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Genomic Prediction of Complex Traits - Methods and Protocols (1st ed. 2022)
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Genomic Prediction of Complex Traits - Methods and Protocols (1st ed. 2022)
Series: Methods in Molecular Biology, 2467
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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.
General
Imprint: |
Springer-Verlag New York
|
Country of origin: |
United States |
Series: |
Methods in Molecular Biology, 2467 |
Release date: |
April 2023 |
Firstpublished: |
2022 |
Editors: |
Nourollah Ahmadi
• Jérôme Bartholomé
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Dimensions: |
254 x 178mm (L x W) |
Pages: |
648 |
Edition: |
1st ed. 2022 |
ISBN-13: |
978-1-07-162207-0 |
Categories: |
Books
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LSN: |
1-07-162207-2 |
Barcode: |
9781071622070 |
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