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Find video protocols related to scientific articles indexed in Pubmed.
DATA SYNTHESIS AND METHOD EVALUATION FOR BRAIN IMAGING GENETICS.
Proc IEEE Int Symp Biomed Imaging
PUBLISHED: 11-20-2014
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Brain imaging genetics is an emergent research field where the association between genetic variations such as single nucleotide polymorphisms (SNPs) and neuroimaging quantitative traits (QTs) is evaluated. Sparse canonical correlation analysis (SCCA) is a bi-multivariate analysis method that has the potential to reveal complex multi-SNP-multi-QT associations. We present initial efforts on evaluating a few SCCA methods for brain imaging genetics. This includes a data synthesis method to create realistic imaging genetics data with known SNP-QT associations, application of three SCCA algorithms to the synthetic data, and comparative study of their performances. Our empirical results suggest, approximating covariance structure using an identity or diagonal matrix, an approach used in these SCCA algorithms, could limit the SCCA capability in identifying the underlying imaging genetics associations. An interesting future direction is to develop enhanced SCCA methods that effectively take into account the covariance structures in the imaging genetics data.
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JOINT IDENTIFICATION OF IMAGING AND PROTEOMICS BIOMARKERS OF ALZHEIMER'S DISEASE USING NETWORK-GUIDED SPARSE LEARNING.
Proc IEEE Int Symp Biomed Imaging
PUBLISHED: 11-20-2014
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Identification of biomarkers for early detection of Alzheimer's disease (AD) is an important research topic. Prior work has shown that multimodal imaging and biomarker data could provide complementary information for prediction of cognitive or AD status. However, the relationship among multiple data modalities are often ignored or oversimplified in prior studies. To address this issue, we propose a network-guided sparse learning model to embrace the complementary information and inter-relationships between modalities. We apply this model to predict cognitive outcome from imaging and proteomic data, and show that the proposed model not only outperforms traditional ones, but also yields stable multimodal biomarkers across cross-validation trials.
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Fetal exposures and perinatal influences on the stool microbiota of premature infants.
J. Matern. Fetal. Neonatal. Med.
PUBLISHED: 11-15-2014
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Abstract Objective: To test the hypothesis that maternal complications significantly affect gut colonization patterns in very low birth weight infants. Methods: 49 serial stool samples were obtained weekly from 9 extremely premature infants enrolled in a prospective longitudinal study. Sequencing of the bacterial 16S rRNA gene from stool samples was performed to approximate the intestinal microbiome. Linear mixed effects models were used to evaluate relationships between perinatal complications and intestinal microbiome development. Results: Subjects with prenatal exposure to a non-sterile intrauterine environment, i.e. PPPROM and chorioamnionitis exposure, were found to have a relatively higher abundance of potentially pathogenic bacteria in the stool across all time points compared to subjects without those exposures, irrespective of exposure to postnatal antibiotics. Compared with those delivered by Caesarean section, vaginally delivered subjects were found to have significantly lower diversity of stool microbiota across all time points, with lower abundance of many genera, most in the family Enterobacteriaceae. Conclusions: We identified persistently increased potential pathogen abundance in the developing stool microbiota of subjects exposed to a non-sterile uterine environment. Maternal complications appear to significantly influence the diversity and bacterial composition of the stool microbiota of premature infants, with findings persisting over time.
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Bayesian Analysis of Glomerular Filtration Rate Trajectories in Kidney Transplant Recipients: A Pilot Study.
Transplantation
PUBLISHED: 11-12-2014
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Detailed modeling and analysis of renal (dys)function trajectories has not been undertaken in kidney transplant recipients. Although previous studies have assumed linear trajectories, this likely represents an oversimplification.
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A novel structure-aware sparse learning algorithm for brain imaging genetics.
Med Image Comput Comput Assist Interv
PUBLISHED: 10-17-2014
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Brain imaging genetics is an emergent research field where the association between genetic variations such as single nucleotide polymorphisms (SNPs) and neuroimaging quantitative traits (QTs) is evaluated. Sparse canonical correlation analysis (SCCA) is a bi-multivariate analysis method that has the potential to reveal complex multi-SNP-multi-QT associations. Most existing SCCA algorithms are designed using the soft threshold strategy, which assumes that the features in the data are independent from each other. This independence assumption usually does not hold in imaging genetic data, and thus inevitably limits the capability of yielding optimal solutions. We propose a novel structure-aware SCCA (denoted as S2CCA) algorithm to not only eliminate the independence assumption for the input data, but also incorporate group-like structure in the model. Empirical comparison with a widely used SCCA implementation, on both simulated and real imaging genetic data, demonstrated that S2CCA could yield improved prediction performance and biologically meaningful findings.
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Clinical characteristics and antibiotic utilization in pediatric patients hospitalized with acute bacterial skin and skin structure infection.
Pediatr. Infect. Dis. J.
PUBLISHED: 09-16-2014
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Hospitalizations for acute bacterial skin and skin structure infection (ABSSSI) in children are increasingly frequent, but little is known about antibiotic utilization. In adults, recent studies suggest substantial opportunity to reduce broad-spectrum antibiotic use and shorten therapy. We sought to determine whether similar opportunity exists in children.
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Transcriptome-guided amyloid imaging genetic analysis via a novel structured sparse learning algorithm.
Bioinformatics
PUBLISHED: 08-28-2014
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Imaging genetics is an emerging field that studies the influence of genetic variation on brain structure and function. The major task is to examine the association between genetic markers such as single-nucleotide polymorphisms (SNPs) and quantitative traits (QTs) extracted from neuroimaging data. The complexity of these datasets has presented critical bioinformatics challenges that require new enabling tools. Sparse canonical correlation analysis (SCCA) is a bi-multivariate technique used in imaging genetics to identify complex multi-SNP-multi-QT associations. However, most of the existing SCCA algorithms are designed using the soft thresholding method, which assumes that the input features are independent from one another. This assumption clearly does not hold for the imaging genetic data. In this article, we propose a new knowledge-guided SCCA algorithm (KG-SCCA) to overcome this limitation as well as improve learning results by incorporating valuable prior knowledge.
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Computational genetics analysis of grey matter density in Alzheimer's disease.
BioData Min
PUBLISHED: 08-22-2014
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Alzheimer's disease is the most common form of progressive dementia and there is currently no known cure. The cause of onset is not fully understood but genetic factors are expected to play a significant role. We present here a bioinformatics approach to the genetic analysis of grey matter density as an endophenotype for late onset Alzheimer's disease. Our approach combines machine learning analysis of gene-gene interactions with large-scale functional genomics data for assessing biological relationships.
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The effects of recombination on phenotypic exploration and robustness in evolution.
Artif. Life
PUBLISHED: 08-22-2014
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Abstract Recombination is a commonly used genetic operator in artificial and computational evolutionary systems. It has been empirically shown to be essential for evolutionary processes. However, little has been done to analyze the effects of recombination on quantitative genotypic and phenotypic properties. The majority of studies only consider mutation, mainly due to the more serious consequences of recombination in reorganizing entire genomes. Here we adopt methods from evolutionary biology to analyze a simple, yet representative, genetic programming method, linear genetic programming. We demonstrate that recombination has less disruptive effects on phenotype than mutation, that it accelerates novel phenotypic exploration, and that it particularly promotes robust phenotypes and evolves genotypic robustness and synergistic epistasis. Our results corroborate an explanation for the prevalence of recombination in complex living organisms, and helps elucidate a better understanding of the evolutionary mechanisms involved in the design of complex artificial evolutionary systems and intelligent algorithms.
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Antibiotic prescribing practices in a multicenter cohort of patients hospitalized for acute bacterial skin and skin structure infection.
Infect Control Hosp Epidemiol
PUBLISHED: 08-20-2014
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Hospitalizations for acute bacterial skin and skin structure infection (ABSSSI) are common. Optimizing antibiotic use for ABSSSIs requires an understanding of current management. The objective of this study was to evaluate antibiotic prescribing practices and factors affecting prescribing in a diverse group of hospitals.
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Building the next generation of quantitative biologists.
Pac Symp Biocomput
PUBLISHED: 08-15-2014
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Many colleges and universities across the globe now offer bachelors, masters, and doctoral degrees, along with certificate programs in bioinformatics. While there is some consensus surrounding curricula competencies, programs vary greatly in their core foci, with some leaning heavily toward the biological sciences and others toward quantitative areas. This allows prospective students to choose a program that best fits their interests and career goals. In the digital age, most scientific fields are facing an enormous growth of data, and as a consequence, the goals and challenges of bioinformatics are rapidly changing; this requires that bioinformatics education also change. In this workshop, we seek to ascertain current trends in bioinformatics education by asking the question, "What are the core competencies all bioinformaticians should have at the end of their training, and how successful have programs been in placing students in desired careers?"
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Applications of bioinformatics to non-coding RNAs in the era of next-generation sequencing.
