Studies Of Complex Diseases Research Articles

The Rat: A Model Used in Biomedical Research.

The laboratory rat, Rattus norvegicus, has been used in biomedical research for more than 150years, and in many cases remains the model of choice for studies of physiology, behavior, and complex human disease. This book provides detailed information on a number of methodologies that can be used in rat. This chapter gives an introduction to rat as a species and as a biomedical model, providing historical information, a brief introduction to the current state of rat research, and a perspective on the future of rat as a model for human disease.

Designs and Analytical Methods In Multi-Omics Studies of Complex Diseases

Complex human diseases involve multiple molecular mechanisms and pathways with dynamic relationships. High-throughput technologies enable omics studies interrogating thousands to millions of makers with similar biochemical properties (e.g. transcriptomics for RNA transcripts). However, a single layer of ‘omics’ can only provide limited insights into the biological mechanisms of a disease. Genomic studies discovered many loci and genes associated with disease traits but provided limited insights about their functional roles and the changing disease risk across the life span. DNA, RNA, protein, and metabolite often have complementary roles to jointly perform a certain biological function. Such complementary effects and synergistic interactions between omic layers in the life-course can only be captured by integrative study of multiple molecular layers. Multi-omics approaches integrate data obtained from different omic levels to understand their interrelation and combined influence on the disease processes. Thus, properly designed multi-omics studies may improve the understanding the molecular function and disease etiology. We summarize major omics approaches available in population research, and review integrative approaches and methodologies interrogating multiple omic layers, which enhance the gene discovery and functional analysis of human diseases. We seek to provide analytical recommendations for different types of multi-omics data and study designs to guide the emerging multi-omic research, and to suggest improvement of the existing analytical methods.

Rat Models of Exercise for the Study of Complex Disease.

Variation in exercise capacity is a translationally powerful indicator for overall health and disease. Here we review the basic methods used for development of theoretically based and hypothesis-driven rat models that divide for both exercise capacity and numerous complex disease risks This rat model system was made by selectively breeding genetically heterogeneous rat populations for low and high performance on a speed ramped treadmill running test.

Behavioral impact of return of genetic test results for complex disease: Systematic review and meta-analysis.

Advances in genomewide association studies have made possible the return of genetic risk results for complex diseases. Two concerns about these results are (a) negative psychological consequences and (b) viewing probabilistic results as deterministic, leading to misinterpretation and inappropriate decisions. The present study evaluates these concerns through a meta-analytic review of existing literature. Seventeen genetic testing studies of complex disease, including 1,171 participants and reporting 195 effects, 104 of which were unadjusted for covariates, were meta-analyzed under a random effects model. Diseases included Alzheimer's, cardiovascular and coronary heart disease, lung cancer, melanoma, thrombophilia, and type II diabetes. Six domains of behavioral-psychological reactions were examined. Carriers showed significantly increased self-reported behavior change compared to noncarriers when assessed 6 months or later after results return (Hedges's g = .36, p = .019). Return of genetic testing results for complex disease does not strongly impact self-reported negative behavior or psychological function of at-risk individuals. Return of results does appear to moderately increase self-reported healthy behavior in carriers, although research on objectively observed behavior change is needed. This is a growing area of research, with preliminary results suggesting potential positive implications of genetic testing for complex disease on behavior change. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Hypoxia Enhanced Red Cell Adhesion in Vitro May Identify Patients at Risk for Vasculopathy

