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Statistical Methods For The Investigation Of Gene Environment Interactions In Genetic Epidemiological Association Studies
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Book Synopsis Statistical Methods for the Investigation of Gene-environment Interactions in Genetic Epidemiological Association Studies by : Rebecca Hein
Download or read book Statistical Methods for the Investigation of Gene-environment Interactions in Genetic Epidemiological Association Studies written by Rebecca Hein and published by . This book was released on 2008 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Statistical Methods for the Investigation of Gene-environment Interactions in Genetic Epidemiological Acssociation Studies by : Rebecca Hein
Download or read book Statistical Methods for the Investigation of Gene-environment Interactions in Genetic Epidemiological Acssociation Studies written by Rebecca Hein and published by . This book was released on 2008 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Statistical Methods in Genetic Epidemiology by : Duncan C. Thomas
Download or read book Statistical Methods in Genetic Epidemiology written by Duncan C. Thomas and published by Oxford University Press. This book was released on 2004-01-29 with total page 458 pages. Available in PDF, EPUB and Kindle. Book excerpt: This well-organized and clearly written text has a unique focus on methods of identifying the joint effects of genes and environment on disease patterns. It follows the natural sequence of research, taking readers through the study designs and statistical analysis techniques for determining whether a trait runs in families, testing hypotheses about whether a familial tendency is due to genetic or environmental factors or both, estimating the parameters of a genetic model, localizing and ultimately isolating the responsible genes, and finally characterizing their effects in the population. Examples from the literature on the genetic epidemiology of breast and colorectal cancer, among other diseases, illustrate this process. Although the book is oriented primarily towards graduate students in epidemiology, biostatistics and human genetics, it will also serve as a comprehensive reference work for researchers. Introductory chapters on molecular biology, Mendelian genetics, epidemiology, statistics, and population genetics will help make the book accessible to those coming from one of these fields without a background in the others. It strikes a good balance between epidemiologic study designs and statistical methods of data analysis.
Book Synopsis Analysis of Genetic Association Studies by : Gang Zheng
Download or read book Analysis of Genetic Association Studies written by Gang Zheng and published by Springer Science & Business Media. This book was released on 2012-01-11 with total page 419 pages. Available in PDF, EPUB and Kindle. Book excerpt: Analysis of Genetic Association Studies is both a graduate level textbook in statistical genetics and genetic epidemiology, and a reference book for the analysis of genetic association studies. Students, researchers, and professionals will find the topics introduced in Analysis of Genetic Association Studies particularly relevant. The book is applicable to the study of statistics, biostatistics, genetics and genetic epidemiology. In addition to providing derivations, the book uses real examples and simulations to illustrate step-by-step applications. Introductory chapters on probability and genetic epidemiology terminology provide the reader with necessary background knowledge. The organization of this work allows for both casual reference and close study.
