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Titlebook: Genetic Data Analysis for Plant and Animal Breeding; Fikret Isik,James Holland,Christian Maltecca Book 2017 Springer International Publish

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發(fā)表于 2025-3-21 19:46:36 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Genetic Data Analysis for Plant and Animal Breeding
編輯Fikret Isik,James Holland,Christian Maltecca
視頻videohttp://file.papertrans.cn/383/382474/382474.mp4
概述Step-by-step data analysis examples for readers to learn quickly and apply in their own research.The first ‘how to‘ book on analyzing genomic data for plant and animal breeding.Fills the gap between t
圖書(shū)封面Titlebook: Genetic Data Analysis for Plant and Animal Breeding;  Fikret Isik,James Holland,Christian Maltecca Book 2017 Springer International Publish
描述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
出版日期Book 2017
關(guān)鍵詞Associate genetics; Genomic selection; Mixed models; Plant and animal breeding; Quantitative genetics
版次1
doihttps://doi.org/10.1007/978-3-319-55177-7
isbn_softcover978-3-319-85586-8
isbn_ebook978-3-319-55177-7
copyrightSpringer International Publishing AG 2017
The information of publication is updating

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Tapan K. Sengupta,Swagata Bhaumik familiar with traditional analysis of variance (ANOVA) based on ordinary least squares methods, we first will review the ANOVA and compare ANOVA to mixed models analysis to help introduce this topic. We will show that under certain conditions, results from ANOVA and mixed models analysis are largel
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DOGMA 2003. Report from Denmarkn particular, ASReml makes use of a notation for direct products of matrices to form some complex variance structures. The direct product notation can be applied both to the residual errors from the model (in the ‘. structure’) and to random model factors (in the ‘. structure’). In this chapter we i
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,Befehle der Befehls-Oberfl?che,n plant and animal breeding programs. When traits are correlated, breeding value predictions from a multivariate model can be more accurate than univariate models. In this chapter we introduce multivariate models for two data sets: a maize inbred line multi-environment trial and pig data with pedigr
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https://doi.org/10.1007/978-3-8349-6242-3onmental conditions to which a cultivar might be exposed. Multi-environment trials provide information about the adaptability of genotypes to specific environments or to sets of environments. The variance-covariance structures introduced in preceding chapters can be used to model genotype-by-environ
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