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Author : Julian Erik Hecker
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (118 download)
Download or read book Robust and Fast Statistical Methods for Genome-wide Association Studies written by Julian Erik Hecker and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Riyan Cheng
Publisher : Frontiers Media SA
ISBN 13 : 2889712125
Total Pages : 148 pages
Book Rating : 4.8/5 (897 download)
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:
Author : Daniel O. Stram
Publisher : Springer Science & Business Media
ISBN 13 : 1461494435
Total Pages : 344 pages
Book Rating : 4.4/5 (614 download)
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.
Author : Christopher Austin German
Publisher :
ISBN 13 :
Total Pages : 162 pages
Book Rating : 4.:/5 (128 download)
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.
Author : Philip Chester Cooley
Publisher : RTI Press
ISBN 13 : 1934831166
Total Pages : 163 pages
Book Rating : 4.9/5 (348 download)
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.
Author :
Publisher :
ISBN 13 :
Total Pages : 646 pages
Book Rating : 4.:/5 (918 download)
Download or read book Gene and Gene-set Analysis for Genome-wide Association Studies written by and published by . This book was released on 2011 with total page 646 pages. Available in PDF, EPUB and Kindle. Book excerpt: This project has also generated the software suite FORGE which allows users to perform fast and robust analysis of GWAS, allowing researchers with a lack of experience in bioinformatics and statistical genetics, e.g. wet-laboratory based biologists, to perform both gene-based and pathway analyses to generate a systems biology interpretation of their phenotype of interest.
Author : D.C. Rao
Publisher : Academic Press
ISBN 13 : 0080569110
Total Pages : 788 pages
Book Rating : 4.0/5 (85 download)
Download or read book Genetic Dissection of Complex Traits written by D.C. Rao and published by Academic Press. This book was released on 2008-04-23 with total page 788 pages. Available in PDF, EPUB and Kindle. Book excerpt: The field of genetics is rapidly evolving and new medical breakthroughs are occuring as a result of advances in knowledge of genetics. This series continually publishes important reviews of the broadest interest to geneticists and their colleagues in affiliated disciplines. Five sections on the latest advances in complex traits Methods for testing with ethical, legal, and social implications Hot topics include discussions on systems biology approach to drug discovery; using comparative genomics for detecting human disease genes; computationally intensive challenges, and more
Author : Nan M. Laird
Publisher : Springer Science & Business Media
ISBN 13 : 1441973389
Total Pages : 226 pages
Book Rating : 4.4/5 (419 download)
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.
Author : Eleftheria Zeggini
Publisher : Academic Press
ISBN 13 : 0123751438
Total Pages : 353 pages
Book Rating : 4.1/5 (237 download)
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
Author : Sheng Yang
Publisher : Frontiers Media SA
ISBN 13 : 2889714675
Total Pages : 113 pages
Book Rating : 4.8/5 (897 download)
Download or read book Integrative Analysis of Genome-Wide Association Studies and Single-Cell Sequencing Studies written by Sheng Yang and published by Frontiers Media SA. This book was released on 2021-09-09 with total page 113 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Florian Frommlet
Publisher : Springer
ISBN 13 : 1447153103
Total Pages : 232 pages
Book Rating : 4.4/5 (471 download)
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.
Author : David J. Balding
Publisher : John Wiley & Sons
ISBN 13 : 1119429250
Total Pages : 1828 pages
Book Rating : 4.1/5 (194 download)
Download or read book Handbook of Statistical Genomics written by David J. Balding and published by John Wiley & Sons. This book was released on 2019-07-09 with total page 1828 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.
Author : William L. Welbourn (Jr.)