Background:Priapism is a serious complication associated with Sickle Cell Disease (SCD) that may be a manifestation of underlying vasculopathy. The Centers for the Study of Complex Diseases of Childhood (CSCCD), comprising independent Comprehensive Sickle Cell Centers, demonstrated an association of priapism with hemolysis.1 Previously, we identified two groups of people with SCD based on red blood cell (RBC) adhesion under hypoxic conditions: those patients whose RBCs showed hypoxia-enhanced adhesion (HEA) and those whose did not (non-HEA).2 Patients with HEA had evidence for more hemolysis in vivo. Here, we aimed to examine (1) the association of HEA with hypoxia in vivo, and (2) RBC adhesion in normoxic and hypoxic conditions in male patients with or without a history of priapism.Methods:This retrospective study was conducted at the Adult Sickle Cell Disease Clinic at the University Hospitals Seidman Cancer Center, in Cleveland, OH between 2015 to 2018. Blood samples were obtained from 26 male subjects (29 samples, 25 HbSS and 1 HbSS HPFH). Adhesion experiments were performed as previously reported by passing surplus whole blood through LN-immobilized microchannels at physiological conditions under both normoxic and hypoxic conditions.2,3 Adherent RBCs were then quantified with microscope after a wash step. The median value was used for data analyses from multiple samples obtained from an individual. Chart review was conducted to examine results of hypoxia testing obtained in vivo as part of routine clinical care.Results:Male subjects with HbSS and a history of priapism had higher HEA in comparison to subjects without a history of priapism (3268 ± 5647 vs. 122 ± 1218, p=0.016). However, there was no significant difference between RBC adhesion of the two groups under normoxic conditions (529 ± 1528 vs. 402 ± 280). More male subjects with priapism had hypoxia in vivo (10 out of 14) than subjects without priapism (5 out of 12). Compared to male subjects with a history of priapism, those without a history of priapism had lower lactate dehydrogenase levels (474 ± 267 vs. 290 ± 215, p=0.008).Conclusions: Our data showed that subjects with a history of priapism had a higher HEA and tended to have more evidence for hypoxia in vivo than did subjects without a history of priapism. Further, male subjects with hypoxia in vivo had more HEA than did those without hypoxia in vivo (not shown). Hypoxia in vivo may cause increased RBC damage (reflected by HEA), hemolysis, nitric oxide depletion, and consequent vasculopathy, resulting in priapism. Hypoxia may be treatable, when identified in subjects with a history priapism in vivo or possibly with HEA in vitro. This could plausibly modify disease severity in some cases.

MicroRNA-Target Interactions Reloaded: Identification of Potentially Functional Sequence Variants Within Validated MicroRNA-Target Interactions.

MicroRNAs (miRNAs) serve as critical regulators of gene expression. However, their binding to target genes can be influenced by genetic variability within the miRNA-target interaction (MTI) sites. We performed an in silico sequence reanalysis to identify novel sequence variants within MTIs with potential functional impacts. A literature search of the PubMed and the Web of Science spanning the years 2008 to April 2018 identified 240 articles reporting MTIs in humans. Sequence reanalysis of reported MTI regions was performed using the Ensembl browser. We found 76 sequence variants within 23 MTIs. We present description of MTIs wherein sequence variants are present within both the mature miRNA seed region and the miRNA target, which we termed miR-gene-target-single nucleotide polymorphism (miR-GenTar-SNP). To the best of our knowledge, this is the first report on copresence of sequence variants within both miRNA gene and the target site. In the course our analyses, the need for extension of current terminology emerged and therefore, novel terminology was introduced: miR-indel, miR-double nucleotide polymorphism (DNP), miR-TS-indel, and miR-TS-DNP. Identification of novel MTI sequence variants is a hitherto understudied, but critical dimension in understanding the complexity of interactions and gene deregulation in various complex diseases. Because such variations might profoundly affect miRNA function, they should be taken into consideration in future research that depends on "variability science" such as precision medicine, human genetics, and genomics in the study of complex diseases. The findings presented herein offer a baseline for further systematic reanalysis of all reported MTIs in human and other species.

ProxECAT: Proxy External Controls Association Test. A new case-control gene region association test using allele frequencies from public controls.

A primary goal of the recent investment in sequencing is to detect novel genetic associations in health and disease improving the development of treatments and playing a critical role in precision medicine. While this investment has resulted in an enormous total number of sequenced genomes, individual studies of complex traits and diseases are often smaller and underpowered to detect rare variant genetic associations. Existing genetic resources such as the Exome Aggregation Consortium (>60,000 exomes) and the Genome Aggregation Database (~140,000 sequenced samples) have the potential to be used as controls in these studies. Fully utilizing these and other existing sequencing resources may increase power and could be especially useful in studies where resources to sequence additional samples are limited. However, to date, these large, publicly available genetic resources remain underutilized, or even misused, in large part due to the lack of statistical methods that can appropriately use this summary level data. Here, we present a new method to incorporate external controls in case-control analysis called ProxECAT (Proxy External Controls Association Test). ProxECAT estimates enrichment of rare variants within a gene region using internally sequenced cases and external controls. We evaluated ProxECAT in simulations and empirical analyses of obesity cases using both low-depth of coverage (7x) whole-genome sequenced controls and ExAC as controls. We find that ProxECAT maintains the expected type I error rate with increased power as the number of external controls increases. With an accompanying R package, ProxECAT enables the use of publicly available allele frequencies as external controls in case-control analysis.

