Studies Of Complex Diseases Research Articles

Environment-Gene interaction in common complex diseases: New approaches

Approximately 100,000 different environmental chemicals that are in use as high production volume chemicals confront us in our daily lives. Many of the chemicals we encounter are persistent and have long half-lives in the environment and our bodies. These compounds are referred to as Persistent Organic Pollutants, or POPS. The total environment however is broader than just toxic pollutants. It includes social capital, social economic status, and other factors that are not commonly considered in traditional approaches to studying environment-human interactions. The mechanism of action of environmental agents in altering the human phenotype from health to disease is more complex than once thought. The focus in public health has shifted away from the study of single-gene rare diseases and has given way to the study of multifactorial complex diseases that are common in the population. To understand common complex diseases, we need teams of scientists from different fields working together with common aims. We review some approaches for studying the action of the environment by discussing use-inspired research, and transdisciplinary research approaches. The Genomic era has yielded new tools for study of gene-environment interactions, including genomics, epigenomics, and systems biology. We use environmentally-driven diabetes mellitus type two as an example of environmental epigenomics and disease. The aim of this review is to start the conversation of how the application of advances in biomedical science can be used to advance public health.

Genome-wide association study of bipolar disorder accounting for effect of body mass index identifies a new risk allele in TCF7L2

Bipolar disorder (BD) is associated with higher body mass index (BMI) and increased metabolic comorbidity. Considering the associated phenotypic traits in genetic studies of complex diseases, either by adjusting for covariates or by investigating interactions between genetic variants and covariates, may help to uncover the missing heritability. However, obesity-related traits have not been incorporated in prior genome-wide analyses of BD as covariates or potential interacting factors. To investigate the genetic factors underlying BD while considering BMI, we conducted genome-wide analyses using data from the Genetic Association Information Network BD study. We analyzed 729,454 genotyped single-nucleotide polymorphism (SNP) markers on 388 European-American BD cases and 1020 healthy controls with available data for maximum BMI. We performed genome-wide association analyses of the genetic effects while accounting for the effect of maximum BMI, and also evaluated SNP-BMI interactions. A joint test of main and interaction effects demonstrated significant evidence of association at the genome-wide level with rs12772424 in an intron of TCF7L2 (P=2.85E-8). This SNP exhibited interaction effects, indicating that the bipolar susceptibility risk of this SNP is dependent on BMI. TCF7L2 codes for the transcription factor TCF/LF, part of the Wnt canonical pathway, and is one of the strongest genetic risk variants for type 2 diabetes (T2D). This is consistent with BD pathophysiology, as the Wnt pathway has crucial implications in neurodevelopment, neurogenesis and neuroplasticity, and is involved in the mechanisms of action of BD and depression treatments. We hypothesize that genetic risk for BD is BMI dependent, possibly related to common genetic risk with T2D.

Functional interpretation of non-coding sequence variation: concepts and challenges.

Understanding the functional mechanisms underlying genetic signals associated with complex traits and common diseases, such as cancer, diabetes and Alzheimer's disease, is a formidable challenge. Many genetic signals discovered through genome-wide association studies map to non-protein coding sequences, where their molecular consequences are difficult to evaluate. This article summarizes concepts for the systematic interpretation of non-coding genetic signals using genome annotation data sets in different cellular systems. We outline strategies for the global analysis of multiple association intervals and the in-depth molecular investigation of individual intervals. We highlight experimental techniques to validate candidate (potential causal) regulatory variants, with a focus on novel genome-editing techniques including CRISPR/Cas9. These approaches are also applicable to low-frequency and rare variants, which have become increasingly important in genomic studies of complex traits and diseases. There is a pressing need to translate genetic signals into biological mechanisms, leading to prognostic, diagnostic and therapeutic advances.