Pac Symp Biocomput
PUBLISHED: 08-15-2014
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The human genome encodes a large number of non-coding RNAs, which employ a new and crucial layer of biological regulation in addition to proteins. Technical advancement in recent years, particularly, the wide application of next generation sequencing analysis, provide an unprecedented opportunity to identify new non-coding RNAs and investigate their functions and regulatory mechanisms. The aim of this workshop is to bring together experimental and computational biologist to exchange ideas on non-coding RNA studies.
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Using the bipartite human phenotype network to reveal pleiotropy and epistasis beyond the gene.
Pac Symp Biocomput
PUBLISHED: 08-15-2014
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With the rapid increase in the quality and quantity of data generated by modern high-throughput sequencing techniques, there has been a need for innovative methods able to convert this tremendous amount of data into more accessible forms. Networks have been a corner stone of this movement, as they are an intuitive way of representing interaction data, yet they offer a full set of sophisticated statistical tools to analyze the phenomena they model. We propose a novel approach to reveal and analyze pleiotropic and epistatic effects at the genome-wide scale using a bipartite network composed of human diseases, phenotypic traits, and several types of predictive elements (i.e. SNPs, genes, or pathways). We take advantage of publicly available GWAS data, gene and pathway databases, and more to construct networks different levels of granularity, from common genetic variants to entire biological pathways. We use the connections between the layers of the network to approximate the pleiotropy and epistasis effects taking place between the traits and the predictive elements. The global graph-theory based quantitative methods reveal that the levels of pleiotropy and epistasis are comparable for all types of predictive element. The results of the magnified "glaucoma" region of the network demonstrate the existence of well documented interactions, supported by overlapping genes and biological pathway, and more obscure associations. As the amount and complexity of genetic data increases, bipartite, and more generally multipartite networks that combine human diseases and other physical attributes with layers of genetic information, have the potential to become ubiquitous tools in the study of complex genetic and phenotypic interactions.
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Phenotypic robustness and the assortativity signature of human transcription factor networks.
PLoS Comput. Biol.
PUBLISHED: 08-14-2014
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Many developmental, physiological, and behavioral processes depend on the precise expression of genes in space and time. Such spatiotemporal gene expression phenotypes arise from the binding of sequence-specific transcription factors (TFs) to DNA, and from the regulation of nearby genes that such binding causes. These nearby genes may themselves encode TFs, giving rise to a transcription factor network (TFN), wherein nodes represent TFs and directed edges denote regulatory interactions between TFs. Computational studies have linked several topological properties of TFNs - such as their degree distribution - with the robustness of a TFN's gene expression phenotype to genetic and environmental perturbation. Another important topological property is assortativity, which measures the tendency of nodes with similar numbers of edges to connect. In directed networks, assortativity comprises four distinct components that collectively form an assortativity signature. We know very little about how a TFN's assortativity signature affects the robustness of its gene expression phenotype to perturbation. While recent theoretical results suggest that increasing one specific component of a TFN's assortativity signature leads to increased phenotypic robustness, the biological context of this finding is currently limited because the assortativity signatures of real-world TFNs have not been characterized. It is therefore unclear whether these earlier theoretical findings are biologically relevant. Moreover, it is not known how the other three components of the assortativity signature contribute to the phenotypic robustness of TFNs. Here, we use publicly available DNaseI-seq data to measure the assortativity signatures of genome-wide TFNs in 41 distinct human cell and tissue types. We find that all TFNs share a common assortativity signature and that this signature confers phenotypic robustness to model TFNs. Lastly, we determine the extent to which each of the four components of the assortativity signature contributes to this robustness.
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Editorial: pharmacogenetics and molecular medicine: "so close and yet so far".
Curr. Mol. Med.
PUBLISHED: 08-12-2014
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The sequencing of the first human genome in 2001 highlighted remarkable complexity and heterogeneity [1] and brought great anticipation in advancing our understanding of disease. The therapeutic promise implicit in research ventures like the Human Genome Project (HGP) and other advancements in genetic-genomic DNA technology lies within the concept of personalized medicine. A key element of personalized medicine is to develop medical treatment that is tailored to the specific disease process of each patient. Pharmacogenetics and pharmacogenomics (often used together or interchangeably) refer to the study of genetic differences and their effect on drug metabolism, therapeutic response, and adverse reactions (i.e., pharmacokinetics and pharmacodynamics). The genetic information can be used to guide clinical decision-making and optimize patient care. Highlighted in this review series are examples by which the use of pharmacogenetics and pharmacogenomics has promoted the advancement of molecular medicine, and started to bridge the gap between science and medicine through a shared progression across a variety of disciplines. This collection of reviews introduces the field of data science, along with the latest experimental approaches and statistical methods being used to analyze the vast amounts of large-scale, genome-based data from pharmacogenetic-pharmacogenomic studies (Penrod and Moore). Furthermore, genome-wide association studies (GWAS) are outlined as a powerful and effective tool to identify susceptibility loci and targeted pharmacotherapies for complex diseases, such as age-related macular degeneration (AMD) (Rosen, Kaushal, and SanGiovanni). Similarly, the utility of lymphoblastoid cell lines (LCLs) is reviewed as an efficient model system for performing human pharmacogenomic studies in vitro (Jack, Rotroff, and Motsinger-Reif). In terms of clinical studies, the latest pharmacogenetic-pharmacogenomic applications relating to neurological disorders, including Parkinson's and Alzheimer's disease, as well as common mental illnesses, such as schizophrenia (SCZ), autism spectrum disorder (ASD), and attention deficit hyperactivity disorder (ADHD) are outlined (Gilman and Mao). The growing field of anti-obesity medications, together with the genes and gene variants thought to impact their effectiveness is also presented (Guzman and Martin). Among a wide array of cardiovascularrelated topics, the timely issue of "aspirin resistance", along with the cardiovascular risks associated with nonsteroidal anti-inflammatory drugs is explored, as are the underlying genetic factors affecting antithrombotic agents in coronary artery disease and ischemic stroke (Stitham and Hwa). Furthermore, there is a focused review examining the latest U.S. and European clinical trials regarding pharmacogenetic-guided warfarin dosing (Baranova and Maitland-van der Zee), as well as a detailed look into genetic variability and its relation to antihypertensive and lipidlowering medications (Vanichakarn and Stitham). Some of the major obstacles facing pharmacogenetic and pharmacogenomic research, as well as its implementation to mainstream clinical practice are also discussed. In particular, a common hindrance revealed in the series is the lack of consistency and reproducibility across studies. While differences in study design, small sample size, and heterogeneity among patient populations have been noted, the complexity within the genetic basis of disease and heritability is staggering. Even with monogenic disorders, issues such as pleiotropy, variable or incomplete penetrance, as well as inconsistent expressivity, can make genotype-phenotype associations quite difficult [2]. Moreover, these same issues are compounded by the multifaceted nature of polygenic diseases, and coupled with a myriad of potential environmental influences adding to the complexity [3]. As outlined in this review series, tremendous progress has been made to address these limitations however further cross-disciplinary collaborations are needed. The exponential expansion of information (tens of thousands of publications being added annually) makes incorporation of genetic markers into everyday clinical practice both needed and inevitable. Billions of dollars are being invested by both the government and private industry, and the rewards are expected to pay off in the near future [4]. More than a decade has passed since the mapping of the first human genome. Pharmacogenetic and genomic research has revealed thousands of genetic variants that contribute to disease susceptibility, progression, and/or treatment outcomes. Moreover, these advancements have provided tremendous insights into the molecular basis of many diseases, potentially leading to the development of genetic-based therapies and diagnostic tests. But as far as we have come, towards personalized medicine there remains much to be done. We are "so close and yet so far".
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Analysis of an interprofessional home visit assignment: student perceptions of team-based care, home visits, and medication-related problems.
Fam Med
PUBLISHED: 07-25-2014
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Interprofessional education (IPE) is recommended by many as a means by which to prepare clinicians for collaborative practice and a mechanism by which to improve the overall quality of health care. The objective of this study was to determine the impact of an interprofessional medicine-pharmacy student home visit experience on students' self-assessments of skills and abilities related to team-based care and identification of medication-related problems.
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SNP characteristics predict replication success in association studies.
Hum. Genet.
PUBLISHED: 07-10-2014
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Successful independent replication is the most direct approach for distinguishing real genotype-disease associations from false discoveries in genome-wide association studies (GWAS). Selecting SNPs for replication has been primarily based on P values from the discovery stage, although additional characteristics of SNPs may be used to improve replication success. We used disease-associated SNPs from more than 2,000 published GWASs to identify predictors of SNP reproducibility. SNP reproducibility was defined as a proportion of successful replications among all replication attempts. The study reporting association for the first time was considered to be discovery and all consequent studies targeting the same phenotype replications. We found that -Log(P), where P is a P value from the discovery study, is the strongest predictor of the SNP reproducibility. Other significant predictors include type of the SNP (e.g., missense vs intronic SNPs) and minor allele frequency. Features of the genes linked to the disease-associated SNP also predict SNP reproducibility. Based on empirically defined rules, we developed a reproducibility score (RS) to predict SNP reproducibility independently of -Log(P). We used data from two lung cancer GWAS studies as well as recently reported disease-associated SNPs to validate RS. Minus Log(P) outperforms RS when the very top SNPs are selected, while RS works better with relaxed selection criteria. In conclusion, we propose an empirical model to predict SNP reproducibility, which can be used to select SNPs for validation and prioritization.
