Download or read book Genomic Prediction of Complex Traits written by Nourollah Ahmadi and published by Springer Nature. This book was released on 2022-04-22 with total page 651 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Genomic Prediction of Complex Traits written by Nourollah Ahmadi and published by Humana. This book was released on 2023-05-07 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Neural Networks in Finance and Investing written by Robert R. Trippi and published by Irwin Professional Publishing. This book was released on 1996 with total page 872 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Genome Wide Association Studies and Genomic Prediction written by Cedric Gondro and published by Humana Press. This book was released on 2013-06-12 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Bayesian Methods in Structural Bioinformatics written by Thomas Hamelryck and published by Springer. This book was released on 2012-03-23 with total page 399 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is an edited volume, the goal of which is to provide an overview of the current state-of-the-art in statistical methods applied to problems in structural bioinformatics (and in particular protein structure prediction, simulation, experimental structure determination and analysis). It focuses on statistical methods that have a clear interpretation in the framework of statistical physics, rather than ad hoc, black box methods based on neural networks or support vector machines. In addition, the emphasis is on methods that deal with biomolecular structure in atomic detail. The book is highly accessible, and only assumes background knowledge on protein structure, with a minimum of mathematical knowledge. Therefore, the book includes introductory chapters that contain a solid introduction to key topics such as Bayesian statistics and concepts in machine learning and statistical physics.

Download or read book Whole genome Prediction of Complex Traits Using Kernel Methods written by and published by . This book was released on 2014 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Prediction of genetic values has been a focus of quantitative genetics since the beginning of the 20th century, with renewed interest following the advent of the era of whole genome-enabled prediction. Opportunities offered by the emergence of high-dimensional genomic data fuelled by post-Sanger sequencing technologies and especially molecular markers, have driven researchers to extend Fisher's infinitesimal model to confront newly arising challenges. In particular, kernel methods are gaining attention as the regression method of choice for genome-enabled prediction. Complex traits are presumably influenced by genomic regions working in concert with others, thus generating interactions. Motivated by this fact, a growing number of statistical approaches based on kernels attempt to capture non-additive effects, either parametrically or non-parametrically. This thesis centers on whole-genome regression using kernel methods applied to a wide range of quantitative traits of agricultural importance in animals and plants. We investigated various kernel-based approaches tailored to capturing total genetic variation, to arrive at an enhanced predictive performance of complex traits in the light of available genome annotation information. In particular, this thesis reports on three studies conducted using kernel methods. In the first study using dairy cattle and wheat data, we constructed a diffusion kernel and compared its predictive performance against that of a Gaussian kernel. The second study evaluated some parametric and nonparametric kernels for predicting pre-corrected phenotypes and progeny tests in dairy cow health traits. The third study partitioned SNPs based on annotation and examined sources of predictive performance of complex traits in broiler chickens. Overall, while we obtained some encouraging results with non-parametric kernels, recovering non-additive genetic variation in a validation dataset still remains an ongoing challenge in quantitative genetics.

Download or read book Data Analysis Using Hierarchical Generalized Linear Models with R written by Youngjo Lee and published by CRC Press. This book was released on 2017-07-06 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since their introduction, hierarchical generalized linear models (HGLMs) have proven useful in various fields by allowing random effects in regression models. Interest in the topic has grown, and various practical analytical tools have been developed. This book summarizes developments within the field and, using data examples, illustrates how to analyse various kinds of data using R. It provides a likelihood approach to advanced statistical modelling including generalized linear models with random effects, survival analysis and frailty models, multivariate HGLMs, factor and structural equation models, robust modelling of random effects, models including penalty and variable selection and hypothesis testing. This example-driven book is aimed primarily at researchers and graduate students, who wish to perform data modelling beyond the frequentist framework, and especially for those searching for a bridge between Bayesian and frequentist statistics.

Download or read book PREDICTION OF COMPLEX TRAITS USING GENOMIC DATA written by GUSTAVO. DE LOS CAMPOS and published by . This book was released on 2023 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

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

Download or read book Linear Models for the Prediction of Animal Breeding Values written by R. A. Mrode and published by Cab International. This book was released on 2014 with total page 343 pages. Available in PDF, EPUB and Kindle. Book excerpt: The prediction of producing desirable traits in offspring such as increased growth rate or superior meat, milk and wool production is a vital economic tool to the animal scientist. Summarizing the latest developments in genomics relating to animal breeding values and design of breeding programs, this new edition includes models of survival analysis, social interaction and sire and dam models, as well as advancements in the use of SNPs in the computation of genomic breeding values.