Discovering Subgroups of Patients from DNA Copy Number Data Using NMF on Compacted Matrices

In the study of complex genetic diseases, the identification of subgroups of patients sharing similar genetic characteristics represents a challenging task, for example, to improve treatment decision. One type of genetic lesion, frequently investigated in such disorders, is the change of the DNA copy number (CN) at specific genomic traits. Non-negative Matrix Factorization (NMF) is a standard technique to reduce the dimensionality of a data set and to cluster data samples, while keeping its most relevant information in meaningful components. Thus, it can be used to discover subgroups of patients from CN profiles. It is however computationally impractical for very high dimensional data, such as CN microarray data. Deciding the most suitable number of subgroups is also a challenging problem. The aim of this work is to derive a procedure to compact high dimensional data, in order to improve NMF applicability without compromising the quality of the clustering. This is particularly important for analyzing high-resolution microarray data. Many commonly used quality measures, as well as our own measures, are employed to decide the number of subgroups and to assess the quality of the results. Our measures are based on the idea of identifying robust subgroups, inspired by biologically/clinically relevance instead of simply aiming at well-separated clusters. We evaluate our procedure using four real independent data sets. In these data sets, our method was able to find accurate subgroups with individual molecular and clinical features and outperformed the standard NMF in terms of accuracy in the factorization fitness function. Hence, it can be useful for the discovery of subgroups of patients with similar CN profiles in the study of heterogeneous diseases.

Regulatory variation: an emerging vantage point for cancer biology

Transcriptional regulation involves complex and interdependent interactions of noncoding and coding regions of the genome with proteins that interact and modify them. Genetic variation/mutation in coding and noncoding regions of the genome can drive aberrant transcription and disease. In spite of accounting for nearly 98% of the genome comparatively little is known about the contribution of noncoding DNA elements to disease. Genome-wide association studies of complex human diseases including cancer have revealed enrichment for variants in the noncoding genome. A striking finding of recent cancer genome re-sequencing efforts has been the previously underappreciated frequency of mutations in epigenetic modifiers across a wide range of cancer types. Taken together these results point to the importance of dysregulation in transcriptional regulatory control in genesis of cancer. Powered by recent technological advancements in functional genomic profiling, exploration of normal and transformed regulatory networks will provide novel insight into the initiation and progression of cancer and open new windows to future prognostic and diagnostic tools.

Network.assisted analysis to prioritize GWAS results: principles, methods and perspectives.

Genome-wide association studies (GWAS) have rapidly become a powerful tool in genetic studies of complex diseases and traits. Traditionally, single marker-based tests have been used prevalently in GWAS and have uncovered tens of thousands of disease-associated SNPs. Network-assisted analysis (NAA) of GWAS data is an emerging area in which network-related approaches are developed and utilized to perform advanced analyses of GWAS data in order to study various human diseases or traits. Progress has been made in both methodology development and applications of NAA in GWAS data, and it has already been demonstrated that NAA results may enhance our interpretation and prioritization of candidate genes and markers. Inspired by the strong interest in and high demand for advanced GWAS data analysis, in this review article, we discuss the methodologies and strategies that have been reported for the NAA of GWAS data. Many NAA approaches search for subnetworks and assess the combined effects of multiple genes participating in the resultant subnetworks through a gene set analysis. With no restriction to pre-defined canonical pathways, NAA has the advantage of defining subnetworks with the guidance of the GWAS data under investigation. In addition, some NAA methods prioritize genes from GWAS data based on their interconnections in the reference network. Here, we summarize NAA applications to various diseases and discuss the available options and potential caveats related to their practical usage. Additionally, we provide perspectives regarding this rapidly growing research area.

The Impact of Phenotypic and Genetic Heterogeneity on Results of Genome Wide Association Studies of Complex Diseases

Phenotypic misclassification (between cases) has been shown to reduce the power to detect association in genetic studies. However, it is conceivable that complex traits are heterogeneous with respect to individual genetic susceptibility and disease pathophysiology, and that the effect of heterogeneity has a larger magnitude than the effect of phenotyping errors. Although an intuitively clear concept, the effect of heterogeneity on genetic studies of common diseases has received little attention. Here we investigate the impact of phenotypic and genetic heterogeneity on the statistical power of genome wide association studies (GWAS). We first performed a study of simulated genotypic and phenotypic data. Next, we analyzed the Wellcome Trust Case-Control Consortium (WTCCC) data for diabetes mellitus (DM) type 1 (T1D) and type 2 (T2D), using varying proportions of each type of diabetes in order to examine the impact of heterogeneity on the strength and statistical significance of association previously found in the WTCCC data. In both simulated and real data, heterogeneity (presence of “non-cases”) reduced the statistical power to detect genetic association and greatly decreased the estimates of risk attributed to genetic variation. This finding was also supported by the analysis of loci validated in subsequent large-scale meta-analyses. For example, heterogeneity of 50% increases the required sample size by approximately three times. These results suggest that accurate phenotype delineation may be more important for detecting true genetic associations than increase in sample size.