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Heuristic Identification of Biological Architectures for Simulating Complex Hierarchical Genetic Interactions.
Genet. Epidemiol.
PUBLISHED: 07-09-2014
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Simulation plays an essential role in the development of new computational and statistical methods for the genetic analysis of complex traits. Most simulations start with a statistical model using methods such as linear or logistic regression that specify the relationship between genotype and phenotype. This is appealing due to its simplicity and because these statistical methods are commonly used in genetic analysis. It is our working hypothesis that simulations need to move beyond simple statistical models to more realistically represent the biological complexity of genetic architecture. The goal of the present study was to develop a prototype genotype-phenotype simulation method and software that are capable of simulating complex genetic effects within the context of a hierarchical biology-based framework. Specifically, our goal is to simulate multilocus epistasis or gene-gene interaction where the genetic variants are organized within the framework of one or more genes, their regulatory regions and other regulatory loci. We introduce here the Heuristic Identification of Biological Architectures for simulating Complex Hierarchical Interactions (HIBACHI) method and prototype software for simulating data in this manner. This approach combines a biological hierarchy, a flexible mathematical framework, a liability threshold model for defining disease endpoints, and a heuristic search strategy for identifying high-order epistatic models of disease susceptibility. We provide several simulation examples using genetic models exhibiting independent main effects and three-way epistatic effects.
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Genetic Simulation Tools for Post-Genome Wide Association Studies of Complex Diseases.
Genet. Epidemiol.
PUBLISHED: 06-15-2014
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Genetic simulation programs are used to model data under specified assumptions to facilitate the understanding and study of complex genetic systems. Standardized data sets generated using genetic simulation are essential for the development and application of novel analytical tools in genetic epidemiology studies. With continuing advances in high-throughput genomic technologies and generation and analysis of larger, more complex data sets, there is a need for updating current approaches in genetic simulation modeling. To provide a forum to address current and emerging challenges in this area, the National Cancer Institute (NCI) sponsored a workshop, entitled "Genetic Simulation Tools for Post-Genome Wide Association Studies of Complex Diseases" at the National Institutes of Health (NIH) in Bethesda, Maryland on March 11-12, 2014. The goals of the workshop were to (1) identify opportunities, challenges, and resource needs for the development and application of genetic simulation models; (2) improve the integration of tools for modeling and analysis of simulated data; and (3) foster collaborations to facilitate development and applications of genetic simulation. During the course of the meeting, the group identified challenges and opportunities for the science of simulation, software and methods development, and collaboration. This paper summarizes key discussions at the meeting, and highlights important challenges and opportunities to advance the field of genetic simulation.
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Comparison of the microbiology and antibiotic treatment among diabetic and nondiabetic patients hospitalized for cellulitis or cutaneous abscess.
J Hosp Med
PUBLISHED: 06-13-2014
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Among diabetics, complicated skin infections may involve gram-negative pathogens; however, the microbiology of cellulitis and cutaneous abscess is not well established.
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Bioinformatics challenges in genome-wide association studies (GWAS).
Methods Mol. Biol.
PUBLISHED: 05-30-2014
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Genome-wide association studies (GWAS) are a powerful tool for investigators to examine the human genome to detect genetic risk factors, reveal the genetic architecture of diseases and open up new opportunities for treatment and prevention. However, despite its successes, GWAS have not been able to identify genetic loci that are effective classifiers of disease, limiting their value for genetic testing. This chapter highlights the challenges that lie ahead for GWAS in better identifying disease risk predictors, and how we may address them. In this regard, we review basic concepts regarding GWAS, the technologies used for capturing genetic variation, the missing heritability problem, the need for efficient study design especially for replication efforts, reducing the bias introduced into a dataset, and how to utilize new resources available, such as electronic medical records. We also look to what lies ahead for the field, and the approaches that can be taken to realize the full potential of GWAS.
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Functional genomics annotation of a statistical epistasis network associated with bladder cancer susceptibility.
BioData Min
PUBLISHED: 04-05-2014
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Several different genetic and environmental factors have been identified as independent risk factors for bladder cancer in population-based studies. Recent studies have turned to understanding the role of gene-gene and gene-environment interactions in determining risk. We previously developed the bioinformatics framework of statistical epistasis networks (SEN) to characterize the global structure of interacting genetic factors associated with a particular disease or clinical outcome. By applying SEN to a population-based study of bladder cancer among Caucasians in New Hampshire, we were able to identify a set of connected genetic factors with strong and significant interaction effects on bladder cancer susceptibility.
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Safety and feasibility of dobutamine stress cardiac magnetic resonance for cardiovascular assessment prior to renal transplantation.
J Cardiovasc Med (Hagerstown)
PUBLISHED: 04-05-2014
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Current guidelines recommend cardiovascular risk assessment prior to renal transplantation. There is currently no evidence for the role of cardiovascular magnetic resonance (CMR) in this population, despite an established evidence base in the non-chronic kidney disease (CKD) population. Our aim is to determine the feasibility and safety of dobutamine stress CMR (DSCMR) imaging in the risk stratification of CKD patients awaiting renal transplantation.
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Genetic polymorphisms modify bladder cancer recurrence and survival in a USA population-based prognostic study.
BJU Int.
PUBLISHED: 03-27-2014
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To identify genetic variants that modify bladder cancer prognosis focusing on genes involved in major biological carcinogenesis processes (apoptosis, proliferation, DNA repair, hormone regulation, immune surveillance, and cellular metabolism), as nearly half of patients with bladder cancer experience recurrences reliable predictors of this recurrent phenotype are needed to guide surveillance and treatment.
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An experimental hut study to quantify the effect of DDT and airborne pyrethroids on entomological parameters of malaria transmission.
Malar. J.
PUBLISHED: 03-23-2014
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Current malaria vector control programmes rely on insecticides with rapid contact toxicity. However, spatial repellents can also be applied to reduce man-vector contact, which might ultimately impact malaria transmission. The aim of this study was to quantify effects of airborne pyrethroids from coils and DDT used an indoor residual spray (IRS) on entomological parameters that influence malaria transmission.
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Longitudinal assessment of cognitive changes associated with adjuvant treatment for breast cancer: the impact of APOE and smoking.
Psychooncology
PUBLISHED: 03-10-2014
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This study examined the association of post-treatment changes in cognitive performance, apolipoprotein E (APOE), and smoking in breast cancer patients treated with adjuvant therapy.
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Interaction between allelic variations in vitamin D receptor and retinoid X receptor genes on metabolic traits.
BMC Genet.
PUBLISHED: 03-10-2014
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Low vitamin D status has been shown to be a risk factor for several metabolic traits such as obesity, diabetes and cardiovascular disease. The biological actions of 1, 25-dihydroxyvitamin D, are mediated through the vitamin D receptor (VDR), which heterodimerizes with retinoid X receptor, gamma (RXRG). Hence, we examined the potential interactions between the tagging polymorphisms in the VDR (22 tag SNPs) and RXRG (23 tag SNPs) genes on metabolic outcomes such as body mass index, waist circumference, waist-hip ratio (WHR), high- and low-density lipoprotein (LDL) cholesterols, serum triglycerides, systolic and diastolic blood pressures and glycated haemoglobin in the 1958 British Birth Cohort (1958BC, up to n?=?5,231). We used Multifactor- dimensionality reduction (MDR) program as a non-parametric test to examine for potential interactions between the VDR and RXRG gene polymorphisms in the 1958BC. We used the data from Northern Finland Birth Cohort 1966 (NFBC66, up to n?=?5,316) and Twins UK (up to n?=?3,943) to replicate our initial findings from 1958BC.
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Genome-wide association study for circulating tissue plasminogen activator levels and functional follow-up implicates endothelial STXBP5 and STX2.
