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Author: Nourollah Ahmadi Publisher: Springer Nature ISBN: 1071622056 Category : Science Languages : en Pages : 651
Book Description
This volume explores the conceptual framework and the practical issues related to genomic prediction of complex traits in human medicine and in animal and plant breeding. The book is organized into five parts. Part One reminds molecular genetics approaches intending to predict phenotypic variations. Part Two presents the principles of genomic prediction of complex traits, and reviews factors that affect its reliability. Part Three describes genomic prediction methods, including machine-learning approaches, accounting for different degree of biological complexity, and reviews the associated computer-packages. Part Four reports on emerging trends such as phenomic prediction and incorporation into genomic prediction models of “omics” data and crop growth models. Part Five is dedicated to lessons learned from cases studies in the fields of human health and animal and plant breeding, and to methods for analysis of the economic effectiveness of genomic prediction. Written in the highly successful Methods in Molecular Biology series format, the book provides theoretical bases and practical guidelines for an informed decision making of practitioners and identifies pertinent routes for further methodological researches. Cutting-edge and thorough, Complex Trait Predictions: Methods and Protocols is a valuable resource for scientists and researchers who are interested in learning more about this important and developing field. Chapters 3, 9, 13, 14, and 21 are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Author: Nourollah Ahmadi Publisher: Springer Nature ISBN: 1071622056 Category : Science Languages : en Pages : 651
Book Description
This volume explores the conceptual framework and the practical issues related to genomic prediction of complex traits in human medicine and in animal and plant breeding. The book is organized into five parts. Part One reminds molecular genetics approaches intending to predict phenotypic variations. Part Two presents the principles of genomic prediction of complex traits, and reviews factors that affect its reliability. Part Three describes genomic prediction methods, including machine-learning approaches, accounting for different degree of biological complexity, and reviews the associated computer-packages. Part Four reports on emerging trends such as phenomic prediction and incorporation into genomic prediction models of “omics” data and crop growth models. Part Five is dedicated to lessons learned from cases studies in the fields of human health and animal and plant breeding, and to methods for analysis of the economic effectiveness of genomic prediction. Written in the highly successful Methods in Molecular Biology series format, the book provides theoretical bases and practical guidelines for an informed decision making of practitioners and identifies pertinent routes for further methodological researches. Cutting-edge and thorough, Complex Trait Predictions: Methods and Protocols is a valuable resource for scientists and researchers who are interested in learning more about this important and developing field. Chapters 3, 9, 13, 14, and 21 are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Author: Nourollah Ahmadi Publisher: Humana ISBN: 9781071622070 Category : Science Languages : en Pages : 0
Book Description
This volume explores the conceptual framework and the practical issues related to genomic prediction of complex traits in human medicine and in animal and plant breeding. The book is organized into five parts. Part One reminds molecular genetics approaches intending to predict phenotypic variations. Part Two presents the principles of genomic prediction of complex traits, and reviews factors that affect its reliability. Part Three describes genomic prediction methods, including machine-learning approaches, accounting for different degree of biological complexity, and reviews the associated computer-packages. Part Four reports on emerging trends such as phenomic prediction and incorporation into genomic prediction models of “omics” data and crop growth models. Part Five is dedicated to lessons learned from cases studies in the fields of human health and animal and plant breeding, and to methods for analysis of the economic effectiveness of genomic prediction. Written in the highly successful Methods in Molecular Biology series format, the book provides theoretical bases and practical guidelines for an informed decision making of practitioners and identifies pertinent routes for further methodological researches. Cutting-edge and thorough, Complex Trait Predictions: Methods and Protocols is a valuable resource for scientists and researchers who are interested in learning more about this important and developing field. Chapters 3, 9, 13, 14, and 21 are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Author: Cedric Gondro Publisher: Humana Press ISBN: 9781627034463 Category : Science Languages : en Pages : 0
Book Description
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.
Author: Robert R. Trippi Publisher: Irwin Professional Publishing ISBN: Category : Artificial intelligence Languages : en Pages : 872
Book Description
This completely updated version of the classic first edition offers a wealth of new material reflecting the latest developments in teh field. For investment professionals seeking to maximize this exciting new technology, this handbook is the definitive information source.
