Download or read book Statistical Methods for Genome wide Association Studies and Personalized Medicine written by and published by . This book was released on 2014 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: In genome-wide association studies (GWAS), researchers analyze the genetic variation across the entire human genome, searching for variations that are associated with observable traits or certain diseases. There are several inference challenges, including the huge number of genetic markers to test, the weak association between truly associated markers and the traits, and the correlation structure between the genetic markers. We discuss the problem of high dimensional statistical inference, especially capturing the dependence among multiple hypotheses. Chapter 3 proposes a feature selection approach based on a unique graphical model which can leverage correlation structure among the markers. This graphical model-based feature selection approach significantly outperforms the conventional feature selection methods used in GWAS. Chapter 4 reformulates this feature selection approach as a multiple testing procedure that has many elegant properties, including controlling false discovery rate at a specified level and significantly improving the power of the tests. In order to relax the parametric assumption within the model, Chapter 5 further proposes a semiparametric graphical model which estimates f1 adaptively. These statistical methods are based on graphical models, and their parameter learning is difficult due to the intractable normalization constant. Capturing the hidden patterns and heterogeneity within the parameters is even harder. Chapters 6 and 7 discuss the problem of learning large-scale graphical models, especially dealing with issues of heterogeneous parameters and latently-grouped parameters. Chapter 6 proposes a nonparametric approach which can adaptively integrate background knowledge about how the different parts of the graph can vary. For learning latently-grouped parameters in undirected graphical models, Chapter 7 imposes Dirichlet process priors over the parameters and estimates the parameters in a Bayesian framework. Chapter 8 explores the potential translation of GWAS discoveries to clinical breast cancer diagnosis. We discovered that, using SNPs known to be associated with breast cancer, we can better stratify patients and thereby significantly reduce false positives during breast cancer diagnosis, alleviating the risk of overdiagnosis. This result suggests that when radiologists are making medical decisions from mammograms (such as suggesting follow-up biopsies), they can consider these risky SNPs for more accurate decisions if the patients' genotype data are available.

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 Genome Wide Association Studies written by Krishnarao Appasani and published by Cambridge University Press. This book was released on 2016-01-14 with total page 449 pages. Available in PDF, EPUB and Kindle. Book excerpt: Experts from academia and industry highlight the potential of genome-wide association studies from basic science to clinical and biotechnological/pharmaceutical applications.

Download or read book Design Analysis and Interpretation of Genome Wide Association Scans written by Daniel O. Stram and published by Springer Science & Business Media. This book was released on 2013-11-23 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the statistical aspects of designing, analyzing and interpreting the results of genome-wide association scans (GWAS studies) for genetic causes of disease using unrelated subjects. Particular detail is given to the practical aspects of employing the bioinformatics and data handling methods necessary to prepare data for statistical analysis. The goal in writing this book is to give statisticians, epidemiologists, and students in these fields the tools to design a powerful genome-wide study based on current technology. The other part of this is showing readers how to conduct analysis of the created study. Design and Analysis of Genome-Wide Association Studies provides a compendium of well-established statistical methods based upon single SNP associations. It also provides an introduction to more advanced statistical methods and issues. Knowing that technology, for instance large scale SNP arrays, is quickly changing, this text has significant lessons for future use with sequencing data. Emphasis on statistical concepts that apply to the problem of finding disease associations irrespective of the technology ensures its future applications. The author includes current bioinformatics tools while outlining the tools that will be required for use with extensive databases from future large scale sequencing projects. The author includes current bioinformatics tools while outlining additional issues and needs arising from the extensive databases from future large scale sequencing projects.

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 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 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 Genome Wide Association Studies written by Tatsuhiko Tsunoda and published by Springer Nature. This book was released on 2019-10-31 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book examines the utility of genome-wide association studies (GWAS) in the era of next-generation sequencing and big data, identifies limitations and potential means of overcoming them, and looks to the future of GWAS and what may lay beyond. GWAS are among the most powerful tools for elucidating the genetic aspects of human and disease diversity. In Genome-Wide Association Studies, experts in the field explore in depth the impacts of GWAS on genomic research into a variety of common diseases, including cardiovascular, autoimmune, diabetic, cancer, and infectious diseases. The book will equip readers with a sound understanding both of the types of disease and phenotypes that are suited for GWAS and of the ways in which a road map resulting from GWAS can lead to the realization of personalized/precision medicine: functional analysis, drug seeds, pathway analysis, disease mechanism, risk prediction, and diagnosis.

Download or read book Statistical Methods for Biomarker Informed Personalized Medicine written by Xinyuan Dong and published by . This book was released on 2021 with total page 101 pages. Available in PDF, EPUB and Kindle. Book excerpt: Identifying biomarkers is key to the success of personalized medicine. The recent progress in high throughput genotyping and genomics technologies has enabled rapid discovery of biomarkers that could potentially be used for informing clinical decision making. Towards this direction, we developed summary statistics-based mixed effects score test statistics (sMiST) for testing the association of multiple genetically predicted mediators simultaneously and direct association of individual variants independent of mediators by using a random effects model. Extensive simulation and real data analyses demonstrate that sMiST recovers the results of MiST that is based on individual level data, but is computationally much faster. We applied our approach to a genome-wide association study of colorectal cancer and gene expression and identified several novel and secondary genetic loci.Medical decision making is often complex in that a treatment that improves clinical efficacy may also incur more medical costs, compared to standard care. In cost effectiveness analysis, incremental cost effectiveness ratio (ICER) is an important metric that measures the trade-off between the costs and health benefits of two medical programs. Individualized treatment regimes (ITRs) take into account patient heterogeneity and thus different ITRs may exhibit different health benefits and costs. Identifying a promising ITR that takes into account both efficacy and cost would be of great interest in practice. We construct ITRs that optimize the ICER, where we adopt Dinkelbach’s algorithm to translate a fractional program into an equivalent parametric program that is easy to handle. We conduct extensive simulation studies to show satisfactory performances of our methods, compared to ITRs that only optimizes one single outcome (benefits or costs). Lastly, we apply our method to Multicenter Automatic Defibrillator Implantation Trial with Cardiac Resynchronization Therapy (MADIT-CRT) study, a randomized trial. Dynamic surveillance rules (DSRs) are sequential surveillance decision rules informing the monitoring schedules in clinical practice, which can adapt overtime according to a patient’s evolving characteristics. In many clinical applications, it is desirable to identify and implement optimal stabilized DSRs, where the parameters indexing the decision rules are shared across different decision points and estimated simultaneously. We propose a new criterion for DSRs that account for benefit-cost tradeoff. We develop two methods to estimate the stabilized DSRs optimizing the proposed criterion, which are easy to implement using slightly modified standard statistical software. We establish the asymptotic properties of the estimated coefficient parameters of biomarkers indexing the decision rules. Extensive simulation studies are conducted to demonstrate the superior performance of the proposed methods. The methods are further applied to the Canary Prostate Active Surveillance Study(PASS) study.

Download or read book Quantitative Methods for Precision Medicine written by Rongling Wu and published by CRC Press. This book was released on 2022-12-26 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern medicine is undergoing a paradigm shift from a "one-size-fits-all" strategy to a more precise patient-customized therapy and medication plan. While the success of precision medicine relies on the level of pharmacogenomic knowledge, dissecting the genetic mechanisms of drug response in a sufficient detail requires powerful computational tools. Quantitative Methods for Precision Medicine: Pharmacogenomics in Action presents the advanced statistical methods for mapping pharmacogenetic control by integrating pharmacokinetic and pharmacodynamic principles of drug-body interactions. Beyond traditional reductionist-based statistical genetic approaches, statistical formulization in this book synthesizes elements of multiple disciplines to infer, visualize, and track how pharmacogenes interact together as an intricate but well-coordinated system to mediate patient-specific drug response. Features: Functional and systems mapping models to characterize the genetic architecture of multiple medication processes Statistical methods for analyzing informative missing data in pharmacogenetic association studies Functional graph theory of inferring genetic interaction networks from association data Leveraging the concept of epistasis to capture its bidirectional, signed and weighted properties Modeling gene-induced cell-cell crosstalk and its impact on drug response A graph model of drug-drug interactions in combination therapies Critical methodological issues to improve pharmacogenomic research as the cornerstone of precision medicine This book is suitable for graduate students and researchers in the fields of biology, medicine, bioinformatics and drug design and delivery who are interested in statistical and computational modelling of biological processes and systems. It may also serve as a major reference for applied mathematicians, computer scientists, and statisticians who attempt to develop algorithmic tools for genetic mapping, systems pharmacogenomics and systems biology. It can be used as both a textbook and research reference. Professionals in pharmaceutical sectors who design drugs and clinical doctors who deliver drugs will also find it useful.

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 for Transcriptome wide Association Studies in Ancestrally Diverse Populations written by Anna V. Mikhaylova and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Transcriptome-wide association studies (TWAS) have become more commonly used in recent years. TWAS integrate genome-wide association studies (GWAS) with gene expression mapping studies in order to identify genes whose gene expression is associated with the phenotype. The main goals of TWAS are in providing insights into biological mechanisms underlying disease etiology and in helping interpret the results of GWAS. TWAS conducted in large-scale ancestrally diverse cohorts face multiple challenges, including the presence of population structure, known or cryptic relatedness and heterogeneity in phenotypic distributions across subgroups. There is a dearth of statistical methodology available to researchers that addresses the aforementioned issues. In this dissertation, we evaluate the performance of existing TWAS methods in ancestrally diverse populations and identify their limitations. We then develop new statistical methodology that addresses these limitations. We validate the performance of the novel methods in extensive series of simulations as well as in applications to large cohorts of ancestrally diverse populations from the Trans-Omics for Precision Medicine (TOPMed) program.

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 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 Analysis of Next Generation Sequencing Data written by Somnath Datta and published by Springer. This book was released on 2014-07-03 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt: Next Generation Sequencing (NGS) is the latest high throughput technology to revolutionize genomic research. NGS generates massive genomic datasets that play a key role in the big data phenomenon that surrounds us today. To extract signals from high-dimensional NGS data and make valid statistical inferences and predictions, novel data analytic and statistical techniques are needed. This book contains 20 chapters written by prominent statisticians working with NGS data. The topics range from basic preprocessing and analysis with NGS data to more complex genomic applications such as copy number variation and isoform expression detection. Research statisticians who want to learn about this growing and exciting area will find this book useful. In addition, many chapters from this book could be included in graduate-level classes in statistical bioinformatics for training future biostatisticians who will be expected to deal with genomic data in basic biomedical research, genomic clinical trials and personalized medicine. About the editors: Somnath Datta is Professor and Vice Chair of Bioinformatics and Biostatistics at the University of Louisville. He is Fellow of the American Statistical Association, Fellow of the Institute of Mathematical Statistics and Elected Member of the International Statistical Institute. He has contributed to numerous research areas in Statistics, Biostatistics and Bioinformatics. Dan Nettleton is Professor and Laurence H. Baker Endowed Chair of Biological Statistics in the Department of Statistics at Iowa State University. He is Fellow of the American Statistical Association and has published research on a variety of topics in statistics, biology and bioinformatics.

Download or read book Statistical Methods for Genome Wide Association Studies on Biobank Data written by Christopher Austin German and published by . This book was released on 2021 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genome-Wide Association Studies (GWAS) encompass an important area of statistical genetics. They seek to identify single-nucleotide polymorphisms (SNPs) that are associated with a trait of interest. It is becoming more common for large-scale resources of patient data such as biobanks to become available to researchers that include both genetic data and phenotype data from electronic health records (EHR). New techniques for GWAS are necessary to handle both the large sample sizes and the types of complex data generated from these resources. The first chapter aims to tackle both of these issues by establishing an efficient method of conducting a genome-wide scan of SNPs associated with ordinal traits, which commonly occur from phenotyping algorithms for complex diseases. Chapter two focuses on estimating the effects of covariates on intra-individual variances in a framework that can scale to big longitudinal data. Within-subject variances of traits such as blood pressure have been found to be risk factors, independent of mean levels, for a variety of conditions such as cardiovascular disease. We develop a weighted method of moments (MoM) framework for fitting a mixed effects location-scale model that is robust to distributional assumptions and is computationally tractable for biobank-sized data sets. The third chapter uses the framework from the second chapter to develop and conduct large-scale GWAS, identifying variants associated with intra-individual variability of longitudinal traits. In all of these projects, a main focus is ensuring that the methods can scale to the large sample sizes common in biobank data sets.

Download or read book Handbook of Statistical Genomics written by David J. Balding and published by John Wiley & Sons. This book was released on 2019-09-10 with total page 1223 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.