Jie Huang, Jennifer E Huffman, Munekazu Yamakuchi, Munekazu Yamkauchi, Stella Trompet, Folkert W Asselbergs, Maria Sabater-Lleal, David-Alexandre Trégouët, Wei-Min Chen, Nicholas L Smith, Marcus E Kleber, So-Youn Shin, Diane M Becker, Weihong Tang, Abbas Dehghan, Andrew D Johnson, Vinh Truong, Lasse Folkersen, Qiong Yang, Tiphaine Oudot-Mellkah, Brendan M Buckley, Jason H Moore, Frances M K Williams, Harry Campbell, Günther Silbernagel, Veronique Vitart, Igor Rudan, Geoffrey H Tofler, Gerjan J Navis, Anita DeStefano, Alan F Wright, Ming-Huei Chen, Anton J M de Craen, Bradford B Worrall, Alicja R Rudnicka, Ann Rumley, Ebony B Bookman, Bruce M Psaty, Fang Chen, Keith L Keene, Oscar H Franco, Bernhard O Böhm, André G Uitterlinden, Angela M Carter, J Wouter Jukema, Naveed Sattar, Joshua C Bis, Mohammad A Ikram, , Michèle M Sale, Barbara McKnight, Myriam Fornage, Ian Ford, Kent Taylor, P Eline Slagboom, Wendy L McArdle, Fang-Chi Hsu, Anders Franco-Cereceda, Alison H Goodall, Lisa R Yanek, Karen L Furie, Mary Cushman, Albert Hofman, Jacqueline C M Witteman, Aaron R Folsom, Saonli Basu, Nena Matijevic, Wiek H van Gilst, James F Wilson, Rudi G J Westendorp, Sekar Kathiresan, Muredach P Reilly, Russell P Tracy, Ozren Polašek, Bernhard R Winkelmann, Peter J Grant, Hans L Hillege, Francois Cambien, David J Stott, Gordon D Lowe, Timothy D Spector, James B Meigs, Winfried März, Per Eriksson, Lewis C Becker, Pierre-Emmanuel Morange, Nicole Soranzo, Scott M Williams, Caroline Hayward, Pim van der Harst, Anders Hamsten, Charles J Lowenstein, David P Strachan, Christopher J O'Donnell.
Arterioscler. Thromb. Vasc. Biol.
PUBLISHED: 02-27-2014
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Tissue plasminogen activator (tPA), a serine protease, catalyzes the conversion of plasminogen to plasmin, the major enzyme responsible for endogenous fibrinolysis. In some populations, elevated plasma levels of tPA have been associated with myocardial infarction and other cardiovascular diseases. We conducted a meta-analysis of genome-wide association studies to identify novel correlates of circulating levels of tPA.
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Optimization of gene set annotations via entropy minimization over variable clusters (EMVC).
Bioinformatics
PUBLISHED: 02-25-2014
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Gene set enrichment has become a critical tool for interpreting the results of high-throughput genomic experiments. Inconsistent annotation quality and lack of annotation specificity, however, limit the statistical power of enrichment methods and make it difficult to replicate enrichment results across biologically similar datasets.
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Risk estimation using probability machines.
BioData Min
PUBLISHED: 02-19-2014
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Logistic regression has been the de facto, and often the only, model used in the description and analysis of relationships between a binary outcome and observed features. It is widely used to obtain the conditional probabilities of the outcome given predictors, as well as predictor effect size estimates using conditional odds ratios.
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Influence networks based on coexpression improve drug target discovery for the development of novel cancer therapeutics.
BMC Syst Biol
PUBLISHED: 01-30-2014
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The demand for novel molecularly targeted drugs will continue to rise as we move forward toward the goal of personalizing cancer treatment to the molecular signature of individual tumors. However, the identification of targets and combinations of targets that can be safely and effectively modulated is one of the greatest challenges facing the drug discovery process. A promising approach is to use biological networks to prioritize targets based on their relative positions to one another, a property that affects their ability to maintain network integrity and propagate information-flow. Here, we introduce influence networks and demonstrate how they can be used to generate influence scores as a network-based metric to rank genes as potential drug targets.
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The multiscale backbone of the human phenotype network based on biological pathways.
BioData Min
PUBLISHED: 01-25-2014
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Networks are commonly used to represent and analyze large and complex systems of interacting elements. In systems biology, human disease networks show interactions between disorders sharing common genetic background. We built pathway-based human phenotype network (PHPN) of over 800 physical attributes, diseases, and behavioral traits; based on about 2,300 genes and 1,200 biological pathways. Using GWAS phenotype-to-genes associations, and pathway data from Reactome, we connect human traits based on the common patterns of human biological pathways, detecting more pleiotropic effects, and expanding previous studies from a gene-centric approach to that of shared cell-processes.
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The ENCODE project and perspectives on pathways.
Genet. Epidemiol.
PUBLISHED: 01-22-2014
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The recently completed ENCODE project is a new source of information on metabolic activity, unveiling knowledge about evolution and similarities among species, refuting the myth that most DNA is "junk" and has no actual function. With this expansive resource comes a challenge: integrating these new layers of information into our current knowledge of single-nucleotide polymorphisms and previously described metabolic pathways with the aim of discovering new genes and pathways related to human diseases and traits. Further, we must determine which computational methods will be most useful in this pursuit. In this paper, we speculate over the possible methods that will emerge in this new, challenging field.
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A system-level pathway-phenotype association analysis using synthetic feature random forest.
Genet. Epidemiol.
PUBLISHED: 01-02-2014
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As the cost of genome-wide genotyping decreases, the number of genome-wide association studies (GWAS) has increased considerably. However, the transition from GWAS findings to the underlying biology of various phenotypes remains challenging. As a result, due to its system-level interpretability, pathway analysis has become a popular tool for gaining insights on the underlying biology from high-throughput genetic association data. In pathway analyses, gene sets representing particular biological processes are tested for significant associations with a given phenotype. Most existing pathway analysis approaches rely on single-marker statistics and assume that pathways are independent of each other. As biological systems are driven by complex biomolecular interactions, embracing the complex relationships between single-nucleotide polymorphisms (SNPs) and pathways needs to be addressed. To incorporate the complexity of gene-gene interactions and pathway-pathway relationships, we propose a system-level pathway analysis approach, synthetic feature random forest (SF-RF), which is designed to detect pathway-phenotype associations without making assumptions about the relationships among SNPs or pathways. In our approach, the genotypes of SNPs in a particular pathway are aggregated into a synthetic feature representing that pathway via Random Forest (RF). Multiple synthetic features are analyzed using RF simultaneously and the significance of a synthetic feature indicates the significance of the corresponding pathway. We further complement SF-RF with pathway-based Statistical Epistasis Network (SEN) analysis that evaluates interactions among pathways. By investigating the pathway SEN, we hope to gain additional insights into the genetic mechanisms contributing to the pathway-phenotype association. We apply SF-RF to a population-based genetic study of bladder cancer and further investigate the mechanisms that help explain the pathway-phenotype associations using SEN. The bladder cancer associated pathways we found are both consistent with existing biological knowledge and reveal novel and plausible hypotheses for future biological validations.
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The genetic interacting landscape of 63 candidate genes in Major Depressive Disorder: an explorative study.
BioData Min
PUBLISHED: 01-01-2014
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Genetic contributions to major depressive disorder (MDD) are thought to result from multiple genes interacting with each other. Different procedures have been proposed to detect such interactions. Which approach is best for explaining the risk of developing disease is unclear. This study sought to elucidate the genetic interaction landscape in candidate genes for MDD by conducting a SNP-SNP interaction analysis using an exhaustive search through 3,704 SNP-markers in 1,732 cases and 1,783 controls provided from the GAIN MDD study. We used three different methods to detect interactions, two logistic regressions models (multiplicative and additive) and one data mining and machine learning (MDR) approach.
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A dietary-wide association study (DWAS) of environmental metal exposure in US children and adults.
PLoS ONE
PUBLISHED: 01-01-2014
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A growing body of evidence suggests that exposure to toxic metals occurs through diet but few studies have comprehensively examined dietary sources of exposure in US populations.
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Diverse convergent evidence in the genetic analysis of complex disease: coordinating omic, informatic, and experimental evidence to better identify and validate risk factors.
BioData Min
PUBLISHED: 01-01-2014
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In omic research, such as genome wide association studies, researchers seek to repeat their results in other datasets to reduce false positive findings and thus provide evidence for the existence of true associations. Unfortunately this standard validation approach cannot completely eliminate false positive conclusions, and it can also mask many true associations that might otherwise advance our understanding of pathology. These issues beg the question: How can we increase the amount of knowledge gained from high throughput genetic data? To address this challenge, we present an approach that complements standard statistical validation methods by drawing attention to both potential false negative and false positive conclusions, as well as providing broad information for directing future research. The Diverse Convergent Evidence approach (DiCE) we propose integrates information from multiple sources (omics, informatics, and laboratory experiments) to estimate the strength of the available corroborating evidence supporting a given association. This process is designed to yield an evidence metric that has utility when etiologic heterogeneity, variable risk factor frequencies, and a variety of observational data imperfections might lead to false conclusions. We provide proof of principle examples in which DiCE identified strong evidence for associations that have established biological importance, when standard validation methods alone did not provide support. If used as an adjunct to standard validation methods this approach can leverage multiple distinct data types to improve genetic risk factor discovery/validation, promote effective science communication, and guide future research directions.
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A classification and characterization of two-locus, pure, strict, epistatic models for simulation and detection.
BioData Min
PUBLISHED: 01-01-2014
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The statistical genetics phenomenon of epistasis is widely acknowledged to confound disease etiology. In order to evaluate strategies for detecting these complex multi-locus disease associations, simulation studies are required. The development of the GAMETES software for the generation of complex genetic models, has provided the means to randomly generate an architecturally diverse population of epistatic models that are both pure and strict, i.e. all n loci, but no fewer, are predictive of phenotype. Previous theoretical work characterizing complex genetic models has yet to examine pure, strict, epistasis which should be the most challenging to detect. This study addresses three goals: (1) Classify and characterize pure, strict, two-locus epistatic models, (2) Investigate the effect of model 'architecture' on detection difficulty, and (3) Explore how adjusting GAMETES constraints influences diversity in the generated models.
