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Titlebook: Genomic Prediction of Complex Traits; Methods and Protocol Nourollah Ahmadi,Jér?me Bartholomé Book 2022 The Editor(s) (if applicable) and T

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發(fā)表于 2025-3-21 19:17:52 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Genomic Prediction of Complex Traits
副標(biāo)題Methods and Protocol
編輯Nourollah Ahmadi,Jér?me Bartholomé
視頻videohttp://file.papertrans.cn/383/382902/382902.mp4
概述Includes cutting-edge methods and protocols.Provides step-by-step detail essential for reproducible results.Contains key notes and implementation advice from the experts
叢書名稱Methods in Molecular Biology
圖書封面Titlebook: Genomic Prediction of Complex Traits; Methods and Protocol Nourollah Ahmadi,Jér?me Bartholomé Book 2022 The Editor(s) (if applicable) and T
描述.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
出版日期Book 2022
關(guān)鍵詞trait architecture; optimal marker density; prediction methods; phenotypic variations; genomic predictio
版次1
doihttps://doi.org/10.1007/978-1-0716-2205-6
isbn_softcover978-1-0716-2207-0
isbn_ebook978-1-0716-2205-6Series ISSN 1064-3745 Series E-ISSN 1940-6029
issn_series 1064-3745
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Science+Busines
The information of publication is updating

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Genotyping, the Usefulness of Imputation to Increase SNP Density, and Imputation Methods and Tools,sity SNP panels and a limited set of reference individuals. Whatever the imputation method, the imputation accuracy, measured by the correct imputation rate or the correlation between true and imputed genotypes, increased with the increasing relatedness of the individual to be imputed with its dense
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,Genome and Environment?Based Prediction Models and Methods of Complex Traits Incorporating Genotypetion models for complex traits measured in continuous and noncontinuous (categorical) scale. Related to G?×?E interaction models this review also examine the analyses of the information generated with high-throughput phenotype data (phenomic) and the joint analyses of multitrait and multienvironment
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Accounting for Correlation Between Traits in Genomic Prediction,ian Ridge regression and best linear unbiased predictor, but also under a deep learning framework. The multitrait deep learning framework helps implement prediction models with mixed outcomes (continuous, binary, ordinal, and count, measured on different scales), which is not easy in conventional st
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Genomic Prediction of Complex Traits in Animal Breeding with Long Breeding History, the Dairy Cattlic ties between years. As genotyping costs decrease, the number of cows genotyped will continue to increase, and these records will become the basic data used to compute genomic evaluations, most likely via application of “single-step” methodologies. Less emphasis in selection goals will be placed o
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Genomic Prediction of Complex Traits in Forage Plants Species: Perennial Grasses Case,s of heritability. The main reasons are (1) the possibility to select single plants based on their genomic estimated breeding values (GEBV) for traits measured at sward level, (2) a reduction in the duration of selection cycles, and less importantly (3) an increase in the selection intensity associa
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