Author: Rajeev K. Varshney Publisher: Springer ISBN: 3319631705 Category : Technology & Engineering Languages : en Pages : 258
Book Description
Genomic Selection for Crop Improvement serves as handbook for users by providing basic as well as advanced understandings of genomic selection. This useful review explains germplasm use, phenotyping evaluation, marker genotyping methods, and statistical models involved in genomic selection. It also includes examples of ongoing activities of genomic selection for crop improvement and efforts initiated to deploy the genomic selection in some important crops. In order to understand the potential of GS breeding, it is high time to bring complete information in the form of a book that can serve as a ready reference for geneticist and plant breeders.
Author: Alison Bashford Publisher: OUP USA ISBN: 0195373146 Category : History Languages : en Pages : 607
Book Description
Philippa Levine is the Mary Helen Thompson Centennial Professor in the Humanities at the University of Texas at Austin. Her books include Prostitution, Race and Politics: Policing Venereal Disease in the British Empire, and The British Empire, Sunrise to Sunset. --
Author: Cedric Gondro Publisher: Humana Press ISBN: 9781493959648 Category : Science Languages : en Pages : 566
Book Description
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.
Author: Fikret Isik Publisher: Springer ISBN: 3319551779 Category : Science Languages : en Pages : 400
Book Description
This book fills the gap between textbooks of quantitative genetic theory, and software manuals that provide details on analytical methods but little context or perspective on which methods may be most appropriate for a particular application. Accordingly this book is composed of two sections. The first section (Chapters 1 to 8) covers topics of classical phenotypic data analysis for prediction of breeding values in animal and plant breeding programs. In the second section (Chapters 9 to 13) we provide the concept and overall review of available tools for using DNA markers for predictions of genetic merits in breeding populations. With advances in DNA sequencing technologies, genomic data, especially single nucleotide polymorphism (SNP) markers, have become available for animal and plant breeding programs in recent years. Analysis of DNA markers for prediction of genetic merit is a relatively new and active research area. The algorithms and software to implement these algorithms are changing rapidly. This section represents state-of-the-art knowledge on the tools and technologies available for genetic analysis of plants and animals. However, readers should be aware that the methods or statistical packages covered here may not be available or they might be out of date in a few years. Ultimately the book is intended for professional breeders interested in utilizing these tools and approaches in their breeding programs. Lastly, we anticipate the usage of this volume for advanced level graduate courses in agricultural and breeding courses.
Author: Kefei Chen Publisher: Frontiers Media SA ISBN: 2832540619 Category : Science Languages : en Pages : 147
Book Description
The advances in “omics” technologies have enabled unprecedented progress in agricultural and biological sciences. The synergy of high-performance computing, high throughput omics approaches, and high dimensional phenotyping data with high spatial and temporal resolution have demonstrated the capacity to enhance our understanding of biological mechanisms but also to provide powerful insights into dissecting the genetic basis of complex traits with agricultural and economical importance. Genome-wide association study (GWAS) has become a useful approach to identify mutations that underlie diseases and complex traits and has provided important insights in exploring genetic profiles. However, it is less suitable for quantitative traits influenced by a large number of genes with small effects. In addition, false discoveries are a major concern and can be partially attributed to population structure. Genomic selection holds the promise to overcome the limitations by using whole-genome information to predict the genetic merits of phenotypes. It has been a powerful tool for predicting the breeding values of candidates for selection in breeding populations. One of the challenges of genomic prediction of breeding values with large-p-with-small-n regressions is to develop robust and efficient approaches that accurately predict phenotypic traits as functions of genotypic and environmental inputs. In addition, the integration of multi-omics data in phenotypic prediction would offer the opportunity to understand the flow of information that underlies the phenotypic traits.
Author: Nourollah Ahmadi
Author: Nourollah Ahmadi
Author: Nourollah Ahmadi
Author: Cedric Gondro
Author: Robert R. Trippi
Author: Rajeev K. Varshney
Author: Alison Bashford
Author: Cedric Gondro
Author: Segun Fatumo
Author: Fikret Isik
Author: Kefei Chen