Titlebook: Epistasis; Methods and Protocol Jason H. Moore,Scott M. Williams Book 2015 Springer Science+Business Media New York 2015 Modern genetic ana

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Biological Knowledge-Driven Analysis of Epistasis in Human GWAS with Application to Lipid Traits,udies (GWAS), mainly due to low power associated with such interaction tests. In this chapter, we integrate biological knowledge and human GWAS data to reveal epistatic interactions underlying quantitative lipid traits, which are major risk factors for coronary artery disease. To increase power to d

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Epistasis in the Risk of Human Neuropsychiatric Disease,ther tissue. In this chapter, two well-studied neuropsychiatric diseases are examined, Alzheimer’s disease (AD) and schizophrenia, which have been shown to have multiple and, often, replicated interactions that associate with clinical endpoints or related phenotypes. In each case, a single gene is r

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Measuring Gene Interactions,s epistatic gene interactions from a measurement-theoretical perspective and argue that the standard measurements of epistasis in classical quantitative genetics have failed to capture aspects of epistasis that are relevant to selection dynamics and adaptation. Instead, the use of statistically moti

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Two Rules for the Detection and Quantification of Epistasis and Other Interaction Effects, a deviation from the additive mode of combining main effects of gene substitutions. Furthermore, the definition is relative to a particular scale and epistasis can potentially be eliminated by a non-linear transformation of the underlying phenotype variables. The latter fact raises questions of the

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Direct Approach to Modeling Epistasis,or genes (quantitative trait loci or QTLs) to select individuals with superior performance and qualities. QTL or genetic effects, including epistatic effects, can be defined at the genotypic (functional) and gene (statistical) levels. In the past, the functional or statistical genetic effects have b

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