Download or read book From Agriculture Genome to Phenome Genome Wide Association Prediction and Selection written by Kefei Chen and published by Frontiers Media SA. This book was released on 2023-12-11 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Genetic Data Analysis for Plant and Animal Breeding written by Fikret Isik and published by Springer. This book was released on 2017-09-09 with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Wheat Landraces written by Nusret Zencirci and published by Springer Nature. This book was released on 2021-09-15 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: Landraces possess a very large genetic base in population structure and are dynamic populations of cultivated plants with historical origin, distinct identity, and without any formal crop improvement. They are often genetically diverse, locally adapted, and associated with traditional farming systems. Resistance genes to biotic and abiotic stress factors, which are especially diversified in landraces, are of great interest to plant breeders, faced with global climate challenge. In addition, gene pools made of different landraces grown in different ecological conditions can be used for wheat breeding to enhance quality; yield and other desirable agricultural parameters. An estimated 75% of the genetic diversity of crop plants was lost in the last century due to the replacement of high yielding modern varieties. There is, thus, an urgent need to preserve existing species, not only for posterity but also as a means to secure food supply for a rising world population. In this book, we provide an overview of wheat landraces with special attention to genetic diversities, conservation, and utilization.

Download or read book Forest Genomics and Biotechnology written by Isabel Allona and published by Frontiers Media SA. This book was released on 2019-11-27 with total page 185 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Research Topic addresses research in genomics and biotechnology to improve the growth and quality of forest trees for wood, pulp, biorefineries and carbon capture. Forests are the world’s greatest repository of terrestrial biomass and biodiversity. Forests serve critical ecological services, supporting the preservation of fauna and flora, and water resources. Planted forests also offer a renewable source of timber, for pulp and paper production, and the biorefinery. Despite their fundamental role for society, thousands of hectares of forests are lost annually due to deforestation, pests, pathogens and urban development. As a consequence, there is an increasing need to develop trees that are more productive under lower inputs, while understanding how they adapt to the environment and respond to biotic and abiotic stress. Forest genomics and biotechnology, disciplines that study the genetic composition of trees and the methods required to modify them, began over a quarter of a century ago with the development of the first genetic maps and establishment of early methods of genetic transformation. Since then, genomics and biotechnology have impacted all research areas of forestry. Genome analyses of tree populations have uncovered genes involved in adaptation and response to biotic and abiotic stress. Genes that regulate growth and development have been identified, and in many cases their mechanisms of action have been described. Genetic transformation is now widely used to understand the roles of genes and to develop germplasm that is more suitable for commercial tree plantations. However, in contrast to many annual crops that have benefited from centuries of domestication and extensive genomic and biotechnology research, in forestry the field is still in its infancy. Thus, tremendous opportunities remain unexplored. This Research Topic aims to briefly summarize recent findings, to discuss long-term goals and to think ahead about future developments and how this can be applied to improve growth and quality of forest trees.

Download or read book Introduction to Modern Information Retrieval written by Gerard Salton and published by New York ; Montreal : McGraw-Hill. This book was released on 1983 with total page 470 pages. Available in PDF, EPUB and Kindle. Book excerpt: Examines Concepts, Functions & Processes of Information Retrieval Systems

Download or read book Multivariate Statistical Machine Learning Methods for Genomic Prediction written by Osval Antonio Montesinos López and published by Springer Nature. This book was released on 2022-02-14 with total page 707 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension.The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.

Download or read book Genomic Selection in Plants written by Ani A. Elias and published by CRC Press. This book was released on 2022-08-18 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genomic selection (GS) is a promising tool in the field of breeding especially in the era where genomic data is becoming cheaper. The potential of this tool has not been realized due to its limited adaptation in various crops. Marker Assisted Selection (MAS) has been the method of choice for plant breeders while using the genomic information in the breeding pipeline. MAS, however, fails to capture vital minor gene effects while focusing only on the major genes, which is not ideal for breeding advancement especially for quantitative traits such as yield. The main aim of statistical methodologies coming under the umbrella of GS on using the whole genome information is to predict potential candidates for breeding advancement while optimizing the use of resources such as land, manpower, and most importantly time. Lack of proper understanding of the methods and their applications is one of the reasons why breeders shy away from this tool. The book is meant for biologists, especially breeders, and provides a comprehensive idea of the statistical methodologies used in GS, guidance on the choice of models, and design of datasets. The book also encourages the readers to adopt GS by demonstrating the current scenarios of these models in some of the important crops among oilseeds, vegetables, legumes, tuber crops, and cereals. For ease of implementation of GS, the book also provides hands-on scripts on GS data design and modeling in a popular open-source statistical program. Additionally, prospective in GS model development and thereby enhancement in crop improvement programs is discussed.