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Statistical Methods Computing And Resources For Genome Wide Association Studies
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Book Synopsis Statistical Methods, Computing, and Resources for Genome-Wide Association Studies by : Riyan Cheng
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:
Book Synopsis Methods in Statistical Genomics by : Philip Chester Cooley
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 Some of the material is unpublished and contains commentary and unpublished research; other chapters (Chapters 4 through 7) have been published in other journals.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.
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 Statistical Methods for Genome-Wide Association Studies on Biobank Data by : Christopher Austin German
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.
Book Synopsis Design, Analysis, and Interpretation of Genome-Wide Association Scans by : Daniel O. Stram
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.
Book Synopsis Statistical Methods for Genome Wide Association Studies by : Malin Östensson
Download or read book Statistical Methods for Genome Wide Association Studies written by Malin Östensson and published by . This book was released on 2012 with total page 181 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Robust and Fast Statistical Methods for Genome-wide Association Studies by : Julian Erik Hecker
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:
Book Synopsis Omic Association Studies with R and Bioconductor by : Juan R. González
Download or read book Omic Association Studies with R and Bioconductor written by Juan R. González and published by CRC Press. This book was released on 2019-06-14 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: After the great expansion of genome-wide association studies, their scientific methodology and, notably, their data analysis has matured in recent years, and they are a keystone in large epidemiological studies. Newcomers to the field are confronted with a wealth of data, resources and methods. This book presents current methods to perform informative analyses using real and illustrative data with established bioinformatics tools and guides the reader through the use of publicly available data. Includes clear, readable programming codes for readers to reproduce and adapt to their own data. Emphasises extracting biologically meaningful associations between traits of interest and genomic, transcriptomic and epigenomic data Uses up-to-date methods to exploit omic data Presents methods through specific examples and computing sessions Supplemented by a website, including code, datasets, and solutions
Book Synopsis Handbook of Statistical Genomics by : David J. Balding
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.
Book Synopsis Genetic Dissection of Complex Traits by : D.C. Rao
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
Book Synopsis Statistical Methods for Genome-wide Association Studies and Personalized Medicine by :
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 0 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.
Book Synopsis Statistical Methods for Detecting Genetic Risk Factors of a Disease with Applications to Genome-wide Association Studies by : Fadhaa Ali
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:
Book Synopsis Genome-Wide Association Studies by : Davoud Torkamaneh
Download or read book Genome-Wide Association Studies written by Davoud Torkamaneh and published by Humana. This book was released on 2023-06-15 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This detailed collection explores genome-wide association studies (GWAS), which have revolutionized the investigation of complex traits over the past decade and have unveiled numerous useful genotype–phenotype associations in plants. The book describes the key concepts and methods underlying GWAS, including the genetic architecture underlying variation for phenotypic traits, the structure of genetic variation in plants, technologies for capturing genetic information, study designs, and the statistical models and bioinformatics tools used for data analysis. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of invaluable implementation advice that leads to the most fruitful research results. Authoritative and practical, Genome-Wide Association Studies serves as an extremely valuable resource for the plant research community by rendering GWAS analysis less challenging and more accessible to a broader group of users.
Book Synopsis Statistical Methods for Transcriptome-wide Association Studies in Ancestrally Diverse Populations by : Anna V. Mikhaylova
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.
Book Synopsis Statistical Methods for Integrative Analysis of Genomic Data by : Jingsi Ming
Download or read book Statistical Methods for Integrative Analysis of Genomic Data written by Jingsi Ming and published by . This book was released on 2018 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Thousands of risk variants underlying complex phenotypes (quantitative traits and diseases) have been identified in genome-wide association studies (GWAS). However, there are still several challenges towards deepening our understanding of the genetic architectures of complex phenotypes. First, the majority of GWAS hits are in non-coding region and their biological interpretation is still unclear. Second, most complex traits are suggested to be highly polygenic, i.e., they are affected by a vast number of risk variants with individually small or moderate effects, whereas a large proportion of risk variants with small effects remain unknown. Third, accumulating evidence from GWAS suggests the pervasiveness of pleiotropy, a phenomenon that some genetic variants can be associated with multiple traits, but there is a lack of unified framework which is scalable to reveal relationship among a large number of traits and prioritize genetic variants simultaneously with functional annotations integrated. In this thesis, we propose two statistical methods to address these challenges using integrative analysis of summary statistics from GWASs and functional annotations. In the first part, we propose a latent sparse mixed model (LSMM) to integrate functional annotations with GWAS data. Not only does it increase the statistical power of identifying risk variants, but also offers more biological insights by detecting relevant functional annotations. To allow LSMM scalable to millions of variants and hundreds of functional annotations, we developed an efficient variational expectation-maximization (EM) algorithm for model parameter estimation and statistical inference. We first conducted comprehensive simulation studies to evaluate the performance of LSMM. Then we applied it to analyze 30 GWASs of complex phenotypes integrated with nine genic category annotations and 127 cell-type specific functional annotations from the Roadmap project. The results demonstrate that our method possesses more statistical power than conventional methods, and can help researchers achieve deeper understanding of genetic architecture of these complex phenotypes. In the second part, we propose a latent probit model (LPM) which combines summary statistics from multiple GWASs and functional annotations, to characterize relationship and increase statistical power to identify risk variants. LPM can also perform hypothesis testing for pleiotropy and annotations enrichment. To enable the scalability of LPM as the number of GWASs increases, we developed an efficient parameter-expanded EM (PX-EM) algorithm which can execute parallelly. We first validated the performance of LPM through comprehensive simulations, then applied it to analyze 44 GWASs with nine genic category annotations. The results demonstrate the benefits of LPM and can offer new insights of disease etiology.
Book Synopsis Statistical Methods in Genetic Association by : Ge Zhang
Download or read book Statistical Methods in Genetic Association written by Ge Zhang and published by . This book was released on 2007 with total page 283 pages. Available in PDF, EPUB and Kindle. Book excerpt: Association studies offer great promise in dissecting the genetic basic of human complex diseases. The rapid expansion of genomic information and the cost-effective genotyping technologies have enabled us to systematically interrogate the role of human genetic variation in common diseases by genome-wide association (GWA) mapping. However, the scale and complexity of such studies will raise significant challenges in study design and data analysis. In this dissertation, we investigated several statistical problems that relevant to population-based association studies and the fine-scale mapping of genetic variants that influence susceptibility to complex diseases. First, we developed a variance-based effect size estimator for the locus-specific genetic effect. Comparing to the traditional measures, the proposed estimator is less sensitive to the risk allele frequency and the population prevalence of the disease. We demonstrated the sample size requirement would be considerable large to obtain an accurate estimate on moderate genetic effect and the sample size will increase exponentially with increased demand for precision. We next compared the power of different association test statistics. We observed that the genotype based single-locus tests is generally more powerful than the multi-locus or haplotype based statistics, especially for risk alleles far from additive; and the power of genotype based tests can be uniformly improved by applying the ordered restriction on genotypic risks. Finally, we tested different GWA strategies and explored the factors that may influence the power of GWA studies by extensive simulations using empirical genotype data from the HapMap ENCODE Project. Our results indicate that current commercial genome-wide typing products are capable of capturing most of the common risk variants; however, their power in detecting rare risk variants or variants within recombination hot spots is not satisfactory. We also showed that the properties of the risk variants (e.g. allele frequency, local recombination rate, and functional category) have significant impacts on the power of GWA. The results generated from this comprehensive exercise would be helpful for developing efficient GWA studies.
Book Synopsis Modeling Biological Processes in Genome-wide Association Studies Using Regularized Regression by : Gabriel Hoffman
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.