Publisher :
ISBN 13 :
Total Pages : 326 pages
Book Rating : 4.:/5 (88 download)
Download or read book Robust Computational Tools for Multiple Testing with Genetic Association Studies written by William L. Welbourn (Jr.) and published by . This book was released on 2012 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: Resolving the interplay of the genetic components of a complex disease is a challenging endeavor. Over the past several years, genome-wide association studies (GWAS) have emerged as a popular approach at locating common genetic variation within the human genome associated with disease risk. Assessing genetic-phenotype associations upon hundreds of thousands of genetic markers using the GWAS approach, introduces the potentially high number of false positive signals and requires statistical correction for multiple hypothesis testing. Permutation tests are considered the gold standard for multiple testing correction in GWAS, because they simultaneously provide unbiased Type I error control and high power. However, they demand heavy computational effort, especially with large-scale data sets of modern GWAS. In recent years, the computational problem has been circumvented by using approximations to permutation tests, but several studies have posed sampling conditions in which these approximations are suggestive to be biased. We have developed an optimized parallel algorithm for the permutation testing approach to multiple testing correction in GWAS, whose implementation essentially abates the computational problem. When introduced to GWAS data, our algorithm yields rapid, precise, and powerful multiplicity adjustment, many orders of magnitude faster than existing employed GWAS statistical software. Although GWAS have identified many potentially important genetic associations which will advance our understanding of human disease, the common variants with modest effects on disease risk discovered through this approach likely account for a small proportion of the heritability in complex disease. On the other hand, interactions between genetic and environmental factors could account for a substantial proportion of the heritability in a complex disease and are overlooked within the GWAS approach. We have developed an efficient and easily implemented tool for genetic association studies, whose aim is identifying genes involved in a gene-environment interaction. Our approach is amenable to a wide range of association studies and assorted densities in sampled genetic marker panels, and incorporates resampling for multiple testing correction. Within the context of a case-control study design we demonstrate by way of simulation that our proposed method offers greater statistical power to detect gene-environment interaction, when compared to several competing approaches to assess this type of interaction.
Author : William K. Scott
Publisher : John Wiley & Sons
ISBN 13 : 1118123913
Total Pages : 340 pages
Book Rating : 4.1/5 (181 download)
Download or read book Genetic Analysis of Complex Disease written by William K. Scott and published by John Wiley & Sons. This book was released on 2021-12-06 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genetic Analysis of Complex Diseases An up-to-date and complete treatment of the strategies, designs and analysis methods for studying complex genetic disease in human beings In the newly revised Third Edition of Genetic Analysis of Complex Diseases, a team of distinguished geneticists delivers a comprehensive introduction to the most relevant strategies, designs and methods of analysis for the study of complex genetic disease in humans. The book focuses on concepts and designs, thereby offering readers a broad understanding of common problems and solutions in the field based on successful applications in the design and execution of genetic studies. This edited volume contains contributions from some of the leading voices in the area and presents new chapters on high-throughput genomic sequencing, copy-number variant analysis and epigenetic studies. Providing clear and easily referenced overviews of the considerations involved in genetic analysis of complex human genetic disease, including sampling, design, data collection, linkage and association studies and social, legal and ethical issues. Genetic Analysis of Complex Diseases also provides: A thorough introduction to study design for the identification of genes in complex traits Comprehensive explorations of basic concepts in genetics, disease phenotype definition and the determination of the genetic components of disease Practical discussions of modern bioinformatics tools for analysis of genetic data Reflecting on responsible conduct of research in genetic studies, as well as linkage analysis and data management New expanded chapter on complex genetic interactions This latest edition of Genetic Analysis of Complex Diseases is a must-read resource for molecular biologists, human geneticists, genetic epidemiologists and pharmaceutical researchers. It is also invaluable for graduate students taking courses in statistical genetics or genetic epidemiology.
Download or read book STATISTICAL METHODS IN GENETIC STUDIES written by and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract : This dissertation includes three Chapters. A brief description of each chapter is organized as follows. In Chapter 1, we proposed a new method, called MF-TOWmuT, for genome-wide association studies with multiple genetic variants and multiple phenotypes using family samples. MF-TOWmuT uses kinship matrix to account for sample relatedness. It is worth mentioning that in simulations, we considered hidden polygenic effects and varied the proportion of variance contributed by it to generate phenotypes. Simulation studies show that MF-TOWmuT can preserve the type I error rates and is more powerful than several existing methods in different simulation scenarios, MFTOWmuT is also quite robust to the proportion of variance explained by invisible polygenic effects and to the direction of effects of genetic variants. In Chapter 2, we proposed a fast and efficient low rank penalized regression with the Elastic Net penalty for the eQTL mapping, called LORSEN. By considering the Elastic Net penalty instead of the L1 penalty, our method can overcome two crucial drawbacks of the L1 penalty, and outperforms two commonly used methods for the eQTL mapping, LORS and FastLORS, in many simulation scenarios in terms of average Area Under the Curve (AUC). In Chapter 3, we proposed a bipartite network-based penalized regression model for the eQTL mapping, called BiNetPeR. This method takes into account the SNPgene marginal association evidence to construct the SNP-gene bipartite network, then uses such a bipartite network to obtain the projected SNP network. Based on the normalized Laplacian matrix of the projected SNP network, we then formulate the eQTL mapping into a penalized regression model. Our simulation results show that our proposed method can maintain the appropriate false positive rate and outperforms two competing methods for the eQTL mapping, FastLORS and mtLasso2G.