Book Synopsis Assessing Gene-Environment Interactions in Genome-Wide Association Studies: Statistical Approaches by : Philip C. Cooley
Download or read book Assessing Gene-Environment Interactions in Genome-Wide Association Studies: Statistical Approaches written by Philip C. Cooley and published by RTI Press. This book was released on 2014-05-14 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this report, we address a scenario that uses synthetic genotype case-control data that is influenced by environmental factors in a genome-wide association study (GWAS) context. The precise way the environmental influence contributes to a given phenotype is typically unknown. Therefore, our study evaluates how to approach a GWAS that may have an environmental component. Specifically, we assess different statistical models in the context of a GWAS to make association predictions when the form of the environmental influence is questionable. We used a simulation approach to generate synthetic data corresponding to a variety of possible environmental-genetic models, including a “main effects only” model as well as a “main effects with interactions” model. Our method takes into account the strength of the association between phenotype and both genotype and environmental factors, but we focus on low-risk genetic and environmental risks that necessitate using large sample sizes (N = 10,000 and 200,000) to predict associations with high levels of confidence. We also simulated different Mendelian gene models, and we analyzed how the collection of factors influences statistical power in the context of a GWAS. Using simulated data provides a “truth set” of known outcomes such that the association-affecting factors can be unambiguously determined. We also test different statistical methods to determine their performance properties. Our results suggest that the chances of predicting an association in a GWAS is reduced if an environmental effect is present and the statistical model does not adjust for that effect. This is especially true if the environmental effect and genetic marker do not have an interaction effect. The functional form of the statistical model also matters. The more accurately the form of the environmental influence is portrayed by the statistical model, the more accurate the prediction will be. Finally, even with very large samples sizes, association predictions involving recessive markers with low risk can be poor
Book Synopsis Statistical Approaches to Gene x Environment Interactions for Complex Phenotypes by : Michael Windle
Download or read book Statistical Approaches to Gene x Environment Interactions for Complex Phenotypes written by Michael Windle and published by MIT Press. This book was released on 2016-07-08 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. Findings from the Human Genome Project and from Genome-Wide Association (GWA) studies indicate that many diseases and traits manifest a more complex genomic pattern than previously assumed. These findings, and advances in high-throughput sequencing, suggest that there are many sources of influence—genetic, epigenetic, and environmental. This volume investigates the role of the interactions of genes and environment (G × E) in diseases and traits (referred to by the contributors as complex phenotypes) including depression, diabetes, obesity, and substance use. The contributors first present different statistical approaches or strategies to address G × E and G × G interactions with high-throughput sequenced data, including two-stage procedures to identify G × E and G × G interactions, marker-set approaches to assessing interactions at the gene level, and the use of a partial-least square (PLS) approach. The contributors then turn to specific complex phenotypes, research designs, or combined methods that may advance the study of G × E interactions, considering such topics as randomized clinical trials in obesity research, longitudinal research designs and statistical models, and the development of polygenic scores to investigate G × E interactions. Contributors Fatima Umber Ahmed, Yin-Hsiu Chen, James Y. Dai, Caroline Y. Doyle, Zihuai He, Li Hsu, Shuo Jiao, Erin Loraine Kinnally, Yi-An Ko, Charles Kooperberg, Seunggeun Lee, Arnab Maity, Jeanne M. McCaffery, Bhramar Mukherjee, Sung Kyun Park, Duncan C. Thomas, Alexandre Todorov, Jung-Ying Tzeng, Tao Wang, Michael Windle, Min Zhang
Book Synopsis Applied Statistical Genetics with R by : Andrea S. Foulkes
Download or read book Applied Statistical Genetics with R written by Andrea S. Foulkes and published by Springer Science & Business Media. This book was released on 2009-04-28 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical genetics has become a core course in many graduate programs in public health and medicine. This book presents fundamental concepts and principles in this emerging field at a level that is accessible to students and researchers with a first course in biostatistics. Extensive examples are provided using publicly available data and the open source, statistical computing environment, R.
Book Synopsis Assessing Gene-environment Interactions in Genome-wide Association Studies by : Philip Chester Cooley
Download or read book Assessing Gene-environment Interactions in Genome-wide Association Studies written by Philip Chester Cooley and published by . This book was released on 2014 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this report, we address a scenario that uses synthetic genotype case-control data that is influenced by environmental factors in a genome-wide association study (GWAS) context. The precise way the environmental influence contributes to a given phenotype is typically unknown. Therefore, our study evaluates how to approach a GWAS that may have an environmental component. Specifically, we assess different statistical models in the context of a GWAS to make association predictions when the form of the environmental influence is questionable. We used a simulation approach to generate synthetic data corresponding to a variety of possible environmental-genetic models, including a "main effects only" model as well as a "main effects with interactions" model. Our method takes into account the strength of the association between phenotype and both genotype and environmental factors, but we focus on low-risk genetic and environmental risks that necessitate using large sample sizes (N = 10,000 and 200,000) to predict associations with high levels of confidence. We also simulated different Mendelian gene models, and we analyzed how the collection of factors influences statistical power in the context of a GWAS. Using simulated data provides a "truth set" of known outcomes such that the association-affecting factors can be unambiguously determined. We also test different statistical methods to determine their performance properties. Our results suggest that the chances of predicting an association in a GWAS is reduced if an environmental effect is present and the statistical model does not adjust for that effect. This is especially true if the environmental effect and genetic marker do not have an interaction effect. The functional form of the statistical model also matters. The more accurately the form of the environmental influence is portrayed by the statistical model, the more accurate the prediction will be. Finally, even with very large samples sizes, association predictions involving recessive markers with low risk can be poor.
