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Titlebook: Epistasis; Methods and Protocol Ka-Chun Wong Book 2021 Springer Science+Business Media, LLC, part of Springer Nature 2021 Synthetic genetic

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书目名称Epistasis
副标题Methods and Protocol
编辑Ka-Chun Wong
视频video
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
丛书名称Methods in Molecular Biology
图书封面Titlebook: Epistasis; Methods and Protocol Ka-Chun Wong Book 2021 Springer Science+Business Media, LLC, part of Springer Nature 2021 Synthetic genetic
描述.This volume explores methods and protocols for detecting epistasis from genetic data. Chapters provide methods and protocols demonstrating approaches to identify epistasis, genetic epistasis testing, genome-wide epistatic SNP networks, epistasis detection through machine learning, and complex interaction analysis using trigenic synthetic genetic array (τ-SGA). Written in the highly successful .Methods in Molecular Biology. series format, chapters include introductions to their respective topics, application details for both the expert and non-expert reader, and tips on troubleshooting and avoiding known pitfalls... ..Authoritative and cutting-edge, .Epistasis: Methods and Protocols .aims to ensure successful results in the further study of this vital field... ."Simulating Evolution in Asexual Populations with Epistasis” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com..
出版日期Book 2021
关键词Synthetic genetic arrays; GWAS; eQTL Mapping; Bioinformatics; translational medicine
版次1
doihttps://doi.org/10.1007/978-1-0716-0947-7
isbn_softcover978-1-0716-0949-1
isbn_ebook978-1-0716-0947-7Series ISSN 1064-3745 Series E-ISSN 1940-6029
issn_series 1064-3745
copyrightSpringer Science+Business Media, LLC, part of Springer Nature 2021
The information of publication is updating

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Epistasis978-1-0716-0947-7Series ISSN 1064-3745 Series E-ISSN 1940-6029
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https://doi.org/10.1007/978-1-4614-6163-0ed with a phenotype, the nominal statistical evidence will be inflated. Corrections are available but computationally expensive for genome-wide studies. We provide a first-order correction that can be applied in practice with essentially no additional computational cost.
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