Author : Lin Li
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (654 download)
Download or read book Statistical Methods and Analysis for Genome-wide Association Studies written by Lin Li and published by . This book was released on 2010 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genome-wide association (GWA) studies utilize a large number of genetic variants, usually single nucleotide polymorphisms (SNPs), across the entire genome to identify genetic basis underlying disease susceptibility or phenotypic variation in a trait of interest. A commonly used analysis tool is single marker analysis (SMA), which tests one SNP at a time. Although it has been successful in identifying some causal loci, further enhancements are possible by considering multi-locus methods that investigate a large number of SNPs simultaneously. One difficulty of doing so is high dimensionality, i.e. the large number of SNPs, making it a challenging statistical problem. My first project addresses this problem in case-control GWA studies. Both the logistic and probit models are considered for binary traits, and three-component mixture priors are assumed to model the fact that only a few SNPs have non-negligible effects. To estimate posterior distributions, I propose three Markov chain Monte Carlo techniques. Specifically, an adaptive independence sampler is proposed for the logistic model, and data augmentation methods are developed for both logistic and probit models. Simulations suggest that they nearly always outperform SMA. The second project deals with GWA studies on quantitative traits with the confounding of population structure. A linear mixed model is used to account for cryptic relatedness between individuals in the sample. I propose an algorithm that is based on least angle regression and can efficiently select a small number of SNPs that are likely to be associated with the trait. Simulations show that the proposed algorithm tends to yield higher ranks for causal loci than least angle regression directly applied, and that both outperform SMA. My third project is part of the so-called CanMap project. More than 1,000 domestic dogs from different breeds, wild canids and village dogs were genotyped on a dense SNP array, and my responsibility was to carry out a GWA analysis for the domestic dog on body weight and other morphological traits including height, shapes, etc. The GWA results enrich our understanding of the impact of strong directional selection on the genetic architecture of complex traits known to be under selection.
Author : Gabriel Hoffman
Publisher :
ISBN 13 :
Total Pages : 336 pages
Book Rating : 4.:/5 (913 download)
Download or read book Modeling Biological Processes in Genome-wide Association Studies Using Regularized Regression written by Gabriel Hoffman and published by . This book was released on 2013 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genome-wide association studies (GWAS) have become a a widely adopted approach to identify genetic variation that produces variation in complex phenotype. Standard statistical methods are able to identify strong associations in these datasets, but more sophisticated statistical methods that model complex aspects of the biological data can identify weaker associations and further elucidate the underlying molecular biology. We develop and apply statistical methods that explicitly model two aspects of GWAS data using two complementary forms of regularized regression. First, we model the polygenic architecture of complex phenotypes using feature selection methods in a penalized regression framework. We propose novel algorithmic, computational and heuristic approaches in order to produce a method that scales to high dimensional GWAS data and increases power to detect weak associations that are not detectable by standard tests. Second, we model the covariance between individuals due to kinship and population structure using a linear mixed model that regularizes the statistical contribution of a metric of ancestry. Linear mixed models have been widely adopted for analysis of GWAS data, but their theoretical properties have not been examined in this context. We formalize the statistical properties of the linear mixed model, develop a novel interpretation in relation to population genetics, and propose a novel low rank linear mixed model that learns the dimensionality of the correction for kinship and population structure from the data. Finally, we combine these two complementary regularized regression models into a penalized linear mixed model. We develop a unified model incorporating a novel algorithm with novel approaches to tuning nonconvex penalties and determining the optimal stopping point in the regularization path. Leveraging recent work on assessing significance of selected features, we produce a well-principled and scalable statistical method applicable to feature selection, hypothesis testing and prediction in many contexts.
