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Environment Wide Associations To Disease And Disease Related Phenotypes
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Book Synopsis Environment-wide Associations to Disease and Disease-related Phenotypes by : Chirag Jagdish Patel
Download or read book Environment-wide Associations to Disease and Disease-related Phenotypes written by Chirag Jagdish Patel and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Common diseases arise out of combination of both genetic and environmental influences. Advances in genomic technology have enabled investigators to create hypotheses regarding the contribution of genetic factors at a breathtaking pace. However, the assessment of multiple and specific environmental factors--and their interactions with the genome-- has not. We lack high-throughput analytic methodologies to comprehensively and systematically associate multiple physical and specific environmental factors, or the "envirome", to disease and human health. We claim that the creation of hypotheses regarding the environmental contribution to disease is practicable through high-throughput analytic methods that have been well established in genomics. In the following dissertation, we develop and apply methods to systematically and comprehensively associate specific factors of the envirome with disease states, prioritizing factors for in-depth future study. The current disciplines of studying the environmental determinants of health include toxicology and epidemiology, which operate on molecular and population scales, respectively. This dissertation proposes approaches in both of these disciplines. For example, we have developed a framework to conduct the first "Environment-wide Association Study" (EWAS), systematically associating environmental factors to disease on a population scale. We have applied this framework to investigate type 2 diabetes and heart disease on cohorts that are representative United States population, finding novel and robust associations in diverse and independent cohorts. Given the lack of explained risk resulting from current day genome-wide studies, the time is ripe to usher in a more comprehensive study of the environment, or "enviromics", toward better understanding of multifactorial diseases and their prevention.
Book Synopsis Environment-wide Associations to Disease and Disease-related Phenotypes by : Chirag Jagdish Patel
Download or read book Environment-wide Associations to Disease and Disease-related Phenotypes written by Chirag Jagdish Patel and published by Stanford University. This book was released on 2011 with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt: Common diseases arise out of combination of both genetic and environmental influences. Advances in genomic technology have enabled investigators to create hypotheses regarding the contribution of genetic factors at a breathtaking pace. However, the assessment of multiple and specific environmental factors—and their interactions with the genome-- has not. We lack high-throughput analytic methodologies to comprehensively and systematically associate multiple physical and specific environmental factors, or the "envirome", to disease and human health. We claim that the creation of hypotheses regarding the environmental contribution to disease is practicable through high-throughput analytic methods that have been well established in genomics. In the following dissertation, we develop and apply methods to systematically and comprehensively associate specific factors of the envirome with disease states, prioritizing factors for in-depth future study. The current disciplines of studying the environmental determinants of health include toxicology and epidemiology, which operate on molecular and population scales, respectively. This dissertation proposes approaches in both of these disciplines. For example, we have developed a framework to conduct the first "Environment-wide Association Study" (EWAS), systematically associating environmental factors to disease on a population scale. We have applied this framework to investigate type 2 diabetes and heart disease on cohorts that are representative United States population, finding novel and robust associations in diverse and independent cohorts. Given the lack of explained risk resulting from current day genome-wide studies, the time is ripe to usher in a more comprehensive study of the environment, or "enviromics", toward better understanding of multifactorial diseases and their prevention.