Systems biology approaches to epidemiological studies of complex diseases.

Systems biology approaches to epidemiological studies of complex diseases include collection of genetic, genomic, epigenomic, and metagenomic data in large-scale epidemiological studies of complex phenotypes. Designs and analyses of such studies raise many statistical challenges. This article reviews some issues related to integrative analysis of such high dimensional and inter-related datasets and outline some possible solutions. I focus my review on integrative approaches for genome-wide genetic variants and gene expression data, methods for joint analysis of genetic and epigenetic variants, and methods for analysis of microbiome data. Statistical methods such as mediation analysis, high-dimensional instrumental variable regression, sparse signal recovery, and compositional data regression provide potential frameworks for integrative analysis of these high-dimensional genomic data.

PS1-23: Capitalizing on the HMO Cancer Research Network (CRN): The Optimal Setting to Conduct Studies of Rare Complex Diseases

Background/Aims Rare cancers are challenging to study, both epidemiologically and clinically, as it is difficult to ascertain enough cases to achieve adequate statistical power or to be representative of a vast range of exposures. Further, as the complexity of unraveling the natural history of disease has increased, a large investigator team with diverse expertise is required to optimize the scientific contributions that can be mined from research projects. The HMOCRN provides a setting that can overcome these barriers. Although many studies evaluate all lymphomas combined, lymphoma consists of over 50 rare histological subtypes with varying incidence and survival rates and epidemiological features. Ideally, each histological subtype should be considered separately in etiological studies, but even the most common, diffuse large B cell lymphoma, has a SEER incidence of only 7.5 per 100,000 in men and 5.0 per 100,000 in women. Other lymphoma types range in incidence from <0.1 cases per 100,000 for NKT cell lymphoma to 2.8 per 100,000 for Hodgkin’s Disease in all race-sex groups combined, to the highest rate found for a population subgroup, only 8.8 per 100,000 for multiple myeloma in African American men.

GentrepidV2.0: a web server for candidate disease gene prediction

BackgroundCandidate disease gene prediction is a rapidly developing area of bioinformatics research with the potential to deliver great benefits to human health. As experimental studies detecting associations between genetic intervals and disease proliferate, better bioinformatic techniques that can expand and exploit the data are required.DescriptionGentrepid is a web resource which predicts and prioritizes candidate disease genes for both Mendelian and complex diseases. The system can take input from linkage analysis of single genetic intervals or multiple marker loci from genome-wide association studies. The underlying database of the Gentrepid tool sources data from numerous gene and protein resources, taking advantage of the wealth of biological information available. Using known disease gene information from OMIM, the system predicts and prioritizes disease gene candidates that participate in the same protein pathways or share similar protein domains. Alternatively, using an ab initio approach, the system can detect enrichment of these protein annotations without prior knowledge of the phenotype.ConclusionsThe system aims to integrate the wealth of protein information currently available with known and novel phenotype/genotype information to acquire knowledge of biological mechanisms underpinning disease. We have updated the system to facilitate analysis of GWAS data and the study of complex diseases. Application of the system to GWAS data on hypertension using the ICBP data is provided as an example. An interesting prediction is a ZIP transporter additional to the one found by the ICBP analysis. The webserver URL is https://www.gentrepid.org/.

Construction of gene clusters resembling genetic causal mechanisms for common complex disease with an application to young-onset hypertension.

