Genetic Association Studies
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Analysis Of Genetic Association Studies
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Author : Gang Zheng
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-01-10
Analysis Of Genetic Association Studies written by Gang Zheng and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-01-10 with Mathematics categories.
This reference book for the analysis of genetic association studies makes an ideal companion to graduate-level students. In addition to providing derivations, the book deploys real examples and simulations to illustrate its step-by-step applications.
Genetic Association Studies
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Author : Mehmet Tevfik Dorak
language : en
Publisher: Garland Science
Release Date : 2016-09-26
Genetic Association Studies written by Mehmet Tevfik Dorak and has been published by Garland Science this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-09-26 with Science categories.
Genetic Association Studies is designed for students of public health, epidemiology, and the health sciecnes, covering the main principles of molecular genetics, population genetics, medical genetics, epidemiology and statistics. It presents a balanced view of genetic associations with coverage of candidate gene studies as well as genome-wide association studies. All aspects of a genetic association study are included, from the lab to analysis and interpretation of results, but also bioinformatics approaches to causality assessment. The role of the environment in genetic disease is also highlighted. Genetic Association Studies will enable readers to understand and critique genetic association studies and set them on the way to designing, executing, analyzing, interpreting, and reporting their own.
Analysis Of Complex Disease Association Studies
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Author : Eleftheria Zeggini
language : en
Publisher: Academic Press
Release Date : 2010-11-17
Analysis Of Complex Disease Association Studies written by Eleftheria Zeggini and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-11-17 with Medical categories.
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
Design Analysis And Interpretation Of Genome Wide Association Scans
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Author : Daniel O. Stram
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-11-23
Design Analysis And Interpretation Of Genome Wide Association Scans written by Daniel O. Stram and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-23 with Medical categories.
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.
Applied Statistical Genetics With R
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Author : Andrea S. Foulkes
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-04-28
Applied Statistical Genetics With R written by Andrea S. Foulkes and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-04-28 with Medical categories.
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.
Methods In Statistical Genomics
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Author : Philip Chester Cooley
language : en
Publisher: RTI Press
Release Date : 2016-08-29
Methods In Statistical Genomics written by Philip Chester Cooley and has been published by RTI Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-08-29 with Medical categories.
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.
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Author :
language : en
Publisher:
Release Date : 1877
written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1877 with categories.
Improving Discovery Of Causal Variants In Genetic Association Studies
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Author :
language : en
Publisher:
Release Date : 2004
Improving Discovery Of Causal Variants In Genetic Association Studies written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with categories.
In recent years population-based association studies have been advocated as the most powerful method of discovering genetic loci that are associated with heritable traits, particularly for complex traits that are likely caused by a variety of factors including environmental effects and multiple genetic loci. Genome-wide association studies (GWAS) have already yielded a large number of such associations, but there is growing concern that the results of these studies are not explaining as much genetic variation as they were expected to. Chapter 2 discusses tagging and imputation to leverage the information available on commercial genotyping chips to make inferences about variants found in large reference samples such as those made available by the International HapMap Consortium. Transferability of multi-marker tagging is assessed. Tagging and imputation are compared, and a method of using tagging to select a reduced tag set to be used for imputation. Chapter 3 details how multiple low frequency causal variants can create synthetic associations among more common variants and may be responsible for many of the genome-wide associations that have already been observed. Examples of synthetic associations are demonstrated in congenital deafness and sickle-cell anemia. Chapter 4 examines issues related to combining samples of diverse genetic ancestry for analysis in genetic association studies. Through simulation it is shown that type I error can be controlled and power increased using statistical methods to account for differences in populations.
Statistical Methods For Gene Selection And Genetic Association Studies
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Author :
language : en
Publisher:
Release Date : 2023
Statistical Methods For Gene Selection And Genetic Association Studies written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
Abstract : This dissertation includes five Chapters. A brief description of each chapter is organized as follows. In Chapter One, we propose a signed bipartite genotype and phenotype network (GPN) by linking phenotypes and genotypes based on the statistical associations. It provides a new insight to investigate the genetic architecture among multiple correlated phenotypes and explore where phenotypes might be related at a higher level of cellular and organismal organization. We show that multiple phenotypes association studies by considering the proposed network are improved by incorporating the genetic information into the phenotype clustering. In Chapter Two, we first illustrate the proposed GPN to GWAS summary statistics. Then, we assess contributions to constructing a well-defined GPN with a clear representation of genetic associations by comparing the network properties with a random network, including connectivity, centrality, and community structure. The network topology annotations based on the sparse representations of GPN can be used to understand the disease heritability for the highly correlated phenotypes. In applications of phenome-wide association studies, the proposed GPN can identify more significant pairs of genetic variant and phenotype categories. In Chapter Three, a powerful and computationally efficient gene-based association test is proposed, aggregating information from different gene-based association tests and also incorporating expression quantitative trait locus information. We show that the proposed method controls the type I error rates very well and has higher power in the simulation studies and can identify more significant genes in the real data analyses. In Chapter Four, we develop six statistical selection methods based on the penalized regression for inferring target genes of a transcription factor (TF). In this study, the proposed selection methods combine statistics, machine learning , and convex optimization approach, which have great efficacy in identifying the true target genes. The methods will fill the gap of lacking the appropriate methods for predicting target genes of a TF, and are instrumental for validating experimental results yielding from ChIP-seq and DAP-seq, and conversely, selection and annotation of TFs based on their target genes. In Chapter Five, we propose a gene selection approach by capturing gene-level signals in network-based regression into case-control association studies with DNA sequence data or DNA methylation data, inspired by the popular gene-based association tests using a weighted combination of genetic variants to capture the combined effect of individual genetic variants within a gene. We show that the proposed gene selection approach have higher true positive rates than using traditional dimension reduction techniques in the simulation studies and select potentially rheumatoid arthritis related genes that are missed by existing methods.
Addressing Sources Of Bias In Genetic Association Studies
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Author :
language : en
Publisher:
Release Date : 2004
Addressing Sources Of Bias In Genetic Association Studies written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with categories.
Genome-wide association studies (GWAS) have become a popular method for the discovery of genetic variants associated with complex diseases or traits. As the size and scope of these studies increase in order to obtain higher power for determining significant associations, careful consideration of population structure becomes paramount. If individ- uals in a study come from different ethnic or ancestral backgrounds, variation in allele frequencies and disproportionate ancestry representation in cases and controls can lead to inflated Type I error rates. Over the years, several methods for controlling population stratification have been introduced, many of which rely on the use of multivariate dimension reduction methods. An important aspect of population stratification is to determine which loci exhibit evidence of population allele frequency differences. We introduce a method based on Hardy-Weinberg Disequilibrium to find substructure-informative markers coupled with the use of nonmetric Multidimensional Scaling (NMDS) in order to visualize popula- tion structure in a sample. We extend the use of NMDS in conjunction with nonparametric clustering to develop a test for association that corrects for population stratification. We show that NMDS is a preferable visualization technique for detecting multiple levels of relatedness within a set of individuals and that the subsequent test correction model is a more powerful test under realistic scenarios. Recent research has shown that technical bias due to differential genotyping errors between cases and controls can also inflate the Type I error rate, possibly an even more severe source of bias in GWAS. Current genotype calling algorithms rely on processing samples in batches due to computational constraints as well as concerns of differences in DNA collection, lab preparation and heterogeneous samples that can skew results of genotype calls. This thesis also addresses possible bias caused by differential genotyping due to.