Book Synopsis Computational Analysis of the Genetic and Environmental Contributions to Disease-related Human Phenotypes by : Diana Marek
Download or read book Computational Analysis of the Genetic and Environmental Contributions to Disease-related Human Phenotypes written by Diana Marek and published by . This book was released on 2010 with total page 131 pages. Available in PDF, EPUB and Kindle. Book excerpt: Thèse. Biologie. Médecine. 2010
Book Synopsis Understanding Racial and Ethnic Differences in Health in Late Life by : National Research Council
Download or read book Understanding Racial and Ethnic Differences in Health in Late Life written by National Research Council and published by National Academies Press. This book was released on 2004-09-08 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: As the population of older Americans grows, it is becoming more racially and ethnically diverse. Differences in health by racial and ethnic status could be increasingly consequential for health policy and programs. Such differences are not simply a matter of education or ability to pay for health care. For instance, Asian Americans and Hispanics appear to be in better health, on a number of indicators, than White Americans, despite, on average, lower socioeconomic status. The reasons are complex, including possible roles for such factors as selective migration, risk behaviors, exposure to various stressors, patient attitudes, and geographic variation in health care. This volume, produced by a multidisciplinary panel, considers such possible explanations for racial and ethnic health differentials within an integrated framework. It provides a concise summary of available research and lays out a research agenda to address the many uncertainties in current knowledge. It recommends, for instance, looking at health differentials across the life course and deciphering the links between factors presumably producing differentials and biopsychosocial mechanisms that lead to impaired health.
Book Synopsis Cells and Surveys by : National Research Council
Download or read book Cells and Surveys written by National Research Council and published by National Academies Press. This book was released on 2001-01-19 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: What can social science, and demography in particular, reasonably expect to learn from biological information? There is increasing pressure for multipurpose household surveys to collect biological data along with the more familiar interviewer-respondent information. Given that recent technical developments have made it more feasible to collect biological information in non-clinical settings, those who fund, design, and analyze survey data need to think through the rationale and potential consequences. This is a concern that transcends national boundaries. Cells and Surveys addresses issues such as which biologic/genetic data should be collected in order to be most useful to a range of social scientists and whether amassing biological data has unintended side effects. The book also takes a look at the various ethical and legal concerns that such data collection entails.
Book Synopsis Statistical Approaches to Gene-environment Interactions for Complex Phenotypes by : Michael T. Windle
Download or read book Statistical Approaches to Gene-environment Interactions for Complex Phenotypes written by Michael T. Windle and published by . This book was released on 2016 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: Diverse methodological and statistical approaches for investigating the role of gene-environment interactions in a range of complex diseases and traits.
Book Synopsis Beyond Genome-wide Association Studies (GWAS) by : Molly Hall
Download or read book Beyond Genome-wide Association Studies (GWAS) written by Molly Hall and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Genome-wide association studies (GWAS) have identified numerous loci associated with human phenotypes. This approach, however, does not consider the richly diverse and complex environment with which humans interact throughout the life course, nor does it allow for interrelationships among genetic loci and across traits. Methods that embrace pleiotropy (the effect of one locus on more than one trait), gene-environment (GxE) and gene-gene (GxG) interactions will further unveil the impact of alterations in biological pathways and identify genes that are only involved with disease in the context of the environment. This valuable information can be used to assess personal risk and choose the most appropriate medical interventions based on an individual's genotype and environment. Additionally, a richer picture of the genetic and environmental aspects that impact complex disease will inform environmental regulations to protect vulnerable populations. Three key limitations of GWAS lead to an inability to robustly model trait prediction in a manner that reflects biological complexity: 1) GWAS explore traits in isolation, one phenotype at a time, preventing investigators from uncovering relationships that exist among multiple traits; 2) GWAS do not account for the exposome; rather, they simply explore the effect of genetic loci on an outcome; and 3) GWAS do not allow for interactions between genetic loci, despite the complexity that exists in biology. The aims described in this dissertation address these limitations. Methods employed in each aim have the potential to: uncover genetic interactions, unveil complex biology behind phenotype networks, inform public policy decisions concerning environmental exposures, and ultimately assess individual disease-risk.