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Why epistasis is important for tackling complex human disease genetics.
Genome Med
PUBLISHED: 01-01-2014
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Epistasis has been dismissed by some as having little role in the genetic architecture of complex human disease. The authors argue that this view is the result of a misconception and explain why exploring epistasis is likely to be crucial to understanding and predicting complex disease.
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Predicting targeted drug combinations based on Pareto optimal patterns of coexpression network connectivity.
Genome Med
PUBLISHED: 01-01-2014
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Molecularly targeted drugs promise a safer and more effective treatment modality than conventional chemotherapy for cancer patients. However, tumors are dynamic systems that readily adapt to these agents activating alternative survival pathways as they evolve resistant phenotypes. Combination therapies can overcome resistance but finding the optimal combinations efficiently presents a formidable challenge. Here we introduce a new paradigm for the design of combination therapy treatment strategies that exploits the tumor adaptive process to identify context-dependent essential genes as druggable targets.
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A Multi-Core Parallelization Strategy for Statistical Significance Testing in Learning Classifier Systems.
Evol Intell
PUBLISHED: 12-21-2013
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Permutation-based statistics for evaluating the significance of class prediction, predictive attributes, and patterns of association have only appeared within the learning classifier system (LCS) literature since 2012. While still not widely utilized by the LCS research community, formal evaluations of test statistic confidence are imperative to large and complex real world applications such as genetic epidemiology where it is standard practice to quantify the likelihood that a seemingly meaningful statistic could have been obtained purely by chance. LCS algorithms are relatively computationally expensive on their own. The compounding requirements for generating permutation-based statistics may be a limiting factor for some researchers interested in applying LCS algorithms to real world problems. Technology has made LCS parallelization strategies more accessible and thus more popular in recent years. In the present study we examine the benefits of externally parallelizing a series of independent LCS runs such that permutation testing with cross validation becomes more feasible to complete on a single multi-core workstation. We test our python implementation of this strategy in the context of a simulated complex genetic epidemiological data mining problem. Our evaluations indicate that as long as the number of concurrent processes does not exceed the number of CPU cores, the speedup achieved is approximately linear.
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Assessing changes in volatile general anesthetic sensitivity of mice after local or systemic pharmacological intervention.
J Vis Exp
PUBLISHED: 11-07-2013
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One desirable endpoint of general anesthesia is the state of unconsciousness, also known as hypnosis. Defining the hypnotic state in animals is less straightforward than it is in human patients. A widely used behavioral surrogate for hypnosis in rodents is the loss of righting reflex (LORR), or the point at which the animal no longer responds to their innate instinct to avoid the vulnerability of dorsal recumbency. We have developed a system to assess LORR in 24 mice simultaneously while carefully controlling for potential confounds, including temperature fluctuations and varying gas flows. These chambers permit reliable assessment of anesthetic sensitivity as measured by latency to return of the righting reflex (RORR) following a fixed anesthetic exposure. Alternatively, using stepwise increases (or decreases) in anesthetic concentration, the chambers also enable determination of a populations sensitivity to induction (or emergence) as measured by EC50 and Hill slope. Finally, the controlled environmental chambers described here can be adapted for a variety of alternative uses, including inhaled delivery of other drugs, toxicology studies, and simultaneous real-time monitoring of vital signs.
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Recurrent tissue-specific mtDNA mutations are common in humans.
PLoS Genet.
PUBLISHED: 11-01-2013
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Mitochondrial DNA (mtDNA) variation can affect phenotypic variation; therefore, knowing its distribution within and among individuals is of importance to understanding many human diseases. Intra-individual mtDNA variation (heteroplasmy) has been generally assumed to be random. We used massively parallel sequencing to assess heteroplasmy across ten tissues and demonstrate that in unrelated individuals there are tissue-specific, recurrent mutations. Certain tissues, notably kidney, liver and skeletal muscle, displayed the identical recurrent mutations that were undetectable in other tissues in the same individuals. Using RFLP analyses we validated one of the tissue-specific mutations in the two sequenced individuals and replicated the patterns in two additional individuals. These recurrent mutations all occur within or in very close proximity to sites that regulate mtDNA replication, strongly implying that these variations alter the replication dynamics of the mutated mtDNA genome. These recurrent variants are all independent of each other and do not occur in the mtDNA coding regions. The most parsimonious explanation of the data is that these frequently repeated mutations experience tissue-specific positive selection, probably through replication advantage.
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No-boundary thinking in bioinformatics research.
BioData Min
PUBLISHED: 09-05-2013
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Currently there are definitions from many agencies and research societies defining "bioinformatics" as deriving knowledge from computational analysis of large volumes of biological and biomedical data. Should this be the bioinformatics research focus? We will discuss this issue in this review article. We would like to promote the idea of supporting human-infrastructure (HI) with no-boundary thinking (NT) in bioinformatics (HINT).
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Let it snow: how snowfall and injury mechanism affect ski and snowboard injuries in Vail, Colorado, 2011-2012.
J Trauma Acute Care Surg
PUBLISHED: 07-27-2013
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Current research examining the impact of mechanism of injury and daily snowfall amounts on injury severity among skiers and snowboarders is limited. The purpose of this study was to define correlations between injury mechanism and daily snowfall on injury patterns and severity among skiers and snowboarders.
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"Batch" Kinetics in Flow: Online IR Analysis and Continuous Control.
Angew. Chem. Int. Ed. Engl.
PUBLISHED: 07-24-2013
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Currently, kinetic data is either collected under steady-state conditions in flow or by generating time-series data in batch. Batch experiments are generally considered to be more suitable for the generation of kinetic data because of the ability to collect data from many time points in a single experiment. Now, a method that rapidly generates time-series reaction data from flow reactors by continuously manipulating the flow rate and reaction temperature has been developed. This approach makes use of inline IR analysis and an automated microreactor system, which allowed for rapid and tight control of the operating conditions. The conversion/residence time profiles at several temperatures were used to fit parameters to a kinetic model. This method requires significantly less time and a smaller amount of starting material compared to one-at-a-time flow experiments, and thus allows for the rapid generation of kinetic data.
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Epistasis, complexity, and multifactor dimensionality reduction.
Methods Mol. Biol.
PUBLISHED: 06-13-2013
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Genome-wide association studies (GWASs) and other high-throughput initiatives have led to an information explosion in human genetics and genetic epidemiology. Conversion of this wealth of new information about genomic variation to knowledge about public health and human biology will depend critically on the complexity of the genotype to phenotype mapping relationship. We review here computational approaches to genetic analysis that embrace, rather than ignore, the complexity of human health. We focus on multifactor dimensionality reduction (MDR) as an approach for modeling one of these complexities: epistasis or gene-gene interaction.
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Key genes for modulating information flow play a temporal role as breast tumor coexpression networks are dynamically rewired by letrozole.
BMC Med Genomics
PUBLISHED: 05-07-2013
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Genes do not act in isolation but instead as part of complex regulatory networks. To understand how breast tumors adapt to the presence of the drug letrozole, at the molecular level, it is necessary to consider how the expression levels of genes in these networks change relative to one another.
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Continuous correction of differential path length factor in near-infrared spectroscopy.
J Biomed Opt
PUBLISHED: 05-04-2013
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In continuous-wave near-infrared spectroscopy (CW-NIRS), changes in the concentration of oxyhemoglobin and deoxyhemoglobin can be calculated by solving a set of linear equations from the modified Beer-Lambert Law. Cross-talk error in the calculated hemodynamics can arise from inaccurate knowledge of the wavelength-dependent differential path length factor (DPF). We apply the extended Kalman filter (EKF) with a dynamical systems model to calculate relative concentration changes in oxy- and deoxyhemoglobin while simultaneously estimating relative changes in DPF. Results from simulated and experimental CW-NIRS data are compared with results from a weighted least squares (WLSQ) method. The EKF method was found to effectively correct for artificially introduced errors in DPF and to reduce the cross-talk error in simulation. With experimental CW-NIRS data, the hemodynamic estimates from EKF differ significantly from the WLSQ (p < 0.001). The cross-correlations among residuals at different wavelengths were found to be significantly reduced by the EKF method compared to WLSQ in three physiologically relevant spectral bands 0.04 to 0.15 Hz, 0.15 to 0.4 Hz and 0.4 to 2.0 Hz (p < 0.001). This observed reduction in residual cross-correlation is consistent with reduced cross-talk error in the hemodynamic estimates from the proposed EKF method.
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Using a new odour-baited device to explore options for luring and killing outdoor-biting malaria vectors: a report on design and field evaluation of the Mosquito Landing Box.
