Genome Wide Association Studies
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Genome Wide Association Studies
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Author : Krishnarao Appasani
language : en
Publisher: Cambridge University Press
Release Date : 2016-01-14
Genome Wide Association Studies written by Krishnarao Appasani and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-01-14 with Medical categories.
Experts from academia and industry highlight the potential of genome-wide association studies from basic science to clinical and biotechnological/pharmaceutical applications.
Integrative Analysis Of Genome Wide Association Studies And Single Cell Sequencing Studies
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Author : Sheng Yang
language : en
Publisher: Frontiers Media SA
Release Date : 2021-09-09
Integrative Analysis Of Genome Wide Association Studies And Single Cell Sequencing Studies written by Sheng Yang and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-09 with Science categories.
Statistical Methods Computing And Resources For Genome Wide Association Studies
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Author : Riyan Cheng
language : en
Publisher: Frontiers Media SA
Release Date : 2021-08-24
Statistical Methods Computing And Resources For Genome Wide Association Studies written by Riyan Cheng and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-24 with Science categories.
Genome Wide Association Studies
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Author : Tatsuhiko Tsunoda
language : en
Publisher: Springer Nature
Release Date : 2019-10-31
Genome Wide Association Studies written by Tatsuhiko Tsunoda and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-31 with Medical categories.
This book examines the utility of genome-wide association studies (GWAS) in the era of next-generation sequencing and big data, identifies limitations and potential means of overcoming them, and looks to the future of GWAS and what may lay beyond. GWAS are among the most powerful tools for elucidating the genetic aspects of human and disease diversity. In Genome-Wide Association Studies, experts in the field explore in depth the impacts of GWAS on genomic research into a variety of common diseases, including cardiovascular, autoimmune, diabetic, cancer, and infectious diseases. The book will equip readers with a sound understanding both of the types of disease and phenotypes that are suited for GWAS and of the ways in which a road map resulting from GWAS can lead to the realization of personalized/precision medicine: functional analysis, drug seeds, pathway analysis, disease mechanism, risk prediction, and diagnosis.
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.
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.
Genome Wide Association Studies And Genomic Prediction
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Author : Cedric Gondro
language : en
Publisher: Humana Press
Release Date : 2013-06-12
Genome Wide Association Studies And Genomic Prediction written by Cedric Gondro and has been published by Humana Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-12 with Science categories.
With the detailed genomic information that is now becoming available, we have a plethora of data that allows researchers to address questions in a variety of areas. Genome-wide association studies (GWAS) have become a vital approach to identify candidate regions associated with complex diseases in human medicine, production traits in agriculture, and variation in wild populations. Genomic prediction goes a step further, attempting to predict phenotypic variation in these traits from genomic information. Genome-Wide Association Studies and Genomic Prediction pulls together expert contributions to address this important area of study. The volume begins with a section covering the phenotypes of interest as well as design issues for GWAS, then moves on to discuss efficient computational methods to store and handle large datasets, quality control measures, phasing, haplotype inference, and imputation. Later chapters deal with statistical approaches to data analysis where the experimental objective is either to confirm the biology by identifying genomic regions associated to a trait or to use the data to make genomic predictions about a future phenotypic outcome (e.g. predict onset of disease). As part of the Methods in Molecular Biology series, chapters provide helpful, real-world implementation advice.
Machine Learning In Genome Wide Association Studies
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Author : Ting Hu
language : en
Publisher: Frontiers Media SA
Release Date : 2020-12-15
Machine Learning In Genome Wide Association Studies written by Ting Hu and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-15 with Science categories.
This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.
Assessing Gene Environment Interactions In Genome Wide Association Studies Statistical Approaches
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Author : Philip C. Cooley
language : en
Publisher: RTI Press
Release Date : 2014-05-14
Assessing Gene Environment Interactions In Genome Wide Association Studies Statistical Approaches written by Philip C. 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 2014-05-14 with Science categories.
In this report, we address a scenario that uses synthetic genotype case-control data that is influenced by environmental factors in a genome-wide association study (GWAS) context. The precise way the environmental influence contributes to a given phenotype is typically unknown. Therefore, our study evaluates how to approach a GWAS that may have an environmental component. Specifically, we assess different statistical models in the context of a GWAS to make association predictions when the form of the environmental influence is questionable. We used a simulation approach to generate synthetic data corresponding to a variety of possible environmental-genetic models, including a “main effects only” model as well as a “main effects with interactions” model. Our method takes into account the strength of the association between phenotype and both genotype and environmental factors, but we focus on low-risk genetic and environmental risks that necessitate using large sample sizes (N = 10,000 and 200,000) to predict associations with high levels of confidence. We also simulated different Mendelian gene models, and we analyzed how the collection of factors influences statistical power in the context of a GWAS. Using simulated data provides a “truth set” of known outcomes such that the association-affecting factors can be unambiguously determined. We also test different statistical methods to determine their performance properties. Our results suggest that the chances of predicting an association in a GWAS is reduced if an environmental effect is present and the statistical model does not adjust for that effect. This is especially true if the environmental effect and genetic marker do not have an interaction effect. The functional form of the statistical model also matters. The more accurately the form of the environmental influence is portrayed by the statistical model, the more accurate the prediction will be. Finally, even with very large samples sizes, association predictions involving recessive markers with low risk can be poor
Genome Wide Association Studies And Genomic Prediction
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Author : Cedric Gondro
language : en
Publisher: Humana Press
Release Date : 2017-04-30
Genome Wide Association Studies And Genomic Prediction written by Cedric Gondro and has been published by Humana Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-04-30 with Science categories.
With the detailed genomic information that is now becoming available, we have a plethora of data that allows researchers to address questions in a variety of areas. Genome-wide association studies (GWAS) have become a vital approach to identify candidate regions associated with complex diseases in human medicine, production traits in agriculture, and variation in wild populations. Genomic prediction goes a step further, attempting to predict phenotypic variation in these traits from genomic information. Genome-Wide Association Studies and Genomic Prediction pulls together expert contributions to address this important area of study. The volume begins with a section covering the phenotypes of interest as well as design issues for GWAS, then moves on to discuss efficient computational methods to store and handle large datasets, quality control measures, phasing, haplotype inference, and imputation. Later chapters deal with statistical approaches to data analysis where the experimental objective is either to confirm the biology by identifying genomic regions associated to a trait or to use the data to make genomic predictions about a future phenotypic outcome (e.g. predict onset of disease). As part of the Methods in Molecular Biology series, chapters provide helpful, real-world implementation advice.