Book Synopsis Environmental Epigenetics by : L. Joseph Su
Download or read book Environmental Epigenetics written by L. Joseph Su and published by Springer. This book was released on 2015-05-18 with total page 327 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book examines the toxicological and health implications of environmental epigenetics and provides knowledge through an interdisciplinary approach. Included in this volume are chapters outlining various environmental risk factors such as phthalates and dietary components, life states such as pregnancy and ageing, hormonal and metabolic considerations and specific disease risks such as cancer cardiovascular diseases and other non-communicable diseases. Environmental Epigenetics imparts integrative knowledge of the science of epigenetics and the issues raised in environmental epidemiology. This book is intended to serve both as a reference compendium on environmental epigenetics for scientists in academia, industry and laboratories and as a textbook for graduate level environmental health courses. Environmental Epigenetics imparts integrative knowledge of the science of epigenetics and the issues raised in environmental epidemiology. This book is intended to serve both as a reference compendium on environmental epigenetics for scientists in academia, industry and laboratories and as a textbook for graduate level environmental health courses.
Book Synopsis Genes, Behavior, and the Social Environment by : Institute of Medicine
Download or read book Genes, Behavior, and the Social Environment written by Institute of Medicine and published by National Academies Press. This book was released on 2006-12-07 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the past century, we have made great strides in reducing rates of disease and enhancing people's general health. Public health measures such as sanitation, improved hygiene, and vaccines; reduced hazards in the workplace; new drugs and clinical procedures; and, more recently, a growing understanding of the human genome have each played a role in extending the duration and raising the quality of human life. But research conducted over the past few decades shows us that this progress, much of which was based on investigating one causative factor at a time—often, through a single discipline or by a narrow range of practitioners—can only go so far. Genes, Behavior, and the Social Environment examines a number of well-described gene-environment interactions, reviews the state of the science in researching such interactions, and recommends priorities not only for research itself but also for its workforce, resource, and infrastructural needs.
Book Synopsis Genetic Dissection of Complex Traits by : D.C. Rao
Download or read book Genetic Dissection of Complex Traits written by D.C. Rao and published by Academic Press. This book was released on 2008-04-23 with total page 788 pages. Available in PDF, EPUB and Kindle. Book excerpt: The field of genetics is rapidly evolving and new medical breakthroughs are occuring as a result of advances in knowledge of genetics. This series continually publishes important reviews of the broadest interest to geneticists and their colleagues in affiliated disciplines. Five sections on the latest advances in complex traits Methods for testing with ethical, legal, and social implications Hot topics include discussions on systems biology approach to drug discovery; using comparative genomics for detecting human disease genes; computationally intensive challenges, and more
Book Synopsis AN INFORMATION THEORETIC FRAMEWORK FOR IDENTIFICATION AND MODELING OF GENE-GENE AND GENE-ENVIRONMENT INTERACTIONS by : Pritam Chanda
Download or read book AN INFORMATION THEORETIC FRAMEWORK FOR IDENTIFICATION AND MODELING OF GENE-GENE AND GENE-ENVIRONMENT INTERACTIONS written by Pritam Chanda and published by . This book was released on 2010 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many applications in various fields of scientific research, economics, financial and marketing applications produce high dimensional data sets in which the data attributes are interdependent. Data mining techniques have been employed to make sense of these data sets, to discover useful patterns and models in the data that aid explaining how the system being represented works. To discover key patterns in the data, it is necessary to find relationships between the variables (or attributes) in the data that helps to explain the interdependencies (such as independence, synergy and redundancy) among the attributes that are important for understanding an appropriate probabilistic model representing the data.^In a biological or genetic context, statistical interactions between two or more genes (called gene-gene interactions or GGI) and also involving several non-genetic or environmental factors (called gene-environment interactions or GEI) are manifestations of the underlying complex biological interactions. The risk of developing many common and complex diseases such as cancer, autoimmune disease and cardiovascular disease involves complex interactions between multiple genes and several endogenous and exogenous environmental factors (or covariates). The successful detection of critical gene-gene and gene-environment statistical interactions can provide the scientific basis for many underlying biological interactions, improves the prospects for uncovering potentially undiscovered genes involved in the disease process and helps to develop preventative and curative measures for particular genetic susceptibilities.