Parasit Vectors
PUBLISHED: 03-28-2013
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Mosquitoes that bite people outdoors can sustain malaria transmission even where effective indoor interventions such as bednets or indoor residual spraying are already widely used. Outdoor tools may therefore complement current indoor measures and improve control. We developed and evaluated a prototype mosquito control device, the Mosquito Landing Box (MLB), which is baited with human odours and treated with mosquitocidal agents. The findings are used to explore technical options and challenges relevant to luring and killing outdoor-biting malaria vectors in endemic settings.
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Taxis assays measure directional movement of mosquitoes to olfactory cues.
Parasit Vectors
PUBLISHED: 03-27-2013
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Malaria control methods targeting indoor-biting mosquitoes have limited impact on vectors that feed and rest outdoors. Exploiting mosquito olfactory behaviour to reduce blood-feeding outdoors might be a sustainable approach to complement existing control strategies. Methodologies that can objectively quantify responses to odour under realistic field conditions and allow high-throughput screening of many compounds are required for development of effective odour-based control strategies.
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A meta-analysis identifies new loci associated with body mass index in individuals of African ancestry.
Keri L Monda, Gary K Chen, Kira C Taylor, Cameron Palmer, Todd L Edwards, Leslie A Lange, Maggie C Y Ng, Adebowale A Adeyemo, Matthew A Allison, Lawrence F Bielak, Guanjie Chen, Mariaelisa Graff, Marguerite R Irvin, Suhn K Rhie, Guo Li, Yongmei Liu, Youfang Liu, Yingchang Lu, Michael A Nalls, Yan V Sun, Mary K Wojczynski, Lisa R Yanek, Melinda C Aldrich, Adeyinka Ademola, Christopher I Amos, Elisa V Bandera, Cathryn H Bock, Angela Britton, Ulrich Broeckel, Quiyin Cai, Neil E Caporaso, Chris S Carlson, John Carpten, Graham Casey, Wei-Min Chen, Fang Chen, Yii-Der I Chen, Charleston W K Chiang, Gerhard A Coetzee, Ellen Demerath, Sandra L Deming-Halverson, Ryan W Driver, Patricia Dubbert, Mary F Feitosa, Ye Feng, Barry I Freedman, Elizabeth M Gillanders, Omri Gottesman, Xiuqing Guo, Talin Haritunians, Tamara Harris, Curtis C Harris, Anselm J M Hennis, Dena G Hernandez, Lorna H McNeill, Timothy D Howard, Barbara V Howard, Virginia J Howard, Karen C Johnson, Sun J Kang, Brendan J Keating, Suzanne Kolb, Lewis H Kuller, Abdullah Kutlar, Carl D Langefeld, Guillaume Lettre, Kurt Lohman, Vaneet Lotay, Helen Lyon, JoAnn E Manson, William Maixner, Yan A Meng, Kristine R Monroe, Imran Morhason-Bello, Adam B Murphy, Josyf C Mychaleckyj, Rajiv Nadukuru, Katherine L Nathanson, Uma Nayak, Amidou N'Diaye, Barbara Nemesure, Suh-Yuh Wu, M Cristina Leske, Christine Neslund-Dudas, Marian Neuhouser, Sarah Nyante, Heather Ochs-Balcom, Adesola Ogunniyi, Temidayo O Ogundiran, Oladosu Ojengbede, Olufunmilayo I Olopade, Julie R Palmer, Edward A Ruiz-Narváez, Nicholette D Palmer, Michael F Press, Evandine Rampersaud, Laura J Rasmussen-Torvik, Jorge L Rodriguez-Gil, Babatunde Salako, Eric E Schadt, Ann G Schwartz, Daniel A Shriner, David Siscovick, Shad B Smith, Sylvia Wassertheil-Smoller, Elizabeth K Speliotes, Margaret R Spitz, Lara Sucheston, Herman Taylor, Bamidele O Tayo, Margaret A Tucker, David J Van Den Berg, Digna R Velez Edwards, Zhaoming Wang, John K Wiencke, Thomas W Winkler, John S Witte, Margaret Wrensch, Xifeng Wu, James J Yang, Albert M Levin, Taylor R Young, Neil A Zakai, Mary Cushman, Krista A Zanetti, Jing Hua Zhao, Wei Zhao, Yonglan Zheng, Jie Zhou, Regina G Ziegler, Joseph M Zmuda, Jyotika K Fernandes, Gary S Gilkeson, Diane L Kamen, Kelly J Hunt, Ida J Spruill, Christine B Ambrosone, Stefan Ambs, Donna K Arnett, Larry Atwood, Diane M Becker, Sonja I Berndt, Leslie Bernstein, William J Blot, Ingrid B Borecki, Erwin P Bottinger, Donald W Bowden, Gregory Burke, Stephen J Chanock, Richard S Cooper, Jingzhong Ding, David Duggan, Michele K Evans, Caroline Fox, W Timothy Garvey, Jonathan P Bradfield, Hakon Hakonarson, Struan F A Grant, Ann Hsing, Lisa Chu, Jennifer J Hu, Dezheng Huo, Sue A Ingles, Esther M John, Joanne M Jordan, Edmond K Kabagambe, Sharon L R Kardia, Rick A Kittles, Phyllis J Goodman, Eric A Klein, Laurence N Kolonel, Loic Le Marchand, Simin Liu, Barbara McKnight, Robert C Millikan, Thomas H Mosley, Badri Padhukasahasram, L Keoki Williams, Sanjay R Patel, Ulrike Peters, Curtis A Pettaway, Patricia A Peyser, Bruce M Psaty, Susan Redline, Charles N Rotimi, Benjamin A Rybicki, Michèle M Sale, Pamela J Schreiner, Lisa B Signorello, Andrew B Singleton, Janet L Stanford, Sara S Strom, Michael J Thun, Mara Vitolins, Wei Zheng, Jason H Moore, Scott M Williams, Shamika Ketkar, Xiaofeng Zhu, Alan B Zonderman, , Charles Kooperberg, George J Papanicolaou, Brian E Henderson, Alex P Reiner, Joel N Hirschhorn, Ruth J F Loos, Kari E North, Christopher A Haiman.
Nat. Genet.
PUBLISHED: 03-18-2013
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Genome-wide association studies (GWAS) have identified 36 loci associated with body mass index (BMI), predominantly in populations of European ancestry. We conducted a meta-analysis to examine the association of >3.2 million SNPs with BMI in 39,144 men and women of African ancestry and followed up the most significant associations in an additional 32,268 individuals of African ancestry. We identified one new locus at 5q33 (GALNT10, rs7708584, P = 3.4 × 10(-11)) and another at 7p15 when we included data from the GIANT consortium (MIR148A-NFE2L3, rs10261878, P = 1.2 × 10(-10)). We also found suggestive evidence of an association at a third locus at 6q16 in the African-ancestry sample (KLHL32, rs974417, P = 6.9 × 10(-8)). Thirty-two of the 36 previously established BMI variants showed directionally consistent effect estimates in our GWAS (binomial P = 9.7 × 10(-7)), five of which reached genome-wide significance. These findings provide strong support for shared BMI loci across populations, as well as for the utility of studying ancestrally diverse populations.
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Genome-wide association analysis of blood-pressure traits in African-ancestry individuals reveals common associated genes in African and non-African populations.
Nora Franceschini, Ervin Fox, Zhaogong Zhang, Todd L Edwards, Michael A Nalls, Yun Ju Sung, Bamidele O Tayo, Yan V Sun, Omri Gottesman, Adebawole Adeyemo, Andrew D Johnson, J Hunter Young, Ken Rice, Qing Duan, Fang Chen, Yun Li, Hua Tang, Myriam Fornage, Keith L Keene, Jeanette S Andrews, Jennifer A Smith, Jessica D Faul, Zhang Guangfa, Wei Guo, Yu Liu, Sarah S Murray, Solomon K Musani, Sathanur Srinivasan, Digna R Velez Edwards, Heming Wang, Lewis C Becker, Pascal Bovet, Murielle Bochud, Ulrich Broeckel, Michel Burnier, Cara Carty, Daniel I Chasman, Georg Ehret, Wei-Min Chen, Guanjie Chen, Wei Chen, Jingzhong Ding, Albert W Dreisbach, Michele K Evans, Xiuqing Guo, Melissa E Garcia, Rich Jensen, Margaux F Keller, Guillaume Lettre, Vaneet Lotay, Lisa W Martin, Jason H Moore, Alanna C Morrison, Thomas H Mosley, Adesola Ogunniyi, Walter Palmas, George Papanicolaou, Alan Penman, Joseph F Polak, Paul M Ridker, Babatunde Salako, Andrew B Singleton, Daniel Shriner, Kent D Taylor, Ramachandran Vasan, Kerri Wiggins, Scott M Williams, Lisa R Yanek, Wei Zhao, Alan B Zonderman, Diane M Becker, Gerald Berenson, Eric Boerwinkle, Erwin Bottinger, Mary Cushman, Charles Eaton, Fredrik Nyberg, Gerardo Heiss, Joel N Hirschhron, Virginia J Howard, Konrad J Karczewsk, Matthew B Lanktree, Kiang Liu, Yongmei Liu, Ruth Loos, Karen Margolis, Michael Snyder, , Bruce M Psaty, Nicholas J Schork, David R Weir, Charles N Rotimi, Michèle M Sale, Tamara Harris, Sharon L R Kardia, Steven C Hunt, Donna Arnett, Susan Redline, Richard S Cooper, Neil J Risch, D C Rao, Jerome I Rotter, Aravinda Chakravarti, Alex P Reiner, Daniel Levy, Brendan J Keating, Xiaofeng Zhu.
