Download or read book Statistical Methods for Detecting Genetic Risk Factors of a Disease with Applications to Genome wide Association Studies written by Fadhaa Ali and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Statistical Methods Computing and Resources for Genome Wide Association Studies written by Riyan Cheng and published by Frontiers Media SA. This book was released on 2021-08-24 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Analysis of Complex Disease Association Studies written by Eleftheria Zeggini and published by Academic Press. This book was released on 2010-11-17 with total page 353 pages. Available in PDF, EPUB and Kindle. Book excerpt: According to the National Institute of Health, a genome-wide association study is defined as any study of genetic variation across the entire human genome that is designed to identify genetic associations with observable traits (such as blood pressure or weight), or the presence or absence of a disease or condition. Whole genome information, when combined with clinical and other phenotype data, offers the potential for increased understanding of basic biological processes affecting human health, improvement in the prediction of disease and patient care, and ultimately the realization of the promise of personalized medicine. In addition, rapid advances in understanding the patterns of human genetic variation and maturing high-throughput, cost-effective methods for genotyping are providing powerful research tools for identifying genetic variants that contribute to health and disease. This burgeoning science merges the principles of statistics and genetics studies to make sense of the vast amounts of information available with the mapping of genomes. In order to make the most of the information available, statistical tools must be tailored and translated for the analytical issues which are original to large-scale association studies. Analysis of Complex Disease Association Studies will provide researchers with advanced biological knowledge who are entering the field of genome-wide association studies with the groundwork to apply statistical analysis tools appropriately and effectively. With the use of consistent examples throughout the work, chapters will provide readers with best practice for getting started (design), analyzing, and interpreting data according to their research interests. Frequently used tests will be highlighted and a critical analysis of the advantages and disadvantage complimented by case studies for each will provide readers with the information they need to make the right choice for their research. Additional tools including links to analysis tools, tutorials, and references will be available electronically to ensure the latest information is available. Easy access to key information including advantages and disadvantage of tests for particular applications, identification of databases, languages and their capabilities, data management risks, frequently used tests Extensive list of references including links to tutorial websites Case studies and Tips and Tricks

Download or read book Methods in Statistical Genomics written by Philip Chester Cooley and published by RTI Press. This book was released on 2016-08-29 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: The objective of this book is to describe procedures for analyzing genome-wide association studies (GWAS). Some of the material is unpublished and contains commentary and unpublished research; other chapters (Chapters 4 through 7) have been published in other journals. Each previously published chapter investigates a different genomics model, but all focus on identifying the strengths and limitations of various statistical procedures that have been applied to different GWAS scenarios.

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

Download or read book Design Analysis and Interpretation of Genome Wide Association Scans written by Daniel O. Stram and published by . This book was released on 2013-12-31 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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.

Download or read book Statistical Methods for Genetic Variants Detection with Epigenomic Information written by Maria Constanza Rojo and published by . This book was released on 2019 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genome-wide association studies (GWAS) have successfully identified thousands of genetic variants contributing to disease and other phenotypes. However, significant obstacles hamper our ability to elucidate causal variants, identify genes affected by causal variants, and characterize the mechanisms by which genotypes influence phenotypes. The increasing availability of genome-wide functional annotation data provides unique opportunities to incorporate prior information into the analysis of GWAS to better understand the impact of variants on disease etiology. Regulatory genomic information has been recognized as a potential source that can improve the detection and biological interpretation of single-nucleotide polymorphisms (SNPs) in GWAS. Although there have been many advances in incorporating prior information into the prioritization of trait-associated variants in GWAS, functional annotation data has played a secondary role in the joint analysis of GWAS and molecular (i.e., expression) quantitative trait loci (eQTL) data in assessing evidence of association. Moreover, current methodologies that aim to integrate such annotation information focus mainly on fine-mapping and overlook the importance of its usage in earlier stages of GWAS analysis. Equally important, there is a lack of development in proper statistical frameworks that can perform selection of annotations and SNPs jointly. To address these shortcomings, we develop two statistical models: iFunMed and GRAD. iFunMed is a novel mediation framework to integrate GWAS and eQTL data with the utilization of publicly available functional annotation data. iFunMed extends the scope of standard mediation analysis by incorporating information from multiple genetic variants at a time and leveraging variant-level summary statistics. GRAD integrates high-dimensional auxiliary information into high-dimensional regression. This method allows annotation information to assist the detection of important genetic variants while identifying relevant annotation simultaneously. We provide an upper bound for the estimation error of the SNP effect sizes to gain insights on what factors affect estimation accuracy. For iFunMed, data-driven computational experiments convey how informative annotations improve SNP selection performance while emphasizing the robustness of the model to non-informative annotations. Applications to the Framingham Heart Study data indicate that iFunMed is able to boost the detection of SNPs with mediation effects that can be attributed to regulatory mechanisms. Simulation experiments indicate that GRAD can improve the identification of genetic variants by increasing the average area under the precision-recall curve by up to 60\%. Real data applications to the Framingham Heart Study show that GRAD can select relevant genetic variants while detecting several transcription factors involved in specific phenotypical changes.