^More specifically, the identification of interactions from available genotype data is crucial because GEI and GGI analysis (1) can highlight important interactions among genetic variations in different regions of the genome and non-genetic or environmental factors. They can be used to identify and prioritize regions for sequencing studies. (2) Can be employed for directing study design so that the relevant informative environmental variables can be collected, (3) Can provide evidence in support of specific mechanisms of causality. In this dissertation, we develop, extend, validate and apply information theoretic metrics for identification and characterization of interactions among genetic variations in the epidemiological studies as studies have linked the complex epidemiological associations between genetic variations with the risk of developing many diseases.^We investigate interactions between genes (referred to as gene-gene interactions or GGI) and between genes and non-genetic factors or environmental variables (referred to as gene-environment interactions or GEI) and systematically investigate the dependence of our metrics on genetic and study-design factors to identify the GGI/GEI and enable a visual presentation of the results. We also develop several simulation strategies to be used extensively for performance evaluation because the underlying structure and true relationships between genetic and environmental factors in experimental data sets are rarely known with certainty. The high dimensionality of large data sets (e.g. from genome-wide studies) and presence of confounding factors like multiple correlations (or linkage disequilibrium among genes) and genetic heterogeneity results in combinatorial explosion of the number of possible interactions present in the data.^This combinatorial growth makes it computationally difficult, if not impossible, to exhaustively assess the full range of predictor variables for potential interactions associated with the trait or phenotype variables and diseases in epidemiological studies. Therefore, we develop and evaluate a set of algorithms capable of efficiently searching the combinatorial space for mining significant and non-redundant interactions for both discrete and quantitative phenotypes and conduct detailed power, false-discovery rate and sample size analysis for epidemiological studies. In GEI analysis, the presence of high degree of linkage disequilibrium among the genetic variables results in several interactions to contain redundant information regarding the phenotype variable.^Therefore it is essential to prune a set of GEI using a modeling step which we define as the process of identifying a parsimonious set of combinations or variables capable of explaining the disease phenotype/trait variable that will avoid over- and under-fitted models. We develop a novel algorithm that uses information theoretic metrics and their properties to efficiently perform the model synthesis task. Another principal challenge in GEI analyses is to develop metrics for prioritization of genetic variables for sequencing studies that incorporates knowledge from interactions between the genes. The gene-environment associations identified from large scale genotyping studies require large follow-on studies to comprehensively sequence the disease-associated regions to enable discovery of less common genetic variations that may be contributing to disease.^Such comprehensive follow up studies are resource intensive and require large sample sizes so that it is essential to leverage the available information from existing genotyping studies to identify the most promising disease associated regions and the possible environmental factors. Prioritizing genetic regions involved in GGI or GEI for sequencing studies can be difficult because the number of interactions, the order of interactions and their magnitudes can vary considerably making it difficult to make decisions regarding the relative importance of, e.g., a few large magnitude interactions vis-a-vis numerous interactions of moderate magnitude.^In this research, we develop a novel metric for effectively visualizing and ranking the genetic and environmental variables involved in numerous statistical interactions. Finally, often in genetic data sets, the phenotype or trait variable is absent and it is useful to mine statistical interactions among the genetic variables in an unsupervised fashion that can highlight the underlying biological interactions among the genes and proteins present in pathways. To address such analyses, in this dissertation, we study the problem of mining statistically significant correlation patterns and interaction information in genetic data. We develop novel concepts of combinations of variables containing highly significant, moderately significant and non-significant correlation information and present some bounds on correlation information and develop several pruning strategies utilizing these bounds to efficiently prune the combinatorial search space.^Using the bounds and pruning strategies, we develop efficient search algorithms to mine such associations in an efficient and effective manner and also critically examine the performance of our proposed mining algorithms.