BackgroundLack of power and reproducibility are caveats of genetic association studies of common complex diseases. Indeed, the heterogeneity of disease etiology demands that causal models consider the simultaneous involvement of multiple genes. Rothman’s sufficient-cause model, which is well known in epidemiology, provides a framework for such a concept. In the present work, we developed a three-stage algorithm to construct gene clusters resembling Rothman’s causal model for a complex disease, starting from finding influential gene pairs followed by grouping homogeneous pairs.ResultsThe algorithm was trained and tested on 2,772 hypertensives and 6,515 normotensives extracted from four large Caucasian and Taiwanese databases. The constructed clusters, each featured by a major gene interacting with many other genes and identified a distinct group of patients, reproduced in both ethnic populations and across three genotyping platforms. We present the 14 largest gene clusters which were capable of identifying 19.3% of hypertensives in all the datasets and 41.8% if one dataset was excluded for lack of phenotype information. Although a few normotensives were also identified by the gene clusters, they usually carried less risky combinatory genotypes (insufficient causes) than the hypertensive counterparts. After establishing a cut-off percentage for risky combinatory genotypes in each gene cluster, the 14 gene clusters achieved a classification accuracy of 82.8% for all datasets and 98.9% if the information-short dataset was excluded. Furthermore, not only 10 of the 14 major genes but also many other contributing genes in the clusters are associated with either hypertension or hypertension-related diseases or functions.ConclusionsWe have shown with the constructed gene clusters that a multi-causal pie-multi-component approach can indeed improve the reproducibility of genetic markers for complex disease. In addition, our novel findings including a major gene in each cluster and sufficient risky genotypes in a cluster for disease onset (which coincides with Rothman’s sufficient cause theory) may not only provide a new research direction for complex diseases but also help to reveal the disease etiology.

An integrated approach to identify causal network modules of complex diseases with application to colorectal cancer

Many methods have been developed to identify disease genes and further module biomarkers of complex diseases based on gene expression data. It is generally difficult to distinguish whether the variations in gene expression are causative or merely the effect of a disease. The limitation of relying on gene expression data alone highlights the need to develop new approaches that can explore various data to reflect the casual relationship between network modules and disease traits. In this work, we developed a novel network-based approach to identify putative causal module biomarkers of complex diseases by integrating heterogeneous information, for example, epigenomic data, gene expression data, and protein-protein interaction network. We first formulated the identification of modules as a mathematical programming problem, which can be solved efficiently and effectively in an accurate manner. Then, we applied our approach to colorectal cancer (CRC) and identified several network modules that can serve as potential module biomarkers for characterizing CRC. Further validations using three additional gene expression datasets verified their candidate biomarker properties and the effectiveness of the method. Functional enrichment analysis also revealed that the identified modules are strongly related to hallmarks of cancer, and the enriched functions, such as inflammatory response, receptor and signaling pathways, are specific to CRC. Through constructing a transcription factor (TF)-module network, we found that aberrant DNA methylation of genes encoding TF considerably contributes to the activity change of some genes, which may function as causal genes of CRC, and that can also be exploited to develop efficient therapies or effective drugs. Our method can potentially be extended to the study of other complex diseases and the multiclassification problem.

Single Nucleotide Polymorphism Genotyping Using BeadChip Microarrays

The genotyping of single nucleotide polymorphisms (SNPs) has successfully contributed to the study of complex diseases more than any other technology to date. Genome-wide association studies (GWAS) using 10,000s to >1,000,000 SNPs have identified 1000s of statistically significant SNPs pertaining to 17 different human disease and trait categories. Post-GWAS fine-mapping studies using 10,000s to 100,000s SNPs on a microarray have narrowed the region of interest for many of these GWAS findings; in addition, independent signals within the original GWAS region have been identified. Focused content, SNP-based microarrays such as the human exome, for example, have too been used successfully to identify novel disease associations. Success has come to studies where 100s to 10,000s (mostly) to >100,000 samples were genotyped. For the time being, SNP-based microarrays remain cost-effective especially when studying large numbers of samples compared to other "genotyping" technologies including next generation sequencing. In this unit, protocols for manual (LIMS-free), semi-manual, and automated processing of BeadChip microarrays are presented. Lower throughput studies will find value in the manual and semi-manual protocols, while all types of studies--low-, medium-, and high-throughput--will find value in the semi-manual and automated protocols.