Download or read book Phenotypes and Genotypes written by Florian Frommlet and published by Springer. This book was released on 2016-02-12 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: This timely text presents a comprehensive guide to genetic association, a new and rapidly expanding field that aims to elucidate how our genetic code (genotypes) influences the traits we possess (phenotypes). The book provides a detailed review of methods of gene mapping used in association with experimental crosses, as well as genome-wide association studies. Emphasis is placed on model selection procedures for analyzing data from large-scale genome scans based on specifically designed modifications of the Bayesian information criterion. Features: presents a thorough introduction to the theoretical background to studies of genetic association (both genetic and statistical); reviews the latest advances in the field; illustrates the properties of methods for mapping quantitative trait loci using computer simulations and the analysis of real data; discusses open challenges; includes an extensive statistical appendix as a reference for those who are not totally familiar with the fundamentals of statistics.

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.

Download or read book The Fundamentals of Modern Statistical Genetics written by Nan M. Laird and published by Springer Science & Business Media. This book was released on 2010-12-13 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the statistical models and methods that are used to understand human genetics, following the historical and recent developments of human genetics. Starting with Mendel’s first experiments to genome-wide association studies, the book describes how genetic information can be incorporated into statistical models to discover disease genes. All commonly used approaches in statistical genetics (e.g. aggregation analysis, segregation, linkage analysis, etc), are used, but the focus of the book is modern approaches to association analysis. Numerous examples illustrate key points throughout the text, both of Mendelian and complex genetic disorders. The intended audience is statisticians, biostatisticians, epidemiologists and quantitatively- oriented geneticists and health scientists wanting to learn about statistical methods for genetic analysis, whether to better analyze genetic data, or to pursue research in methodology. A background in intermediate level statistical methods is required. The authors include few mathematical derivations, and the exercises provide problems for students with a broad range of skill levels. No background in genetics is assumed.

Download or read book Handbook of Statistical Genomics written by David J. Balding and published by John Wiley & Sons. This book was released on 2019-07-02 with total page 1224 pages. Available in PDF, EPUB and Kindle. Book excerpt: A timely update of a highly popular handbook on statistical genomics This new, two-volume edition of a classic text provides a thorough introduction to statistical genomics, a vital resource for advanced graduate students, early-career researchers and new entrants to the field. It introduces new and updated information on developments that have occurred since the 3rd edition. Widely regarded as the reference work in the field, it features new chapters focusing on statistical aspects of data generated by new sequencing technologies, including sequence-based functional assays. It expands on previous coverage of the many processes between genotype and phenotype, including gene expression and epigenetics, as well as metabolomics. It also examines population genetics and evolutionary models and inference, with new chapters on the multi-species coalescent, admixture and ancient DNA, as well as genetic association studies including causal analyses and variant interpretation. The Handbook of Statistical Genomics focuses on explaining the main ideas, analysis methods and algorithms, citing key recent and historic literature for further details and references. It also includes a glossary of terms, acronyms and abbreviations, and features extensive cross-referencing between chapters, tying the different areas together. With heavy use of up-to-date examples and references to web-based resources, this continues to be a must-have reference in a vital area of research. Provides much-needed, timely coverage of new developments in this expanding area of study Numerous, brand new chapters, for example covering bacterial genomics, microbiome and metagenomics Detailed coverage of application areas, with chapters on plant breeding, conservation and forensic genetics Extensive coverage of human genetic epidemiology, including ethical aspects Edited by one of the leading experts in the field along with rising stars as his co-editors Chapter authors are world-renowned experts in the field, and newly emerging leaders. The Handbook of Statistical Genomics is an excellent introductory text for advanced graduate students and early-career researchers involved in statistical genetics.