Overview of current mouse models of autism and strategies for their development using CRISPR/Cas9 technology

Autism spectrum disorders (ASD) are a group of highly heterogenous neurological disorders that are believed to have strong genetic component. Due to the limited use of approaches of functional genomics in human medicine, creating adequate animal models for the study of complex human diseases shows great potential. There are several already established mouse models of autism that offer insight into single phenotypic traits, although causes for its complex phenotype have not yet been fully understood. Development of new technologies, such as CRISPR/Cas9, represent great capability for targeted genome engineering and establishment of new animal models. This article provides an up to date overview of current knowledge in the area of autism genomics and describes the potential of CRISPR/Cas9 technology for the establishment of new mouse models, representing sgRNA design as one of the initial steps in planning a CRISPR/Cas9 single knock-out experiment. In addition, it offers an overview of current approaches to behavioural studies, explaining how relevant animal models could be developed.

GtTR: Bayesian estimation of absolute tandem repeat copy number using sequence capture and high throughput sequencing

BackgroundTandem repeats comprise significant proportion of the human genome including coding and regulatory regions. They are highly prone to repeat number variation and nucleotide mutation due to their repetitive and unstable nature, making them a major source of genomic variation between individuals. Despite recent advances in high throughput sequencing, analysis of tandem repeats in the context of complex diseases is still hindered by technical limitations. We report a novel targeted sequencing approach, which allows simultaneous analysis of hundreds of repeats. We developed a Bayesian algorithm, namely – GtTR - which combines information from a reference long-read dataset with a short read counting approach to genotype tandem repeats at population scale. PCR sizing analysis was used for validation.ResultsWe used a PacBio long-read sequenced sample to generate a reference tandem repeat genotype dataset with on average 13% absolute deviation from PCR sizing results. Using this reference dataset GtTR generated estimates of VNTR copy number with accuracy within 95% high posterior density (HPD) intervals of 68 and 83% for capture sequence data and 200X WGS data respectively, improving to 87 and 94% with use of a PCR reference. We show that the genotype resolution increases as a function of depth, such that the median 95% HPD interval lies within 25, 14, 12 and 8% of the its midpoint copy number value for 30X, 200X WGS, 395X and 800X capture sequence data respectively. We validated nine targets by PCR sizing analysis and genotype estimates from sequencing results correlated well with PCR results.ConclusionsThe novel genotyping approach described here presents a new cost-effective method to explore previously unrecognized class of repeat variation in GWAS studies of complex diseases at the population level. Further improvements in accuracy can be obtained by improving accuracy of the reference dataset.

Application of Causal Inference to Genomic Analysis: Advances in Methodology.

The current paradigm of genomic studies of complex diseases is association and correlation analysis. Despite significant progress in dissecting the genetic architecture of complex diseases by genome-wide association studies (GWAS), the identified genetic variants by GWAS can only explain a small proportion of the heritability of complex diseases. A large fraction of genetic variants is still hidden. Association analysis has limited power to unravel mechanisms of complex diseases. It is time to shift the paradigm of genomic analysis from association analysis to causal inference. Causal inference is an essential component for the discovery of mechanism of diseases. This paper will review the major platforms of the genomic analysis in the past and discuss the perspectives of causal inference as a general framework of genomic analysis. In genomic data analysis, we usually consider four types of associations: association of discrete variables (DNA variation) with continuous variables (phenotypes and gene expressions), association of continuous variables (expressions, methylations, and imaging signals) with continuous variables (gene expressions, imaging signals, phenotypes, and physiological traits), association of discrete variables (DNA variation) with binary trait (disease status) and association of continuous variables (gene expressions, methylations, phenotypes, and imaging signals) with binary trait (disease status). In this paper, we will review algorithmic information theory as a general framework for causal discovery and the recent development of statistical methods for causal inference on discrete data, and discuss the possibility of extending the association analysis of discrete variable with disease to the causal analysis for discrete variable and disease.