SeqSIMLA: a sequence and phenotype simulation tool for complex disease studies

BackgroundAssociation studies based on next-generation sequencing (NGS) technology have become popular, and statistical association tests for NGS data have been developed rapidly. A flexible tool for simulating sequence data in either unrelated case–control or family samples with different disease and quantitative trait models would be useful for evaluating the statistical power for planning a study design and for comparing power among statistical methods based on NGS data.ResultsWe developed a simulation tool, SeqSIMLA, which can simulate sequence data with user-specified disease and quantitative trait models. We implemented two disease models, in which the user can flexibly specify the number of disease loci, effect sizes or population attributable risk, disease prevalence, and risk or protective loci. We also implemented a quantitative trait model, in which the user can specify the number of quantitative trait loci (QTL), proportions of variance explained by the QTL, and genetic models. We compiled recombination rates from the HapMap project so that genomic structures similar to the real data can be simulated.ConclusionsSeqSIMLA can efficiently simulate sequence data with disease or quantitative trait models specified by the user. SeqSIMLA will be very useful for evaluating statistical properties for new study designs and new statistical methods using NGS. SeqSIMLA can be downloaded for free at http://seqsimla.sourceforge.net.

Associate PCR-RFLP Assay Design With SNPs Based on Genetic Algorithm in Appropriate Parameters Estimation

Polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) is a commonly used laboratory technique and useful in small-scale basic research studies of complex genetic diseases that are associated with single nucleotide polymorphisms (SNPs). Before PCR-RFLP assay for SNP genotyping can be performed, a feasible primer pair observes numerous constraints and an available restriction enzyme for discriminating a target SNP, are required. The computation of feasible PCR-RFLP primers and find available restriction enzymes simultaneously aim at a target SNP is a challenging problem. Here, we propose an available method which combines the updated core of SNP-RFLPing with a genetic algorithm to reliably mine available restriction enzymes and search for feasible PCR-RFLP primers. We have in silico simulated the method in the SLC6A4 gene under different parameter settings and provided an appropriate parameter setting. The wet laboratory validation showed that it indeed usable in providing the available restriction enzymes and designing feasible primers that fit the common primer constraints. We have provided an easy and kindly interface to assist the researchers designing their PCR-RFLP assay for SNP genotyping. The program is implemented in JAVA and is freely available at http://bio.kuas.edu.tw/ganpd/.

Meeting report of the European mouse complex genetics network SYSGENET

The second scientific meeting of the European systems genetics network for the study of complex genetic human disease using genetic reference populations (SYSGENET) took place at the Center for Cooperative Research in Biosciences in Bilbao, Spain, December 10-12, 2012. SYSGENET is funded by the European Cooperation in the Field of Scientific and Technological Research (COST) and represents a network of scientists in Europe that use mouse genetic reference populations (GRPs) to identify complex genetic factors influencing disease phenotypes (Schughart, Mamm Genome 21:331-336, 2010). About 50 researchers working in the field of systems genetics attended the meeting, which consisted of 27 oral presentations, a poster session, and a management committee meeting. Participants exchanged results, set up future collaborations, and shared phenotyping and data analysis methodologies. This meeting was particularly instrumental for conveying the current status of the US, Israeli, and Australian Collaborative Cross (CC) mouse GRP. The CC is an open source project initiated nearly a decade ago by members of the Complex Trait Consortium to aid the mapping of multigenetic traits (Threadgill, Mamm Genome 13:175-178, 2002). In addition, representatives of the International Mouse Phenotyping Consortium were invited to exchange ongoing activities between the knockout and complex genetics communities and to discuss and explore potential fields for future interactions.

