Genome-Wide Association Studies and Genomic Prediction
Genome-Wide Association Studies and Genomic Prediction

Author: Cedric Gondro

Publisher: Humana Press

Published: 2013-06-12

Total Pages: 0

ISBN-13: 9781627034463

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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.

Genome-Wide Association Studies and Genomic Prediction
Genome-Wide Association Studies and Genomic Prediction

Author: Cedric Gondro

Publisher: Humana Press

Published: 2017-04-30

Total Pages: 566

ISBN-13: 9781493959648

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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.

Genome-Wide Association Studies
Genome-Wide Association Studies

Author: Tatsuhiko Tsunoda

Publisher: Springer Nature

Published: 2019-10-31

Total Pages: 209

ISBN-13: 9811381771

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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.

Genetic Dissection of Complex Traits
Genetic Dissection of Complex Traits

Author: D.C. Rao

Publisher: Academic Press

Published: 2008-04-23

Total Pages: 788

ISBN-13: 0080569110

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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

Genomic Prediction and Genome Wide Association Mapping for Disease Resistance in Wheat
Genomic Prediction and Genome Wide Association Mapping for Disease Resistance in Wheat

Author: Philomin Juliana

Publisher:

Published: 2017

Total Pages: 400

ISBN-13:

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Wheat (Triticum aestivum L.) is one of the major food crops in the world that is grown on more land area than any other commercial crop. The demand for wheat is expected to increase by 60% by 2050 which cannot be met with the current yield gain of 1%. Hence, it is important to evaluate different strategies for increasing the genetic gain in wheat. With this focus, we evaluated two strategies, genomic prediction and genome-wide association studies (GWAS) for disease resistance in CIMMYT’s international bread wheat screening nurseries (IBWSN). Our objective was to compare different prediction models for resistance to leaf rust (LR), stem rust (SR), stripe rust (STR), Septoria tritici blotch (STB), Stagonospora nodorum blotch (SNB) and tan spot (TS) in the 45th and 46th IBWSN entries. The prediction models tested include: Least-squares (LS), genomic-BLUP (G-BLUP), Bayesian ridge regression (BRR), Bayes A (BA), Bayes B (BB), Bayes C (BC), Bayesian least absolute shrinkage and selection operator (BL), reproducing kernel Hilbert spaces (RKHS) markers (RKHS-M), RKHS pedigree (RKHS-P) and RKHS markers and pedigree (RKHS-MP). The 333 lines in the 45th IBWSN and the 313 lines in the 46th IBWSN were genotyped using genotyping-by-sequencing markers. For the rusts, the mean prediction accuracies were 0.74 for LR seedling, 0.56 for LR APR, 0.65 for SR APR, 0.78 for YR seedling and 0.71 for YR APR. For the leaf spotting diseases, the mean genomic prediction accuracies were 0.45 for STB APR, 0.55 for SNB seedling, 0.66 for TS seedling and 0.48 for TS APR. Using genome-wide marker based models resulted in an average of 42-48% increase in accuracy over LS. Overall, the RKHS-MP model gave the highest accuracies, while LS gave the lowest. GWAS was also performed on these traits and several significant markers and candidate genes were identified. We conclude that implementing GWAS and genomic selection in breeding for these diseases would help to achieve higher accuracies and rapid gains from selection. ...

Machine Learning in Genome-Wide Association Studies
Machine Learning in Genome-Wide Association Studies

Author: Ting Hu

Publisher: Frontiers Media SA

Published: 2020-12-15

Total Pages: 74

ISBN-13: 2889662292

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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.

Delivering the Fruits of Plant Genomics
Delivering the Fruits of Plant Genomics

Author: Jennifer Elizabeth Spindel

Publisher:

Published: 2015

Total Pages: 314

ISBN-13:

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To address the multiple challenges to food security posed by global climate change, population growth and rising incomes, plant breeders are developing new crop varieties that can enhance both agricultural productivity and environmental sustainability. Current breeding practices, however, are unable to keep pace with demand. Genomic Selection (GS) is a new breeding method in which genome-wide markers are used to predict the breeding value of individuals in a breeding population. GS has been shown to improve breeding efficiency in dairy cattle and several crop plant species, and here I evaluate for the first time its efficacy for breeding inbred lines of rice. I performed a genome-wide association study (GWAS) in conjunction with five-fold GS cross-validation on a population of 363 elite breeding lines from the International Rice Research Institute's (IRRI) irrigated rice breeding program and herein report the GS results. The population was genotyped using a genotyping-by-sequencing protocol optimized for rice in the first part of this thesis. The training population, statistical method used to build the GS model, number of markers, and trait were varied to determine their effect on prediction accuracy. For all three traits, genomic prediction models outperformed prediction based on pedigree records alone. Furthermore, the most accurate GS models were those that incorporated fixed variables derived from genome-wide-association studies (GWAS) performed on rice model training data and by incorporating data from multiple environments. Two breeding schemas are then presented, including an extended, two-stream breeding design that can be used to efficiently integrate novel variation into elite breeding populations, expanding genetic diversity and enhancing the potential for sustainable productivity gains.