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High Dimensional Statistical Methods For Gene Environment Interactions
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Book Synopsis High Dimensional Statistical Methods for Gene-environment Interactions by : Cen Wu
Download or read book High Dimensional Statistical Methods for Gene-environment Interactions written by Cen Wu and published by . This book was released on 2013 with total page 117 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 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 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 Methods for the Analysis of Genomic Data by : Hui Jiang
Download or read book Statistical Methods for the Analysis of Genomic Data written by Hui Jiang and published by MDPI. This book was released on 2020-12-29 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, technological breakthroughs have greatly enhanced our ability to understand the complex world of molecular biology. Rapid developments in genomic profiling techniques, such as high-throughput sequencing, have brought new opportunities and challenges to the fields of computational biology and bioinformatics. Furthermore, by combining genomic profiling techniques with other experimental techniques, many powerful approaches (e.g., RNA-Seq, Chips-Seq, single-cell assays, and Hi-C) have been developed in order to help explore complex biological systems. As a result of the increasing availability of genomic datasets, in terms of both volume and variety, the analysis of such data has become a critical challenge as well as a topic of great interest. Therefore, statistical methods that address the problems associated with these newly developed techniques are in high demand. This book includes a number of studies that highlight the state-of-the-art statistical methods for the analysis of genomic data and explore future directions for improvement.
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: 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 x E) in diseases and traits (referred to by the contributors as complex phenotypes) including depression, diabetes, obesity, and substance use.
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 Advances in Statistical Methods for the Genetic Dissection of Complex Traits in Plants by : Yuan-Ming Zhang
Download or read book Advances in Statistical Methods for the Genetic Dissection of Complex Traits in Plants written by Yuan-Ming Zhang and published by Frontiers Media SA. This book was released on 2024-01-26 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genome-wide association studies (GWAS) have been widely used in the genetic dissection of complex traits. However, there are still limits in current GWAS statistics. For example, (1) almost all the existing methods do not estimate additive and dominance effects in quantitative trait nucleotide (QTN) detection; (2) the methods for detecting QTN-by-environment interaction (QEI) are not straightforward and do not estimate additive and dominance effects as well as additive-by-environment and dominance-by-environment interaction effects, leading to unreliable results; and (3) no or too simple polygenic background controls have been employed in QTN-by-QTN interaction (QQI) detection. As a result, few studies of QEI and QQI for complex traits have been reported based on multiple-environment experiments. Recently, new statistical tools, including 3VmrMLM, have been developed to address these needs in GWAS. In 3VmrMLM, all the trait-associated effects, including QTN, QEI and QQI related effects, are compressed into a single effect-related vector, while all the polygenic backgrounds are compressed into a single polygenic effect matrix. These compressed parameters can be accurately and efficiently estimated through a unified mixed model analysis. To further validate these new GWAS methods, particularly 3VmrMLM, they should be rigorously tested in real data of various plants and a wide range of other species.
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 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 Statistical Methods to Assess Gene-environment by : Silke Schmidt
Download or read book Statistical Methods to Assess Gene-environment written by Silke Schmidt and published by . This book was released on 1999 with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 Human Genome Epidemiology, 2nd Edition by : Muin J. Khoury
Download or read book Human Genome Epidemiology, 2nd Edition written by Muin J. Khoury and published by Oxford University Press. This book was released on 2010-01-20 with total page 701 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first edition of Human Genome Epidemiology, published in 2004, discussed how the epidemiologic approach provides an important scientific foundation for studying the continuum from gene discovery to the development, applications and evaluation of human genome information in improving health and preventing disease. Since that time, advances in human genomics have continued to occur at a breathtaking pace.With contributions from leaders in the field from around the world, this new edition is a fully updated look at the ways in which genetic factors in common diseases are studied. Methodologic developments in collection, analysis and synthesis of data, as well as issues surrounding specific applications of human genomic information for medicine and public health are all discussed. In addition, the book focuses on practical applications of human genome variation in clinical practice and disease prevention. Students, clinicians, public health professionals and policy makers will find the book a useful tool for understanding the rapidly evolving methods of the discovery and use of genetic information in medicine and public health in the 21st century.
Book Synopsis Human Genome Epidemiology, 2nd Edition by : Muin Khoury
Download or read book Human Genome Epidemiology, 2nd Edition written by Muin Khoury and published by Oxford University Press. This book was released on 2010-01-20 with total page 701 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first edition of Human Genome Epidemiology, published in 2004, discussed how the epidemiologic approach provides an important scientific foundation for studying the continuum from gene discovery to the development, applications and evaluation of human genome information in improving health and preventing disease. Since that time, advances in human genomics have continued to occur at a breathtaking pace. With contributions from leaders in the field from around the world, this new edition is a fully updated look at the ways in which genetic factors in common diseases are studied. Methodologic developments in collection, analysis and synthesis of data, as well as issues surrounding specific applications of human genomic information for medicine and public health are all discussed. In addition, the book focuses on practical applications of human genome variation in clinical practice and disease prevention. Students, clinicians, public health professionals and policy makers will find the book a useful tool for understanding the rapidly evolving methods of the discovery and use of genetic information in medicine and public health in the 21st century.
Download or read book Artificial Intelligence written by and published by BoD – Books on Demand. This book was released on 2019-07-31 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence (AI) is taking on an increasingly important role in our society today. In the early days, machines fulfilled only manual activities. Nowadays, these machines extend their capabilities to cognitive tasks as well. And now AI is poised to make a huge contribution to medical and biological applications. From medical equipment to diagnosing and predicting disease to image and video processing, among others, AI has proven to be an area with great potential. The ability of AI to make informed decisions, learn and perceive the environment, and predict certain behavior, among its many other skills, makes this application of paramount importance in today's world. This book discusses and examines AI applications in medicine and biology as well as challenges and opportunities in this fascinating area.
Book Synopsis Handbook of Statistical Methods for Case-Control Studies by : Ørnulf Borgan
Download or read book Handbook of Statistical Methods for Case-Control Studies written by Ørnulf Borgan and published by CRC Press. This book was released on 2018-06-27 with total page 700 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Statistical Methods for Case-Control Studies is written by leading researchers in the field. It provides an in-depth treatment of up-to-date and currently developing statistical methods for the design and analysis of case-control studies, as well as a review of classical principles and methods. The handbook is designed to serve as a reference text for biostatisticians and quantitatively-oriented epidemiologists who are working on the design and analysis of case-control studies or on related statistical methods research. Though not specifically intended as a textbook, it may also be used as a backup reference text for graduate level courses. Book Sections Classical designs and causal inference, measurement error, power, and small-sample inference Designs that use full-cohort information Time-to-event data Genetic epidemiology About the Editors Ørnulf Borgan is Professor of Statistics, University of Oslo. His book with Andersen, Gill and Keiding on counting processes in survival analysis is a world classic. Norman E. Breslow was, at the time of his death, Professor Emeritus in Biostatistics, University of Washington. For decades, his book with Nick Day has been the authoritative text on case-control methodology. Nilanjan Chatterjee is Bloomberg Distinguished Professor, Johns Hopkins University. He leads a broad research program in statistical methods for modern large scale biomedical studies. Mitchell H. Gail is a Senior Investigator at the National Cancer Institute. His research includes modeling absolute risk of disease, intervention trials, and statistical methods for epidemiology. Alastair Scott was, at the time of his death, Professor Emeritus of Statistics, University of Auckland. He was a major contributor to using survey sampling methods for analyzing case-control data. Chris J. Wild is Professor of Statistics, University of Auckland. His research includes nonlinear regression and methods for fitting models to response-selective data.
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: