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Statistical Methods For High Dimensional Data In Genetic Epidemiology
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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 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 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 A Statistical Approach to Genetic Epidemiology by : Andreas Ziegler
Download or read book A Statistical Approach to Genetic Epidemiology written by Andreas Ziegler and published by John Wiley & Sons. This book was released on 2011-08-24 with total page 522 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Statistical Approach to Genetic Epidemiology After studying statistics and mathematics at the University of Munich and obtaining his doctoral degree from the University of Dortmund, Andreas Ziegler received the Johann-Peter-Süssmilch-Medal of the German Association for Medical Informatics, Biometry and Epidemiology for his post-doctoral work on “Model Free Linkage Analysis of Quantitative Traits” in 1999. In 2004, he was one of the recipients of the Fritz-Linder-Forum-Award from the German Association for Surgery.
Book Synopsis The Fundamentals of Modern Statistical Genetics by : Nan M. Laird
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.
Book Synopsis An Introduction to Genetic Epidemiology by : Palmer, Lyle J.
Download or read book An Introduction to Genetic Epidemiology written by Palmer, Lyle J. and published by Policy Press. This book was released on 2011-05-31 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book brings together leading experts to provide an introduction to genetic epidemiology that begins with a primer in human molecular genetics through all the standard methods in population genetics and genetic epidemiology required for an adequate grounding in the field.
Book Synopsis Statistical Diagnostics for Cancer by : Matthias Dehmer
Download or read book Statistical Diagnostics for Cancer written by Matthias Dehmer and published by John Wiley & Sons. This book was released on 2012-11-28 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt: This ready reference discusses different methods for statistically analyzing and validating data created with high-throughput methods. As opposed to other titles, this book focusses on systems approaches, meaning that no single gene or protein forms the basis of the analysis but rather a more or less complex biological network. From a methodological point of view, the well balanced contributions describe a variety of modern supervised and unsupervised statistical methods applied to various large-scale datasets from genomics and genetics experiments. Furthermore, since the availability of sufficient computer power in recent years has shifted attention from parametric to nonparametric methods, the methods presented here make use of such computer-intensive approaches as Bootstrap, Markov Chain Monte Carlo or general resampling methods. Finally, due to the large amount of information available in public databases, a chapter on Bayesian methods is included, which also provides a systematic means to integrate this information. A welcome guide for mathematicians and the medical and basic research communities.
Book Synopsis Statistical Methods for High-dimensional Sequencing Studies by : Changshuai Wei
Download or read book Statistical Methods for High-dimensional Sequencing Studies written by Changshuai Wei and published by . This book was released on 2014 with total page 113 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Mathematical and Statistical Methods for Genetic Analysis by : Kenneth Lange
Download or read book Mathematical and Statistical Methods for Genetic Analysis written by Kenneth Lange and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written to equip students in the mathematical siences to understand and model the epidemiological and experimental data encountered in genetics research. This second edition expands the original edition by over 100 pages and includes new material. Sprinkled throughout the chapters are many new problems.
Book Synopsis Biostatistical Genetics and Genetic Epidemiology by : Robert C. Elston
Download or read book Biostatistical Genetics and Genetic Epidemiology written by Robert C. Elston and published by John Wiley & Sons. This book was released on 2002-04-22 with total page 860 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Human Genetics and Genetic Epidemiology" ist der 3. Band aus der sehr erfolgreichen Reihe 'Wiley Biostatistics Reference Series', die auf Artikeln der "Encyclopedia of Biostatistics" basiert. Dieser Band gibt einen topaktuellen und umfassenden Überblick über ein Forschungsgebiet, das insbesondere im Zuge des Human-Genomprojekts eine regelrechte Explosion an Forschungsaktivitäten erlebt hat. Enthalten sind komplett aktualisierte Artikel aus der "Encyclopedia of Biostatistics" sowie über 25% neue Artikel. Mit einem komplexen System an Querverweisen, die das Auffinden der gewünschten Information erheblich erleichtern. Eine interessante Lektüre für Genetiker, Epidemiologen, Biostatistiker und Forscher in diesen Bereichen.
Book Synopsis Statistical Methods for High-dimensional Genomics Data Analysis by : Vasyl Pihur
Download or read book Statistical Methods for High-dimensional Genomics Data Analysis written by Vasyl Pihur and published by . This book was released on 2009 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: The advent of high-throughput technologies in genomics and proteomics promoted the development of novel statistical methods for handling and analyzing enormous amounts of data that are being produced on a daily bases in laboratories around the world. In this dissertation work, we propose several novel methodologies for handling important problems that come up when analyzing high-dimensional genomic and proteomic data. In the context of meta-analysis, we develop a rank-based aggregation approach suitable for combining results from microarray experiments conducted on different, not directly compatible, platforms. Borrowing ideas for weighted rank aggregation, we incorporate them into the bootstrap aggregation to create an advanced adaptive ensemble classification method with desirable performance characteristics. Computational reconstruction of genetic association networks is a hot topic in genomics and the proposed in this work PLS-based approach outperforms many existing methods from the literature. Finally, we develop a novel method for estimating the number of null hypotheses in multiple testing problems based on the Benjamini-Hochberg rule and improve the existing two-step adaptive Benjamini-Hochberg procedure.
Book Synopsis Statistical Analysis of Next Generation Sequencing Data by : Somnath Datta
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.
Book Synopsis Statistical Methods in Epidemiology by : Harold A. Kahn
Download or read book Statistical Methods in Epidemiology written by Harold A. Kahn and published by Monographs in Epidemiology and. This book was released on 1989 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is an expanded version of the Kahn's widely used text, An Introduction to Epidemiologic Methods (Oxford, 1983). It provides clear insight into the basic statistical tools used in epidemiology and is written so that those without advanced statistical training can comprehend the ideas underlying the analytical techniques. The authors emphasize the extent to which similar results are obtained from different methods, both simple and complex. To this edition they have added a new chapter on "Comparison of Numerical Results for Various Methods of Adjustment" and also one on "The Primacy of Data Collection." New topics include the Kaplan-Meier product-limit method and the Cox proportional hazards model for analysis of time-related outcomes. An appendix of data from the Framingham Heart Study is used to illustrate the application of various analytical methods to an identical set of real data and provides source material for student exercises. The text has been updated throughout.
Book Synopsis A Statistical Approach to Genetic Epidemiology by : Andreas Ziegler
Download or read book A Statistical Approach to Genetic Epidemiology written by Andreas Ziegler and published by John Wiley & Sons. This book was released on 2010-06-14 with total page 522 pages. Available in PDF, EPUB and Kindle. Book excerpt: Diese zweite Auflage des sehr erfolgreichen Lehrbuchs der Statistik in der genetischen Epidemiologie wurde sorgfältig durchgesehen, aktualisiert und an vielen Stellen erweitert. Wie gewohnt mit vielen Aufgaben und Lösungen, dazu jetzt auch Farbabbildungen und auf Wunsch gekoppelt mit einem maßgeschneiderten E-Learning-Kurs.
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 Novel Approaches to the Analysis of Family Data in Genetic Epidemiology by : Xiangqing Sun
Download or read book Novel Approaches to the Analysis of Family Data in Genetic Epidemiology written by Xiangqing Sun and published by Frontiers Media SA. This book was released on 2016-08-17 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genome-wide association studies (GWAS) for complex disorders with large case-control populations have been performed on hundreds of traits in more than 1200 published studies (http://www.genome.gov/gwastudies/) but the variants detected by GWAS account for little of the heritability of these traits, leading to an increasing interest in using family based designs. While GWAS studies are designed to find common variants with low to moderate attributable risks, family based studies are expected to find rare variants with high attributable risk. Because family-based designs can better control both genetic and environmental background, this study design is robust to heterogeneity and population stratification. Moreover, in family-based analysis, the background genetic variation can be modeled to control the residual variance which could increase the power to identify disease associated rare variants. Analysis of families can also help us gain knowledge about disease transmission and inheritance patterns. Although a family-based design has the advantage of being robust to false positives, novel and powerful methods to analyze families in genetic epidemiology continue to be needed, especially for the interaction between genetic and environmental factors associated with disease. Moreover, with the rapid development of sequencing technology, advances in approaches to the design and analysis of sequencing data in families are also greatly needed. The 11 articles in this book all introduce new methodology and, using family data, substantial new findings are presented in the areas of infectious diseases, diabetes, eye traits, autism spectrum disorder and prostate cancer.
Book Synopsis Statistical Methods for High-dimensional Genomic Data by : Michael Chiao-An Wu
Download or read book Statistical Methods for High-dimensional Genomic Data written by Michael Chiao-An Wu and published by . This book was released on 2009 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: High-throughput genomic studies hold great promise for providing insight into key biological and medical problems, but the high-dimensionality of the data from these studies constitutes a great challenge for researchers. This thesis seeks to address some of the methodological challenges posed by high-dimensional genomic data. First, the need to develop accurate classifiers based on genomic markers motivated the development of sparse linear discriminant analysis (sLDA), a regularized form of linear discriminant analysis, which performs simultaneous classification and variable selection. The second and third chapters of this thesis are concerned with multifeature testing. In the gene expression setting, we apply sLDA to test for differential expression of gene pathways by using the sLDA weights to reduce each pathway to a univariate score which may be evaluated via permutation. Then for genome wide association studies, we consider using the logistic kernel machine based testing framework to evaluate the significance of SNPs grouped on the basis of proximity to known genomic features. Finally, in the last chapter we study the use of sparse regularized regression for making inference in high dimensional data. Specifically, we develop a parametric permutation test based on the LASSO estimator for testing the effect of individual markers in "omics" settings.