Book Synopsis Gene-Environment Interaction Analysis by : Sumiko Anno
Download or read book Gene-Environment Interaction Analysis written by Sumiko Anno and published by CRC Press. This book was released on 2016-03-30 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gene-environment (GE) interaction analysis is a statistical method for clarifying GE interactions applicable to a phenotype or a disease that is the result of interactions between genes and the environment. This book is the first to deal with the theme of GE interaction analysis. It compiles and details cutting-edge research in bioinformatics
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 Statistical Methods for High-Dimensional Data in Genetic Epidemiology by : Xinyi Lin
Download or read book Statistical Methods for High-Dimensional Data in Genetic Epidemiology written by Xinyi Lin and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent technological advancements have enabled us to collect an unprecedented amount of genetic epidemiological data. The overarching goal of these genetic epidemiology studies is to uncover the underlying biological mechanisms so that improved strategies for disease prevention and management can be developed. To efficiently analyze and interpret high-dimensional biological data, it is imperative to develop novel statistical methods as conventional statistical methods are generally not applicable or are inefficient. In this dissertation, we introduce three novel, powerful and computationally efficient kernel machine set-based association tests for analyzing high-throughput genetic epidemiological data. In the first chapter, we construct a test for identifying common genetic variants that are predictive of a time-to-event outcome. In the second chapter, we develop a test for identifying gene-environment interactions for common genetic variants. In the third chapter, we propose a test for identifying gene-environment interactions for rare genetic variants.
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 Gene-environment Interactions in Genetic Epidemiology by : Christine Marie Spinka
Download or read book Gene-environment Interactions in Genetic Epidemiology written by Christine Marie Spinka and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Gene-environment interactions are an area of increasing interest in complex human diseases. The first step in any study of the interactions between genes and the environment involves identifying genes which influence the trait of interest. In this dissertation, a new method for using the information in complex pedigrees to perform a joint linkage disequilibrium and linkage mapping of quantitative trait loci is developed. Subsequently, methods are needed to determine the interaction, if any, between these genes and environmental risk factors. Many of these factors, such as weight or age, are continuous and little is known about their distributions. Thus, we introduce a new method for estimating the gene-environment interaction parameters in a logistic regression for the case-control study design. In doing so, we make the assumption that in the underlying population, the distributions of the genetic factors and the environmental covariates are independent. Additionally, we treat the environmental parameters nonparametricly, utilizing the profile likelihood. Furthermore, the methodology we develop is also general enough to be used on many different types of genetic information, including haplotypes, and can accommodate missing genotype data. The method is also extended to allow analysis in the presence of population stratification or genotype misclassification. We show that the standard errors of parameter estimates using our method are smaller than those found using complete data only. These methods are illustrated using simulations and are applied to a real data set exploring the interaction between genotype and environment in disease risk.
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 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 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.
Book Synopsis Fundamentals of Genetic Epidemiology by : Muin J. Khoury
Download or read book Fundamentals of Genetic Epidemiology written by Muin J. Khoury and published by Monographs in Epidemiology and. This book was released on 1993 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text integrates the principles, methods and approaches of epidemiology and genetics in the study of disease aetiology. The authors define the central theme of genetic epidemiology as the study of the role of genetic factors and their interaction with environmental factors in the occurrence of disease in populations.