Am. J. Hum. Genet.
PUBLISHED: 03-01-2013
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High blood pressure (BP) is more prevalent and contributes to more severe manifestations of cardiovascular disease (CVD) in African Americans than in any other United States ethnic group. Several small African-ancestry (AA) BP genome-wide association studies (GWASs) have been published, but their findings have failed to replicate to date. We report on a large AA BP GWAS meta-analysis that includes 29,378 individuals from 19 discovery cohorts and subsequent replication in additional samples of AA (n = 10,386), European ancestry (EA) (n = 69,395), and East Asian ancestry (n = 19,601). Five loci (EVX1-HOXA, ULK4, RSPO3, PLEKHG1, and SOX6) reached genome-wide significance (p < 1.0 × 10(-8)) for either systolic or diastolic BP in a transethnic meta-analysis after correction for multiple testing. Three of these BP loci (EVX1-HOXA, RSPO3, and PLEKHG1) lack previous associations with BP. We also identified one independent signal in a known BP locus (SOX6) and provide evidence for fine mapping in four additional validated BP loci. We also demonstrate that validated EA BP GWAS loci, considered jointly, show significant effects in AA samples. Consequently, these findings suggest that BP loci might have universal effects across studied populations, demonstrating that multiethnic samples are an essential component in identifying, fine mapping, and understanding their trait variability.
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Role of genetic heterogeneity and epistasis in bladder cancer susceptibility and outcome: a learning classifier system approach.
J Am Med Inform Assoc
PUBLISHED: 02-26-2013
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Detecting complex patterns of association between genetic or environmental risk factors and disease risk has become an important target for epidemiological research. In particular, strategies that provide multifactor interactions or heterogeneous patterns of association can offer new insights into association studies for which traditional analytic tools have had limited success.
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Statistical epistasis networks reduce the computational complexity of searching three-locus genetic models.
Pac Symp Biocomput
PUBLISHED: 02-21-2013
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The rapid development of sequencing technologies makes thousands to millions of genetic attributes available for testing associations with various biological traits. Searching this enormous high-dimensional data space imposes a great computational challenge in genome-wide association studies. We introduce a network-based approach to supervise the search for three-locus models of disease susceptibility. Such statistical epistasis networks (SEN) are built using strong pairwise epistatic interactions and provide a global interaction map to search for higher-order interactions by prioritizing genetic attributes clustered together in the networks. Applying this approach to a population-based bladder cancer dataset, we found a high susceptibility three-way model of genetic variations in DNA repair and immune regulation pathways, which holds great potential for studying the etiology of bladder cancer with further biological validations. We demonstrate that our SEN-supervised search is able to find a small subset of three-locus models with significantly high associations at a substantially reduced computational cost.
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Admixture mapping in lupus identifies multiple functional variants within IFIH1 associated with apoptosis, inflammation, and autoantibody production.
PLoS Genet.
PUBLISHED: 02-18-2013
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Systemic lupus erythematosus (SLE) is an inflammatory autoimmune disease with a strong genetic component. African-Americans (AA) are at increased risk of SLE, but the genetic basis of this risk is largely unknown. To identify causal variants in SLE loci in AA, we performed admixture mapping followed by fine mapping in AA and European-Americans (EA). Through genome-wide admixture mapping in AA, we identified a strong SLE susceptibility locus at 2q22-24 (LOD=6.28), and the admixture signal is associated with the European ancestry (ancestry risk ratio ~1.5). Large-scale genotypic analysis on 19,726 individuals of African and European ancestry revealed three independently associated variants in the IFIH1 gene: an intronic variant, rs13023380 [P(meta) = 5.20×10(-14); odds ratio, 95% confidence interval = 0.82 (0.78-0.87)], and two missense variants, rs1990760 (Ala946Thr) [P(meta) = 3.08×10(-7); 0.88 (0.84-0.93)] and rs10930046 (Arg460His) [P(dom) = 1.16×10(-8); 0.70 (0.62-0.79)]. Both missense variants produced dramatic phenotypic changes in apoptosis and inflammation-related gene expression. We experimentally validated function of the intronic SNP by DNA electrophoresis, protein identification, and in vitro protein binding assays. DNA carrying the intronic risk allele rs13023380 showed reduced binding efficiency to a cellular protein complex including nucleolin and lupus autoantigen Ku70/80, and showed reduced transcriptional activity in vivo. Thus, in SLE patients, genetic susceptibility could create a biochemical imbalance that dysregulates nucleolin, Ku70/80, or other nucleic acid regulatory proteins. This could promote antibody hypermutation and auto-antibody generation, further destabilizing the cellular network. Together with molecular modeling, our results establish a distinct role for IFIH1 in apoptosis, inflammation, and autoantibody production, and explain the molecular basis of these three risk alleles for SLE pathogenesis.
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The influence of assortativity on the robustness and evolvability of gene regulatory networks upon gene birth.
J. Theor. Biol.
PUBLISHED: 02-15-2013
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Gene regulatory networks (GRNs) represent the interactions between genes and gene products, which drive the gene expression patterns that produce cellular phenotypes. GRNs display a number of characteristics that are beneficial for the development and evolution of organisms. For example, they are often robust to genetic perturbation, such as mutations in regulatory regions or loss of gene function. Simultaneously, GRNs are often evolvable as these genetic perturbations are occasionally exploited to innovate novel regulatory programs. Several topological properties, such as degree distribution, are known to influence the robustness and evolvability of GRNs. Assortativity, which measures the propensity of nodes of similar connectivity to connect to one another, is a separate topological property that has recently been shown to influence the robustness of GRNs to point mutations in cis-regulatory regions. However, it remains to be seen how assortativity may influence the robustness and evolvability of GRNs to other forms of genetic perturbation, such as gene birth via duplication or de novo origination. Here, we employ a computational model of genetic regulation to investigate whether the assortativity of a GRN influences its robustness and evolvability upon gene birth. We find that the robustness of a GRN generally increases with increasing assortativity, while its evolvability generally decreases. However, the rate of change in robustness outpaces that of evolvability, resulting in an increased proportion of assortative GRNs that are simultaneously robust and evolvable. By providing a mechanistic explanation for these observations, this work extends our understanding of how the assortativity of a GRN influences its robustness and evolvability upon gene birth.
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Comparative field evaluation of combinations of long-lasting insecticide treated nets and indoor residual spraying, relative to either method alone, for malaria prevention in an area where the main vector is Anopheles arabiensis.
Parasit Vectors
PUBLISHED: 02-12-2013
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Long-lasting insecticidal nets (LLINs) and indoor residual spraying (IRS) are commonly used together in the same households to improve malaria control despite inconsistent evidence on whether such combinations actually offer better protection than nets alone or IRS alone.
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Multifactor dimensionality reduction reveals a three-locus epistatic interaction associated with susceptibility to pulmonary tuberculosis.
BioData Min
PUBLISHED: 02-11-2013
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Identifying high-order genetics associations with non-additive (i.e. epistatic) effects in population-based studies of common human diseases is a computational challenge. Multifactor dimensionality reduction (MDR) is a machine learning method that was designed specifically for this problem. The goal of the present study was to apply MDR to mining high-order epistatic interactions in a population-based genetic study of tuberculosis (TB).
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Identification of SNPs associated with variola virus virulence.
BioData Min
PUBLISHED: 02-11-2013
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Decades after the eradication of smallpox, its etiological agent, variola virus (VARV), remains a threat as a potential bioweapon. Outbreaks of smallpox around the time of the global eradication effort exhibited variable case fatality rates (CFRs), likely attributable in part to complex viral genetic determinants of smallpox virulence. We aimed to identify genome-wide single nucleotide polymorphisms associated with CFR. We evaluated unadjusted and outbreak geographic location-adjusted models of single SNPs and two- and three-way interactions between SNPs.
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An information-gain approach to detecting three-way epistatic interactions in genetic association studies.
J Am Med Inform Assoc
PUBLISHED: 02-08-2013
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Epistasis has been historically used to describe the phenomenon that the effect of a given gene on a phenotype can be dependent on one or more other genes, and is an essential element for understanding the association between genetic and phenotypic variations. Quantifying epistasis of orders higher than two is very challenging due to both the computational complexity of enumerating all possible combinations in genome-wide data and the lack of efficient and effective methodologies.
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ViSEN: methodology and software for visualization of statistical epistasis networks.
Genet. Epidemiol.