Joint analysis of multiple phenotypes in association studies using allele-based clustering approach for non-normal distributions.

In the study of complex diseases, several correlated phenotypes are usually measured. There is also increasing evidence showing that testing the association between a single-nucleotide polymorphism (SNP) and multiple-dependent phenotypes jointly is often more powerful than analyzing only one phenotype at a time. Therefore, developing statistical methods to test for genetic association with multiple phenotypes has become increasingly important. In this paper, we develop an Allele-based Clustering Approach (ACA) for the joint analysis of multiple non-normal phenotypes in association studies. In ACA, we consider the alleles at a SNP of interest as a dependent variable with two classes, and the correlated phenotypes as predictors to predict the alleles at the SNP of interest. We perform extensive simulation studies to evaluate the performance of ACA and compare the power of ACA with the powers of Adaptive Fisher's Combination test, Trait-based Association Test that uses Extended Simes procedure, Fisher's Combination test, the standard MANOVA, and the joint model of Multiple Phenotypes. Our simulation studies show that the proposed method has correct type I error rates and is much more powerful than other methods for some non-normal distributions.

Imaging Endophenotypes of Stroke as a Target for Genetic Studies.

Stroke is a heterogeneous disease leading to death of neural tissue and often resulting in the loss of motor and cognitive function. It is the fifth leading cause of death and a leading cause of severe long-term disability in the United States.1 Most strokes (≈80%–90%) are caused by an acute interruption of the brain arterial blood supply because of vascular occlusion, leading to brain tissue ischemia. Approximately 10% to 20% of strokes are caused by blood vessel rupture, leading to hemorrhage. Although a growing number of genetic loci have been identified for the major stroke risk factors, the genetic architecture of stroke and its subtypes remains largely uncharacterized. The use of imaging measures as endophenotypes in genetic studies of stroke is leading to new discoveries and may provide a better understanding of the biological mechanisms underlying stroke pathogenesis. The concept of endophenotype was first developed in the early 1970s by Gottesman and Shields2 for schizophrenia research. An endophenotype was originally defined as a quantitative characteristic of the disease, which cannot be observed by the naked eye (Endo- means internal or inside; Pheno- means showing or appearing).3 It is not a risk factor but rather an expression of the underlying disease liability. Gottesman and Gould3 identified 6 criteria that define an endophenotype (Table). Because endophenotypes are typically quantitative and lie in the causal pathway to the disease but are closer to the gene action than the clinical phenotype,3 they provide greater power than their corresponding clinical phenotypes in gene discovery, as has been shown in the genetic study of other complex diseases.4,5 This review will discuss selected imaging endophenotypes of stroke. Genetic studies referenced in this article mostly focus on genome-wide association studies (GWASs) in large population-based samples. View this table: Table. Criteria Defining an Endophenotype3 …

Identifying essential proteins based on sub-network partition and prioritization by integrating subcellular localization information

Essential proteins are important participants in various life activities and play a vital role in the survival and reproduction of living organisms. Identification of essential proteins from protein-protein interaction (PPI) networks has great significance to facilitate the study of human complex diseases, the design of drugs and the development of bioinformatics and computational science. Studies have shown that highly connected proteins in a PPI network tend to be essential. A series of computational methods have been proposed to identify essential proteins by analyzing topological structures of PPI networks. However, the high noise in the PPI data can degrade the accuracy of essential protein prediction. Moreover, proteins must be located in the appropriate subcellular localization to perform their functions, and only when the proteins are located in the same subcellular localization, it is possible that they can interact with each other. In this paper, we propose a new network-based essential protein discovery method based on sub-network partition and prioritization by integrating subcellular localization information, named SPP. The proposed method SPP was tested on two different yeast PPI networks obtained from DIP database and BioGRID database. The experimental results show that SPP can effectively reduce the effect of false positives in PPI networks and predict essential proteins more accurately compared with other existing computational methods DC, BC, CC, SC, EC, IC, NC.

Glycosylation Profile of Immunoglobulin G Is Cross-Sectionally Associated With Cardiovascular Disease Risk Score and Subclinical Atherosclerosis in Two Independent Cohorts.