Book Synopsis Incorporating Dependent Data Into Precision Medicine Research Beyond Genome-Wide Association Studies by : Kristin Passero
Download or read book Incorporating Dependent Data Into Precision Medicine Research Beyond Genome-Wide Association Studies written by Kristin Passero and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Non-independent data is a common feature of human health data, occurring, for example, when datasets contain family clusters or repeated measurements. The field of genomics has developed best practices to handle dependent data, especially the dependency introduced by relatedness, to avoid fallacious findings in human health studies. However, in order to identify all risk factors that predispose individuals to diseases and disorders, both genetic and environmental factors must be considered. The exposome has a complex structure of dependency; it varies across time and location, meaning that to measure the exposome requires longitudinal assessments across multiple levels of hierarchy (individual, family, community, etc.) all of which can produce correlation between measures in datasets, thus violating the independence assumptions of traditional methods used in exposome research. Furthermore, in order to identify factors that contribute to the future development of disease, it is vital for researchers to obtain longitudinal phenotypic data as well. However, traditional environment-wide association studies (EWAS) and GxE studies do not incorporate longitudinal measures, and those that do focus on a subset of methods such as linear mixed models or generalized estimating equations. Another issue is that many methods exist with the potential to accommodate dependency and that can be leveraged to allow EWAS or GxE to utilize longitudinal or family data, but there is no consensus as to the preferred method. This dissertation addresses these concerns by 1) extending EWAS and GxE to include temporal information and 2) performing a thorough, comparative assessment of various methods that can be leveraged to analyze family and longitudinal data. The results discussed herein have the potential to further the identification of what and when risk factors influence future health outcomes.
Book Synopsis Methods for Assessing Replicability in GWAS and Analyzing Large-scale Electronic Health Record Datasets by : Daniel McGuire
Download or read book Methods for Assessing Replicability in GWAS and Analyzing Large-scale Electronic Health Record Datasets written by Daniel McGuire and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis is a collection of two articles presented in two Chapters. While the topics of the Chapters are not strongly related in theme, they both present novel approaches for tackling large data problems in the field of Biostatistics. In the first Chapter, we develop a model based approach for assessing replicability of associations within a genome-wide association meta-analysis (GWAMA). GWAMA is an effective approach to enlarge sample sizes and empower the discovery of novel associations between genotype and phenotype. Independent replication has been used as a gold-standard for validating genetic associations. However, as current GWAMA often seeks to aggregate all available datasets, it becomes impossible to find a large enough independent dataset to replicate new discoveries. Here we introduce a method, MAMBA (Meta-Analysis Model-based Assessment of replicability), for assessing the ``posterior-probability-of-replicability'' for identified associations by leveraging the strength and consistency of association signals between contributing studies. We demonstrate using simulations that MAMBA is more powerful and robust than existing methods, and produces more accurate genetic effects estimates. We apply MAMBA to a large-scale meta-analysis of addiction phenotypes with 1.2 million individuals. In addition to accurately identifying replicable common variant associations, MAMBA also pinpoints novel replicable rare variant associations from imputation-based GWAMA and hence greatly expands the set of analyzable variants. In the second Chapter, ``Dissecting Genetic Heritability, Environmental Risk Components and Causal Effects of Air Pollution for Complex Human Diseases Using a Health Insurance Database of 50 Million Individuals'', we use a large dataset of electronic health records (EHR) to decompose contributions of genetic heritability, family environment, and community-level environment on 1,083 phenotypes, using the familial relationships and approximate geographic information embedded in the EHR. We also assess causal effects of pollution on those phenotypes using publicly available datasets of environmental exposures summarized at county and metropolitan statistical area level. To motivate this idea, we note that most complex diseases are jointly influenced by both genetics and environment. The advent of large national-level EHR datasets has offered new opportunities for disentangling the role of genes and environment through the deep phenotype information and approximate pedigree structures that EHR datasets provide. In this study, we made innovative use of the approximate geographical locations of patients to jointly model genetics and spatially correlated sources of environmental