

Are rheumatic musculoskeletal diseases (RMSD) given their due recognition by the medical fraternity and policy makers in India today? Focus on lifestyle diseases has taken away the importance of morbidity caused by musculoskeletal pain, which is one of the commonest ailments in the community. Poor awareness in general regarding the upcoming field of rheumatology and lack of proper data regarding these diseases in the country are the primary causes for this debacle. The epidemiology of RMSD in the country is fast changing, especially in the wake of viral epidemics, which leave their mark for months and years together. This view point emphasizes the burden of RMSD by highlighting the findings of two Community Oriented Programme for the Control of Rheumatic Diseases studies conducted to study the prevalence of RMSD in rural communities in the southern state of Kerala, which inadvertently captured the burden of RMSD following Chikungunya viral epidemics in the regions. Both the studies have reported a high prevalence of RMSD following the epidemics. The value of including RMSD in a national programme to combat the morbidity caused and to improve the health related quality of life of patients has been stressed upon, in the background of altering epidemiology of these disorders in the country.     

Estimating adjusted prevalence ratio in clustered cross-sectional epidemiological data

Background

Several thousand human genome epidemiology association studies are published every year investigating the relationship between common genetic variants and diverse phenotypes. Transparent reporting of study methods and results allows readers to better assess the validity of study findings. Here, we document reporting practices of human genome epidemiology studies.

Methods

Articles were randomly selected from a continuously updated database of human genome epidemiology association studies to be representative of genetic epidemiology literature. The main analysis evaluated 315 articles published in 2001–2003. For a comparative update, we evaluated 28 more recent articles published in 2006, focusing on issues that were poorly reported in 2001–2003.

Results

During both time periods, most studies comprised relatively small study populations and examined one or more genetic variants within a single gene. Articles were inconsistent in reporting the data needed to assess selection bias and the methods used to minimize misclassification (of the genotype, outcome, and environmental exposure) or to identify population stratification. Statistical power, the use of unrelated study participants, and the use of replicate samples were reported more often in articles published during 2006 when compared with the earlier sample.

Conclusion

We conclude that many items needed to assess error and bias in human genome epidemiology association studies are not consistently reported. Although some improvements were seen over time, reporting guidelines and online supplemental material may help enhance the transparency of this literature.     

Twin studies in autoimmune disease: genetics, gender and environment

Twin studies are powerful tools to discriminate whether a complex disease is due to genetic or environmental factors. High concordance rates among monozygotic (MZ) twins support genetic factors being predominantly involved, whilst low rates are suggestive of environmental factors. Twin studies have often been utilised in the study of systemic and organ specific autoimmune diseases. As an example, type I diabetes mellitus has been investigated to establish that that disease is largely affected by genetic factors, compared to rheumatoid arthritis or scleroderma, which have a weaker genetic association. However, large twin studies are scarce or virtually non-existent in other autoimmune diseases which have been limited to few sets of twins and individual case reports. In addition to the study of the genetic and environmental contributions to disease, it is likely that twin studies will also provide data in regards to the clinical course of disease, as well as risk for development in related individuals. More importantly, genome-wide association studies have thus far reported genomic variants that only account for a minority of autoimmunity cases, and cannot explain disease discordance in MZ twins. Future research is therefore encouraged not only in the analysis of twins with autoimmune disease, but also in regards to epigenetic factors or rare variants that may be discovered with next-generation sequencing. This review will examine the literature surrounding twin studies in autoimmune disease including discussions of genetics and gender.     

Human genetics of common mycobacterial infections

There is increasing interest in and understanding of the role of human genetic factors controlling susceptibility/resistance to infectious diseases. This is of particular importance for the two most common mycobacterial infections, tuberculosis and leprosy, because this will allow a genetic dissection of antimycobacterial immunity and should open new fields of preventive and therapeutic measures. In this review we will initially discuss various methods of genetic epidemiology that have been and are being developed to identify human genes controlling infectious diseases, and then illustrate the findings obtained in the numerous studies performed in tuberculosis and leprosy. Although the most convincing results were observed for HLA-DR2 and NRAMP1 (or a closely linked gene) in pulmonary tuberculosis and leprosy subtypes and for a 10p13 locus in paucibacillary leprosy, the molecular basis of their effects remains to be established.     

Susceptibility genes in thyroid autoimmunity

The autoimmune thyroid diseases (AITD) are complex diseases which are caused by an interaction between susceptibility genes and environmental triggers. Genetic susceptibility in combination with external factors (e.g. dietary iodine) is believed to initiate the autoimmune response to thyroid antigens. Abundant epidemiological data, including family and twin studies, point to a strong genetic influence on the development of AITD. Various techniques have been employed to identify the genes contributing to the etiology of AITD, including candidate gene analysis and whole genome screening. These studies have enabled the identification of several loci (genetic regions) that are linked with AITD, and in some of these loci, putative AITD susceptibility genes have been identified. Some of these genes/loci are unique to Graves' disease (GD) and Hashimoto's thyroiditis (HT) and some are common to both the diseases, indicating that there is a shared genetic susceptibility to GD and HT. The putative GD and HT susceptibility genes include both immune modifying genes (e.g. HLA, CTLA-4) and thyroid specific genes (e.g. TSHR, Tg). Most likely, these loci interact and their interactions may influence disease phenotype and severity.     