One measure of protein glycosylation (GlycA) has been reported to predict higher cardiovascular risk by reflecting inflammatory pathways. The main objective of this study is to assess the role of a comprehensive panel of IgG glycosylation traits on traditional risk factors for cardiovascular disease and on presence of subclinical atherosclerosis in addition to GlycA. We measured 76 IgG glycosylation traits in 2970 women (age range, 40-79 years) from the TwinsUK cohort and correlated it to their estimated 10-year atherosclerotic cardiovascular disease risk score and their carotid and femoral plaque measured by ultrasound imaging. Eight IgG glycan traits are associated with the 10-year atherosclerotic cardiovascular disease risk score after adjusting for multiple tests and for individual risk factors-5 with increased risk and 3 with decreased risk. These glycans replicated in 967 women from ORCADES cohort (Orkney Complex Disease Study), and 6 of them were also associated in 845 men. A linear combination of IgG glycans and GlycA is also associated with presence of carotid (odds ratio, 1.55; 95% confidence interval, 1.25-1.93; P=7.5×10-5) and femoral (odds ratio, 1.32; 95% confidence interval, 1.06-1.64; P=0.01) plaque in a subset of women with atherosclerosis data after adjustment for traditional risk factors. One specific glycosylation trait, GP18-the percentage of FA2BG2S1 glycan in total IgG glycans, was negatively correlated with very-low-density lipoprotein and triglyceride levels in serum and with presence of carotid plaque (odds ratio, 0.60; 95% confidence interval, 0.50-0.71; P=5×10-4). We find molecular pathways linking IgG to arterial lesion formation. Glycosylation traits are independently associated with subclinical atherosclerosis. One specific trait related to the sialylated N-glycan is negatively correlated with cardiovascular disease risk, very-low-density lipoprotein and triglyceride serum levels, and presence of carotid plaque.

Genetic association of molecular traits: A help to identify causative variants in complex diseases.

In the past 15 years, major progresses have been made in the understanding of the genetic basis of regulation of gene expression. These new insights have revolutionized our approach to resolve the genetic variation underlying complex diseases. Gene transcript levels were the first expression phenotypes that were studied. They are heritable and therefore amenable to genome-wide association studies. The genetic variants that modulate them are called expression quantitative trait loci. Their study has been extended to other molecular quantitative trait loci (molQTLs) that regulate gene expression at the various levels, from chromatin state to cellular responses. Altogether, these studies have generated a wealth of basic information on the genome-wide patterns of gene expression and their inter-individual variation. Most importantly, molQTLs have become an invaluable asset in the genetic study of complex diseases. Although the identification of the disease-causing variants on the basis of their overlap with molQTLs requires caution, molQTLs can help to prioritize the relevant candidate gene(s) in the disease-associated regions and bring a functional interpretation of the associated variants, therefore, bridging the gap between genotypes and clinical phenotypes.

New considerations for hiPSC-based models of neuropsychiatric disorders.

The development of human induced pluripotent stem cells (hiPSCs) has made possible patient-specific modeling across the spectrum of human disease. Here we discuss recent advances in psychiatric genomics and post-mortem studies that provide critical insights concerning cell type composition and sample size that should be considered when designing hiPSC-based studies of complex genetic disease. We review recent hiPSC-based models of SZ, in light of our new understanding of critical power limitations in the design of hiPSC-based studies of complex genetic disorders. Three possible solutions are a movement towards genetically stratified cohorts of rare variant patients, application of CRISPR technologies to engineer isogenic neural cells to study the impact of common variants, and integration of advanced genetics and hiPSC-based datasets in future studies. Overall, we emphasize that to advance the reproducibility and relevance of hiPSC-based studies, stem cell biologists must contemplate statistical and biological considerations that are already well accepted in the field of genetics. We conclude with a discussion of the hypothesis of biological convergence of disease - through molecular, cellular, circuit and patient level phenotypes - and how this might emerge through hiPSC-based studies.