Identification of multiple gene-gene interactions for ordinal phenotypes

BackgroundMultifactor dimensionality reduction (MDR) is a powerful method for analysis of gene-gene interactions and has been successfully applied to many genetic studies of complex diseases. However, the main application of MDR has been limited to binary traits, while traits having ordinal features are commonly observed in many genetic studies (e.g., obesity classification - normal, pre-obese, mild obese and severe obese).MethodsWe propose ordinal MDR (OMDR) to facilitate gene-gene interaction analysis for ordinal traits. As an alternative to balanced accuracy, the use of tau-b, a common ordinal association measure, was suggested to evaluate interactions. Also, we generalized cross-validation consistency (GCVC) to identify multiple best interactions. GCVC can be practically useful for analyzing complex traits, especially in large-scale genetic studies.Results and conclusionsIn simulations, OMDR showed fairly good performance in terms of power, predictability and selection stability and outperformed MDR. For demonstration, we used a real data of body mass index (BMI) and scanned 1~4-way interactions of obesity ordinal and binary traits of BMI via OMDR and MDR, respectively. In real data analysis, more interactions were identified for ordinal trait than binary traits. On average, the commonly identified interactions showed higher predictability for ordinal trait than binary traits. The proposed OMDR and GCVC were implemented in a C/C++ program, executables of which are freely available for Linux, Windows and MacOS upon request for non-commercial research institutions.

Hospitalizations for vaccine preventable pneumonias in patients with inflammatory bowel disease: a 6-year analysis of the Nationwide Inpatient Sample

BackgroundPneumonias are among the most common causes of hospitalization among inflammatory bowel disease (IBD) patients. Guidelines published in 2004 advocate vaccination against Streptococcus pneumoniae and influenza virus. We sought to examine trends in hospitalizations for vaccine preventable pneumonias among IBD patients since the availability of published guidelines, and to identify whether Haemophilus influenzae is a causative organism for pneumonia hospitalizations among IBD patients.MethodsThis cross-sectional study on the Nationwide Inpatient Sample was used to identify admissions for pneumonias in patients with IBD between 2004 and 2009. A multivariate logistic regression analysis was performed comparing IBD patients to controls, accounting for potential confounders.ResultsThere were more admissions for S. pneumoniae pneumonia than influenza virus or H. influenzae (787, 393, and 183 respectively). Crohn’s disease (CD) as well as ulcerative colitis (UC) patients did not demonstrate increased adjusted odds of hospitalization for S. pneumoniae pneumonia (1.08; confidence interval [CI] 0.99–1.17 compared to 0.93; CI 0.82–1.06 respectively). Increased adjusted odds for hospitalization for pneumonias due to influenza virus were seen among UC patients in the bottom quartile of income (1.86; CI 1.46–2.37). Adjusted odds for H. influenzae pneumonia admission in patients with UC and CD patients were increased compared to controls (1.42; CI 1.13–1.79 and 1.28; CI 1.06–1.54, respectively).ConclusionThe study identified lowest income UC patients as having higher adjusted odds, and these patients should be targeted for influenza virus vaccination. Additionally, H. influenzae may be another vaccine preventable cause for pneumonia among IBD patients.

Causal Genomic and Epigenomic Network Analysis emerges as a New Generation of Genetic Studies of Complex Diseases.

Causal Genomic and Epigenomic Network Analysis emerges as a New Generation of Genetic Studies of Complex Diseases.

Stem cells, pluripotency and nuclear reprogramming

The recent demonstration of in vitro reprogramming using transduction of 4 transcription factors by Yamanaka and colleagues represents a major advance in the field. However, major questions regarding the mechanism of in vitro reprogramming need to be understood and will be one focus of the talk.During cellular reprogramming only a small fraction of cells become induced pluripotent stem cells (iPSCs). Previous analyses of gene expression during reprogramming were based on populations of cells and impeded identification of events at the single‐cell level. We utilized two different gene expression technologies to profile 48 genes in single cells at various stages during the reprogramming process. Analysis of early stages revealed considerable variation in gene expression between cells in contrast to late stages. We show that Esrrb, Utf1, Lin28, and Dppa2 have a better potential to predict cells that will become iPSCs, compared to Fbxo15, Fgf4, and Oct4, which were previously suggested to be markers of reprogramming. Our data suggest that stochastic gene expression early in reprogramming is followed by a late sequential phase with Sox2 activation upstream in a gene expression hierarchy. Finally, we demonstrate that subsets of downstream factors derived from the sequential phase can activate the pluripotency circuitry.A major impediment in realizing the potential of ES and iPS cells to study human diseases is the inefficiency of gene targeting. Using Zn finger or TALEN mediated genome editing we have established efficient protocols to target expressed and silent genes in human ES and iPS cells. Finally, our progress in using iPS cells for therapy and for the study of complex human diseases will be summarized.