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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Epistasis-Based Feature Selection Algorithm,ecomes an alternative to assess the gene (variable) interdependence and select the most significative variables. This chapter describes the Epistasis-based Feature Selection Algorithm (EbFSA). Such implementation was recently proposed in a doctorate thesis from Computer Science. It has been applied

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The Combined Analysis of Pleiotropy and Epistasis (CAPE),rectly is critical to understanding the genotype–phenotype map. However, inference of genetic interactions is challenging compared to inference of individual allele effects due to low statistical power. Furthermore, genetic interactions can appear inconsistent across different quantitative traits, p

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Two-Stage Testing for Epistasis: Screening and Verification,-wide scale, screening for epistatic effects among all possible pairs of genetic markers faces two main complications. Firstly, the classical statistical methods for modeling epistasis are computationally very expensive, which makes them impractical on such large scale. Secondly, straightforward cor

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Using Collaborative Mixed Models to Account for Imputation Uncertainty in Transcriptome-Wide Associs) to prioritize candidate target genes for complex traits. TWASs have become increasingly popular. They have been used to analyze many complex traits with expression profiles from different tissues, successfully enhancing the discovery of genetic risk loci for complex traits. Though conceptually st

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Protocol for Construction of Genome-Wide Epistatic SNP Networks Using WISH-R Package,ase outcome. Statistically and biologically, significant evidence of epistatic loci for several traits and diseases is well known in human, animals, and plants. However, there is no straightforward way to compute a large number of pairwise epistasis among millions of variants along the whole genome,

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Brief Survey on Machine Learning in Epistasis, gene, located far away on the chromosome or on an entirely different chromosome, and this interaction can have a strong effect on the function of the two genes. These changes then can alter the consequences of the biological processes, influencing the organism’s phenotype. Machine learning is an ar

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First-Order Correction of Statistical Significance for Screening Two-Way Epistatic Interactions,ed 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.