PUBLISHED: 02-05-2013
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The nonlinear interaction effect among multiple genetic factors, i.e. epistasis, has been recognized as a key component in understanding the underlying genetic basis of complex human diseases and phenotypic traits. Due to the statistical and computational complexity, most epistasis studies are limited to interactions with an order of two. We developed ViSEN to analyze and visualize epistatic interactions of both two-way and three-way. ViSEN not only identifies strong interactions among pairs or trios of genetic attributes, but also provides a global interaction map that shows neighborhood and clustering structures. This visualized information could be very helpful to infer the underlying genetic architecture of complex diseases and to generate plausible hypotheses for further biological validations. ViSEN is implemented in Java and freely available at https://sourceforge.net/projects/visen/.
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Robustness, evolvability, and the logic of genetic regulation.
Artif. Life
PUBLISHED: 02-01-2013
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Abstract In gene regulatory circuits, the expression of individual genes is commonly modulated by a set of regulating gene products, which bind to a genes cis-regulatory region. This region encodes an input-output function, referred to as signal-integration logic, that maps a specific combination of regulatory signals (inputs) to a particular expression state (output) of a gene. The space of all possible signal-integration functions is vast and the mapping from input to output is many-to-one: For the same set of inputs, many functions (genotypes) yield the same expression output (phenotype). Here, we exhaustively enumerate the set of signal-integration functions that yield identical gene expression patterns within a computational model of gene regulatory circuits. Our goal is to characterize the relationship between robustness and evolvability in the signal-integration space of regulatory circuits, and to understand how these properties vary between the genotypic and phenotypic scales. Among other results, we find that the distributions of genotypic robustness are skewed, so that the majority of signal-integration functions are robust to perturbation. We show that the connected set of genotypes that make up a given phenotype are constrained to specific regions of the space of all possible signal-integration functions, but that as the distance between genotypes increases, so does their capacity for unique innovations. In addition, we find that robust phenotypes are (i) evolvable, (ii) easily identified by random mutation, and (iii) mutationally biased toward other robust phenotypes. We explore the implications of these latter observations for mutation-based evolution by conducting random walks between randomly chosen source and target phenotypes. We demonstrate that the time required to identify the target phenotype is independent of the properties of the source phenotype.
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Distinct patterns of DNA methylation in conventional adenomas involving the right and left colon.
Mod. Pathol.
PUBLISHED: 01-22-2013
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Recent studies have shown two distinct non-CIMP methylation clusters in colorectal cancer, raising the possibility that DNA methylation, involving non-CIMP genes, may play a role in the conventional adenoma-carcinoma pathway. A total of 135 adenomas (65 left colon and 70 right colon) were profiled for epigenome-wide DNA methylation using the Illumina HumanMethylation450 BeadChip. A principal components analysis was performed to examine the association between variability in DNA methylation and adenoma location. Linear regression and linear mixed effects models were used to identify locus-specific differential DNA methylation in adenomas of right and left colon. A significant association was present between the first principal component and adenoma location (P=0.007), even after adjustment for subject age and gender (P=0.009). A total of 168 CpG sites were differentially methylated between right- and left-colon adenomas and these loci demonstrated enrichment of homeobox genes (P=3.0 × 10(-12)). None of the 168 probes were associated with CIMP genes. Among CpG loci with the largest difference in methylation between right- and left-colon adenomas, probes associated with PRAC (prostate cancer susceptibility candidate) gene showed hypermethylation in right-colon adenomas whereas those associated with CDX2 (caudal type homeobox transcription factor 2) showed hypermethylation in left-colon adenomas. A subgroup of left-colon adenomas enriched for current smokers (OR=6.1, P=0.004) exhibited a methylation profile similar to right-colon adenomas. In summary, our results indicate distinct patterns of DNA methylation, independent of CIMP genes, in adenomas of the right and left colon.
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A Simple and Computationally Efficient Approach to Multifactor Dimensionality Reduction Analysis of Gene-Gene Interactions for Quantitative Traits.
PLoS ONE
PUBLISHED: 01-01-2013
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We present an extension of the two-class multifactor dimensionality reduction (MDR) algorithm that enables detection and characterization of epistatic SNP-SNP interactions in the context of a quantitative trait. The proposed Quantitative MDR (QMDR) method handles continuous data by modifying MDRs constructive induction algorithm to use a T-test. QMDR replaces the balanced accuracy metric with a T-test statistic as the score to determine the best interaction model. We used a simulation to identify the empirical distribution of QMDRs testing score. We then applied QMDR to genetic data from the ongoing prospective Prevention of Renal and Vascular End-Stage Disease (PREVEND) study.
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Next generation analytic tools for large scale genetic epidemiology studies of complex diseases.
Genet. Epidemiol.
PUBLISHED: 12-06-2011
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Over the past several years, genome-wide association studies (GWAS) have succeeded in identifying hundreds of genetic markers associated with common diseases. However, most of these markers confer relatively small increments of risk and explain only a small proportion of familial clustering. To identify obstacles to future progress in genetic epidemiology research and provide recommendations to NIH for overcoming these barriers, the National Cancer Institute sponsored a workshop entitled "Next Generation Analytic Tools for Large-Scale Genetic Epidemiology Studies of Complex Diseases" on September 15-16, 2010. The goal of the workshop was to facilitate discussions on (1) statistical strategies and methods to efficiently identify genetic and environmental factors contributing to the risk of complex disease; and (2) how to develop, apply, and evaluate these strategies for the design, analysis, and interpretation of large-scale complex disease association studies in order to guide NIH in setting the future agenda in this area of research. The workshop was organized as a series of short presentations covering scientific (gene-gene and gene-environment interaction, complex phenotypes, and rare variants and next generation sequencing) and methodological (simulation modeling and computational resources and data management) topic areas. Specific needs to advance the field were identified during each session and are summarized.
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Treating seeds with activators of plant defence generates long-lasting priming of resistance to pests and pathogens.
New Phytol.
PUBLISHED: 12-05-2011
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• Priming of defence is a strategy employed by plants exposed to stress to enhance resistance against future stress episodes with minimal associated costs on growth. Here, we test the hypothesis that application of priming agents to seeds can result in plants with primed defences. • We measured resistance to arthropod herbivores and disease in tomato (Solanum lycopersicum) plants grown from seed treated with jasmonic acid (JA) and/or ?-aminobutryric acid (BABA). • Plants grown from JA-treated seed showed increased resistance against herbivory by spider mites, caterpillars and aphids, and against the necrotrophic fungal pathogen, Botrytis cinerea. BABA seed treatment provided primed defence against powdery mildew disease caused by the biotrophic fungal pathogen, Oidium neolycopersici. Priming responses were long-lasting, with significant increases in resistance sustained in plants grown from treated seed for at least 8 wk, and were associated with enhanced defence gene expression during pathogen attack. There was no significant antagonism between different forms of defence in plants grown from seeds treated with a combination of JA and BABA. • Long-term defence priming by seed treatments was not accompanied by reductions in growth, and may therefore be suitable for commercial exploitation.
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Comparison of head-neck responses in frontal impacts using restrained human surrogates.
Ann Adv Automot Med
PUBLISHED: 11-23-2011
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The objective of the study was to evaluate the head and neck kinetics of three-point belted Hybrid III dummy and Test Device for Human Occupant Restraint (THOR) in frontal impacts, and compare their responses with data from post mortem human subjects (PMHS). Surrogates were placed on a buck, capable of accommodating different anthropometry with similar initial positioning. Duplicate tests were conducted at low, medium, and high (3.6, 6.9, and 15.8 m/s) velocities. Upper and lower neck forces and moments were determined from load cell measures and its locations with respect to the ends of the neck. Head excursion-time responses were more repeatable in the Hybrid III dummy than the THOR dummy. Hybrid III dummy response was more rigid in the sagittal plane. Peak THOR motions were closer to PMHS. Based on times of occurrences of peak excursions, THOR was closer to PMHS at all velocities, while Hybrid III dummy showed biofidelity at the medium and high velocities. Controlled positioning and testing with different surrogates provide an evaluation of inter-subject responses. THOR was more likely to "get the head where and when it needs to be" in frontal impacts. With the importance of testing at lower speeds due to recent recognition of real-world injuries, these data suggest that THOR may be an optimal dummy for frontal impacts. Comparisons of head-neck kinetic data with PMHS are valuable in frontal impact injury assessments.
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Rapid eye movement sleep debt accrues in mice exposed to volatile anesthetics.
Anesthesiology
PUBLISHED: 09-22-2011
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General anesthesia has been likened to a state in which anesthetized subjects are locked out of access to both rapid eye movement (REM) sleep and wakefulness. Were this true for all anesthetics, a significant REM rebound after anesthetic exposure might be expected. However, for the intravenous anesthetic propofol, studies demonstrate that no sleep debt accrues. Moreover, preexisting sleep debts dissipate during propofol anesthesia. To determine whether these effects are specific to propofol or are typical of volatile anesthetics, the authors tested the hypothesis that REM sleep debt would accrue in rodents anesthetized with volatile anesthetics.
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What is Visualize?

JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.

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We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.

Video X seems to be unrelated to Abstract Y...

In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.