Epistasis (eBook, PDF)

Methods and Protocols
Redaktion: Moore, Jason H.; Williams, Scott M.

• Format: PDF

Produktbeschreibung

This volume presents a valuable and readily reproducible collection of established and emerging techniques on modern genetic analyses. Chapters focus on statistical or data mining analyses, genetic architecture, the burden of multiple testing, genetic variance, measuring epistasis, multifactor dimensionality reduction, and ReliefF. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and key tips on troubleshooting and avoiding known pitfalls.

Authoritative and practical, Epistasis: Methods and Protocols aids scientists in continuing to study elucidate epistasis in the context of modern data availability.

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Produktdetails

• Verlag: Springer New York
• Seitenzahl: 350
• Erscheinungstermin: 17. November 2014
• Englisch
• ISBN-13: 9781493921553
• Artikelnr.: 64700662

Inhaltsangabe

Long Term Selection Experiments: Epistasis and The Response To Selection.- Finding the Epistasis Needles in the Genome-Wide Haystack.- Biological Knowledge-Driven Analysis of Epistasis in Human GWAS with Application to Lipid Traits.- Epistasis for Quantitative Traits in Drosophila.- Epistasis In The Risk Of Human Neuropsychiatric Disease.- On the Partitioning of Genetic Variance with Epistasis.- Measuring Gene Interactions.- Two Rules for the Detection and Quantification of Epistasis and other Interaction Effects.- Direct Approach to Modeling Epistasis.- Capacitating Epistasis - Detection and Role in the Genetic Architecture of Complex Traits.- Compositional Epistasis: An Epidemiologic Perspective.- Identification Of Genome-Wide SNP-SNP and SNP-Clinical Boolean Interactions In Age-Related Macular Degeneration.- Epistasis Analysis Using Information Theory.- Genome-Wide Epistasis and Pleiotropy Characterized by the Bipartite Human.- Network Theory for Data-Driven Epistasis Networks.- Epistasis Analysis Using Multifactor Dimensionality Reduction.- Epistasis Analysis Using ReliefF.- Epistasis Analysis Using Artificial Intelligence.