risk. Environmental risk factors (such as air pollution) are often shared across families living in similar locations but are typically insufficiently considered in traditional family-based variance components models, leading to biased estimates of genetic heritability. In this study, we extracted EHR from 257,620 quad-families (parents with two children) and analyzed 1,083 disease outcome measurements. We used approximate geographical locations embedded in the EHR to estimate community-level environment effects and quantified genetic heritability and environmental risk factors on disease phenotype variation. We found that jointly modeling both genetic and community-level environment effects improve both heritability and environmental variance component estimates. We further augmented the EHR with publicly available environmental data, including levels of particular matter (PM2.5), nitrogen dioxide (NO2), climate, and sociodemographic data. We used wind speed and direction as instrument variables in regression models to assess the causal effects of air pollution on 1,083 diseases. While individual air pollutant levels often stem from common sources such as traffic pollution, we found PM2.5 and NO2 to have unique disease etiologies and affect biologically distinct disease categories. In total, we found PM2.5 or NO2 have statistically significant putative causal effects on 135 diseases, including respiratory, musculoskeletal, digestive, metabolic, and sleep disorders. Many of these associations have been previously cited with plausible biological mechanisms, although some were reported only in small studies from heavily polluted areas. These analyses showcase several novel strategies for jointly modeling genetic and environmental effects on disease risk using large EHR datasets, and will benefit upcoming biobank studies in the era of precision medicine.
Book Synopsis Regression Methods in Biostatistics by : Eric Vittinghoff
Download or read book Regression Methods in Biostatistics written by Eric Vittinghoff and published by Springer Science & Business Media. This book was released on 2012 with total page 526 pages. Available in PDF, EPUB and Kindle. Book excerpt: This fresh edition, substantially revised and augmented, provides a unified, in-depth, readable introduction to the multipredictor regression methods most widely used in biostatistics. The examples used, analyzed using Stata, can be applied to other areas.
Book Synopsis Molecular Epidemiology by : Paul A. Schulte
Download or read book Molecular Epidemiology written by Paul A. Schulte and published by Academic Press. This book was released on 2012-12-02 with total page 609 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book will serve as a primer for both laboratory and field scientists who are shaping the emerging field of molecular epidemiology. Molecular epidemiology utilizes the same paradigm as traditional epidemiology but uses biological markers to identify exposure, disease or susceptibility. Schulte and Perera present the epidemiologic methods pertinent to biological markers. The book is also designed to enumerate the considerations necessary for valid field research and provide a resource on the salient and subtle features of biological indicators.
Book Synopsis Environmental Genomics by : C. Cristofre Martin
Download or read book Environmental Genomics written by C. Cristofre Martin and published by Springer Science & Business Media. This book was released on 2008-01-18 with total page 363 pages. Available in PDF, EPUB and Kindle. Book excerpt: Here is a manual for an environmental scientist who wishes to embrace genomics to answer environmental questions. The volume covers: gene expression profiling, whole genome and chromosome mutation detection, and methods to assay genome diversity and polymorphisms within a particular environment. This book provides a systematic framework for determining environmental impact and ensuring human health and the sustainability of natural populations.
Book Synopsis Biosocial Surveys by : National Research Council
Download or read book Biosocial Surveys written by National Research Council and published by National Academies Press. This book was released on 2008-01-06 with total page 429 pages. Available in PDF, EPUB and Kindle. Book excerpt: Biosocial Surveys analyzes the latest research on the increasing number of multipurpose household surveys that collect biological data along with the more familiar interviewerâ€"respondent information. This book serves as a follow-up to the 2003 volume, Cells and Surveys: Should Biological Measures Be Included in Social Science Research? and asks these questions: What have the social sciences, especially demography, learned from those efforts and the greater interdisciplinary communication that has resulted from them? Which biological or genetic information has proven most useful to researchers? How can better models be developed to help integrate biological and social science information in ways that can broaden scientific understanding? This volume contains a collection of 17 papers by distinguished experts in demography, biology, economics, epidemiology, and survey methodology. It is an invaluable sourcebook for social and behavioral science researchers who are working with biosocial data.