Parkinson's disease genetic risk in a midbrain neuronal cell line

In genome-wide association studies of complex diseases, many risk polymorphisms are found to lie in non-coding DNA and likely confer risk through allele-dependent differences in gene regulatory elements. However, because distal regulatory elements can alter gene expression at various distances on linear DNA, the identity of relevant genes is unknown for most risk loci. In Parkinson's disease, at least some genetic risk is likely intrinsic to a neuronal subpopulation of cells in the brain regions affected. In order to compare neuron-relevant methods of pairing risk polymorphisms to target genes as well as to further characterize a single-cell model of a neurodegenerative disease, we used the portionally-dopaminergic, neuronal, mesencephalic-derived cell line LUHMES to dissect differentiation-specific mechanisms of gene expression. We compared genome-wide gene expression in undifferentiated and differentiated cells with genome-wide histone H3K27ac and CTCF-bound regions. Whereas promoters and CTCF binding were largely consistent between differentiated and undifferentiated cells, enhancers were mostly unique. We matched the differentiation-specific appearance or disappearance of enhancers with changes in gene expression and identified 22,057 enhancers paired with 6388 differentially expressed genes by proximity. These enhancers are enriched with at least 13 transcription factor response elements, driving a cluster of genes involved in neurogenesis. We show that differentiated LUHMES cells, but not undifferentiated cells, show enrichment for PD-risk SNPs. Candidate genes for these loci are largely unrelated, though a subset is linked to synaptic vesicle cycling and transport, implying that PD-related disruption of these pathways is intrinsic to dopaminergic neurons.

A Guide to Illumina BeadChip Data Analysis.

The Illumina Infinium BeadChips are a powerful array-based platform for genome-wide DNA methylation profiling at approximately 485,000 (450K) and 850,000 (EPIC) CpG sites across the genome. The platform is used in many large-scale population-based epigenetic studies of complex diseases, environmental exposures, or other experimental conditions. This chapter provides an overview of the key steps in analyzing Illumina BeadChip data. We describe key preprocessing steps including data extraction and quality control as well as normalization strategies. We further present principles and guidelines for conducting association analysis at the individual CpG level as well as more sophisticated pathway-based association tests.

Impact of regulatory variation across human iPSCs and differentiated cells.

Induced pluripotent stem cells (iPSCs) are an essential tool for studying cellular differentiation and cell types that are otherwise difficult to access. We investigated the use of iPSCs and iPSC-derived cells to study the impact of genetic variation on gene regulation across different cell types and as models for studies of complex disease. To do so, we established a panel of iPSCs from 58 well-studied Yoruba lymphoblastoid cell lines (LCLs); 14 of these lines were further differentiated into cardiomyocytes. We characterized regulatory variation across individuals and cell types by measuring gene expression levels, chromatin accessibility, and DNA methylation. Our analysis focused on a comparison of inter-individual regulatory variation across cell types. While most cell-type–specific regulatory quantitative trait loci (QTLs) lie in chromatin that is open only in the affected cell types, we found that 20% of cell-type–specific regulatory QTLs are in shared open chromatin. This observation motivated us to develop a deep neural network to predict open chromatin regions from DNA sequence alone. Using this approach, we were able to use the sequences of segregating haplotypes to predict the effects of common SNPs on cell-type–specific chromatin accessibility.

A comparison of graph- and kernel-based \u2013omics data integration algorithms for classifying complex traits

BackgroundHigh-throughput sequencing data are widely collected and analyzed in the study of complex diseases in quest of improving human health. Well-studied algorithms mostly deal with single data source, and cannot fully utilize the potential of these multi-omics data sources. In order to provide a holistic understanding of human health and diseases, it is necessary to integrate multiple data sources. Several algorithms have been proposed so far, however, a comprehensive comparison of data integration algorithms for classification of binary traits is currently lacking.ResultsIn this paper, we focus on two common classes of integration algorithms, graph-based that depict relationships with subjects denoted by nodes and relationships denoted by edges, and kernel-based that can generate a classifier in feature space. Our paper provides a comprehensive comparison of their performance in terms of various measurements of classification accuracy and computation time. Seven different integration algorithms, including graph-based semi-supervised learning, graph sharpening integration, composite association network, Bayesian network, semi-definite programming-support vector machine (SDP-SVM), relevance vector machine (RVM) and Ada-boost relevance vector machine are compared and evaluated with hypertension and two cancer data sets in our study.In general, kernel-based algorithms create more complex models and require longer computation time, but they tend to perform better than graph-based algorithms. The performance of graph-based algorithms has the advantage of being faster computationally.ConclusionsThe empirical results demonstrate that composite association network, relevance vector machine, and Ada-boost RVM are the better performers. We provide recommendations on how to choose an appropriate algorithm for integrating data from multiple sources.