Going MAD: development of a "matrix academic division" to facilitate translating research to personalized medicine

Personalized medicine integrates an individual's genetic and other information for the prevention or treatment of complex disorders, and translational research seeks to identify those data most important to disease processes based on observations at the bench and the bedside. To understand complex disorders such as chronic pancreatitis, inflammatory bowel disease, liver cirrhosis, and other idiopathic chronic inflammatory diseases, physician-scientists must systematically collect data on relevant risks, clinical status, biomarkers, and outcomes. The author describes a "matrix academic division" (MAD), a highly effective academic program created at the University of Pittsburgh School of Medicine and the University of Pittsburgh Medical Center using translational research to rapidly develop personalized medicine for digestive diseases. MAD is designed to capture patient-specific data and biologic samples for analysis of steps in a complex process (reverse engineering), reconstructing the system conceptually and mathematically (disease modeling), and deciphering disease mechanism in individual patients to predict the effects of interventions (personalized medicine). MAD draws on the expertise of the medical school's and medical center's physician-scientists to translate essential disease information between the bed and the bench and to communicate with researchers from multiple domains, including epidemiology, genetics, cell biology, immunology, regenerative medicine, neuroscience, and oncology. The author illustrates this approach by describing its successful application to the reverse engineering of chronic pancreatitis.     

The Contribution of Immune Regulatory and Thyroid Specific Genes to the Etiology of Graves' and Hashimoto's Diseases

The autoimmune thyroid diseases (AITD) are complex diseases which are caused by an interaction between susceptibility genes and environmental triggers. Genetic susceptibility in combination with external factors (e.g. dietary iodine) are believed to initiate the autoimmune response to thyroid antigens. Abundant epidemiological data, including family and twin studies, point to a strong genetic influence on the development of AITD. Various techniques have been employed to identify the genes contributing to the etiology of AITD, including candidate gene analysis and whole genome screening. These studies have enabled the identification of several loci (genetic regions) that are linked with AITD, and in some of these loci putative AITD susceptibility genes have been identified. Some of these genes/loci are unique to Graves' disease (GD) and Hashimoto's thyroiditis (HT) and some are common to both diseases, indicating that there is a shared genetic susceptibility to GD and HT. The putative GD and HT susceptibility genes include both immune modifying genes (e.g. HLA, CTLA-4) and thyroid specific genes (e.g. TSHR, Tg). Most likely these loci interact and their interactions may influence disease phenotype and severity.     

The contribution of immune regulatory and thyroid specific genes to the etiology of Graves' and Hashimoto's diseases

The autoimmune thyroid diseases (AITD) are complex diseases which are caused by an interaction between susceptibility genes and environmental triggers. Genetic susceptibility in combination with external factors (e.g. dietary iodine) are believed to initiate the autoimmune response to thyroid antigens. Abundant epidemiological data, including family and twin studies, point to a strong genetic influence on the development of AITD. Various techniques have been employed to identify the genes contributing to the etiology of AITD, including candidate gene analysis and whole genome screening. These studies have enabled the identification of several loci (genetic regions) that are linked with AITD, and in some of these loci putative AITD susceptibility genes have been identified. Some of these genes/loci are unique to Graves' disease (GD) and Hashimoto's thyroiditis (HT) and some are common to both diseases, indicating that there is a shared genetic susceptibility to GD and HT. The putative GD and HT susceptibility genes include both immune modifying genes (e.g. HLA, CTLA-4) and thyroid specific genes (e.g. TSHR, Tg). Most likely these loci interact and their interactions may influence disease phenotype and severity.     

Robust Quantitative Trait Association Tests in the Parent-Offspring Triad Design: Conditional Likelihood-Based Approaches

Association studies, based on either population data or familial data, have been widely applied to mapping of genes underlying complex diseases. In family-based association studies, using case-parent triad families, the popularly used transmission/disequilibrium test (TDT) was proposed for avoidance of spurious association results caused by other confounders such as population stratification. Originally, the TDT was developed for analysis of binary disease data. Extending it to allow for quantitative trait analysis of complex diseases and for robust analysis of binary diseases against the uncertainty of mode of inheritance has been thoroughly discussed. Nevertheless, studies on robust analysis of quantitative traits for complex diseases received relatively less attention. In this paper, we use parent-offspring triad families to demonstrate the feasibility of establishment of the robust candidate-gene association tests for quantitative traits. We first introduce the score statistics from the conditional likelihoods based on parent-offspring triad data under various genetic models. By applying two existing robust procedures we then construct the robust association tests for analysis of quantitative traits. Simulations are conducted to evaluate empirical type I error rates and powers of the proposed robust tests. The results show that these robust association tests do exhibit robustness against the effect of misspecification of the underlying genetic model on testing powers.     

Genetic epidemiology of familial Mediterranean fever through integrative analysis of whole genome and exome sequences from Middle East and North Africa

Familial Mediterranean fever (FMF), an autosomal recessive and rare autoinflammatory disease is caused by genetic mutations in the MEFV gene and is highly prevalent in the Mediterranean basin. Although the carrier frequency of specific disease variants in the MEFV gene has been reported from isolated studies, a comprehensive view of variants in the Mediterranean region has not been possible due to paucity of data. The recent availability of whole‐genome and whole‐exome datasets prompted us to study the genetic epidemiology of MEFV variants in the region. We assembled data from 5 datasets encompassing whole‐genome and whole‐exome datasets for 2115 individuals from multiple subpopulations in the region and also created a compendium for MEFV genetic variants, which were further systematically annotated as per the American College of Medical Genetics and Genomics (ACMG) guidelines. Our analysis points to significant differences in allele frequencies in the subpopulations, and the carrier frequency for MEFV genetic variants in the population to be about 8%. The MEFV gene appears to be under natural selection from our analysis. To the best of our knowledge, this is the most comprehensive study and analysis of population epidemiology of MEFV gene variants in the Middle East and North African populations.