Download or read book Genetic Epidemiology written by Melissa A. Austin and published by CABI. This book was released on 2013 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genetic epidemiology plays a key role in discovering genetic factors influencing health and disease, and in understanding how genes and environmental risk factors interact. There is growing interest in this field within public health, with the goal of translating the results into promoting health and preventing disease in both families and populations. This textbook provides graduate students with a working knowledge of genetic epidemiology research methods. Following an overview of the field, the book reviews key genetic concepts, provides an update on relevant genomic technology, including genome-wide chips and DNA sequencing, and describes methods for assessing the magnitude of genetic influences on diseases and risk factors. The book focuses on research study designs for discovering disease susceptibility genes, including family-based linkage analysis, candidate gene and genome-side association studies, assessing gene-environment interactions and epistasis, studies of Non-Mendelian inheritance, and statistical analyses of data from these studies. Specific applications of each research method are illustrated using a variety of diseases and risk factors relevant to public health, and useful web-based genetic analysis software, human reference panels, and repositories, that can greatly facilitate this work, are described.

Download or read book Heterogeneity in Statistical Genetics written by Derek Gordon and published by Springer Nature. This book was released on 2020-12-16 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt: Heterogeneity, or mixtures, are ubiquitous in genetics. Even for data as simple as mono-genic diseases, populations are a mixture of affected and unaffected individuals. Still, most statistical genetic association analyses, designed to map genes for diseases and other genetic traits, ignore this phenomenon. In this book, we document methods that incorporate heterogeneity into the design and analysis of genetic and genomic association data. Among the key qualities of our developed statistics is that they include mixture parameters as part of the statistic, a unique component for tests of association. A critical feature of this work is the inclusion of at least one heterogeneity parameter when performing statistical power and sample size calculations for tests of genetic association. We anticipate that this book will be useful to researchers who want to estimate heterogeneity in their data, develop or apply genetic association statistics where heterogeneity exists, and accurately evaluate statistical power and sample size for genetic association through the application of robust experimental design.

Download or read book Between the Lines of Genetic Code written by Leonid Padyukov and published by Academic Press. This book was released on 2013-09-28 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: Between the Lines of Genetic Code lays out methodologies and tools for the measurement and evaluation of gene-gene and gene-environment studies and gives perspective on the future of this discipline. The book begins by defining terms for interaction studies, describing methodologies, and critically assessing the viability of current study designs and the possibilities for integrating designs. It then provides recent applications data with case studies in rheumatoid arthritis, multiple sclerosis, myositis and other complex human diseases. Last, it examines current studies and directions for future applications in patient care. Recent multivariate studies show that gene-gene and gene-environment interactions can explain significant variances in inheritance that have previously been undetectable in univariate analysis. These links among genes and between genes and their environments during the development of diseases may serve as important hints for understanding pathogenic mechanisms and for developing new tools for prognosis, diagnosis, and treatment of various diseases. Systematically integrates methods of defining and detecting gene interactions to provide an overview of the field Critically analyzes current methods and tools to aid researchers in integrating gene interaction studies Includes examples of current biomedical applications and presents current research expected to shape clinical research in the near future

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.

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.