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Epistasis

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Cover of 'Epistasis'

Table of Contents

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    Book Overview
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    Chapter 1 Long-Term Selection Experiments: Epistasis and the Response to Selection
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    Chapter 2 Finding the Epistasis Needles in the Genome-Wide Haystack
  4. Altmetric Badge
    Chapter 3 Biological Knowledge-Driven Analysis of Epistasis in Human GWAS with Application to Lipid Traits.
  5. Altmetric Badge
    Chapter 4 Epistasis for quantitative traits in Drosophila.
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    Chapter 5 Epistasis in the risk of human neuropsychiatric disease.
  7. Altmetric Badge
    Chapter 6 On the Partitioning of Genetic Variance with Epistasis
  8. Altmetric Badge
    Chapter 7 Measuring gene interactions.
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    Chapter 8 Two Rules for the Detection and Quantification of Epistasis and Other Interaction Effects
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    Chapter 9 Direct Approach to Modeling Epistasis
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    Chapter 10 Capacitating Epistasis—Detection and Role in the Genetic Architecture of Complex Traits
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    Chapter 11 Compositional Epistasis: An Epidemiologic Perspective
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    Chapter 12 Identification of Genome-Wide SNP-SNP and SNP-Clinical Boolean Interactions in Age-Related Macular Degeneration.
  14. Altmetric Badge
    Chapter 13 Epistasis Analysis Using Information Theory
  15. Altmetric Badge
    Chapter 14 Genome-wide epistasis and pleiotropy characterized by the bipartite human phenotype network.
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    Chapter 15 Network theory for data-driven epistasis networks.
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    Chapter 16 Epistasis analysis using multifactor dimensionality reduction.
  18. Altmetric Badge
    Chapter 17 Epistasis Analysis Using ReliefF
  19. Altmetric Badge
    Chapter 18 Epistasis analysis using artificial intelligence.
Attention for Chapter 4: Epistasis for quantitative traits in Drosophila.
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Chapter title
Epistasis for quantitative traits in Drosophila.
Chapter number 4
Book title
Epistasis
Published in
Methods in molecular biology, January 2015
DOI 10.1007/978-1-4939-2155-3_4
Pubmed ID
Book ISBNs
978-1-4939-2154-6, 978-1-4939-2155-3
Authors

Trudy F C Mackay, Trudy F. C. Mackay, Mackay, Trudy F. C.

Abstract

The role of gene-gene interactions in the genetic architecture of quantitative traits is controversial, despite the biological plausibility of nonlinear molecular interactions underpinning variation in quantitative traits. In strictly outbreeding populations, genetic architecture is inferred indirectly by estimating variance components; however, failure to detect epistatic variance does not mean lack of epistatic gene action and is even consistent with pervasive epistasis. In Drosophila, more focused approaches to detecting epistatic gene action are possible, based on the ability to create de novo mutations and perform crosses among them; to construct inbred lines, artificial selection lines, and chromosome substitution lines; to map quantitative trait loci affecting complex traits by linkage and association; and to evaluate effects of induced mutations on multiple wild-derived backgrounds. Here, I review evidence for epistasis in Drosophila from the application of these methods, and conclude that additivity is an emergent property of underlying epistatic gene action for Drosophila quantitative traits. Such studies can be used to infer novel, highly interconnected genetic networks that are enriched for gene ontology categories and metabolic and cellular pathways. The consequence of epistasis is that the main effects of each of the interacting loci depend on allele frequency, which negatively impacts the predictive ability of additive models. Finally, epistasis results in hidden quantitative genetic variation in natural populations (genetic canalization) and the potential for rapid evolution of Dobzhansky-Muller incompatibilities (speciation).

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 21 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 29%
Researcher 4 19%
Professor > Associate Professor 2 10%
Student > Master 1 5%
Other 1 5%
Other 2 10%
Unknown 5 24%
Readers by discipline Count As %
Agricultural and Biological Sciences 8 38%
Biochemistry, Genetics and Molecular Biology 6 29%
Pharmacology, Toxicology and Pharmaceutical Science 1 5%
Unknown 6 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 November 2014.
All research outputs
#19,971,836
of 24,542,484 outputs
Outputs from Methods in molecular biology
#8,644
of 13,811 outputs
Outputs of similar age
#267,189
of 362,759 outputs
Outputs of similar age from Methods in molecular biology
#506
of 992 outputs
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