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Reconstructability Analysis of Epistasis
Author(s) -
Zwick Martin
Publication year - 2011
Publication title -
annals of human genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.537
H-Index - 77
eISSN - 1469-1809
pISSN - 0003-4800
DOI - 10.1111/j.1469-1809.2010.00628.x
Subject(s) - epistasis , computer science , computational biology , biology , artificial intelligence , machine learning , genetics , gene
Summary The literature on epistasis describes various methods to detect epistatic interactions and to classify different types of epistasis. Reconstructability analysis (RA) has recently been used to detect epistasis in genomic data. This paper shows that RA offers a classification of types of epistasis at three levels of resolution (variable‐based models without loops, variable‐based models with loops, state‐based models). These types can be defined by the simplest RA structures that model the data without information loss; a more detailed classification can be defined by the information content of multiple candidate structures. The RA classification can be augmented with structures from related graphical modeling approaches. RA can analyze epistatic interactions involving an arbitrary number of genes or SNPs and constitutes a flexible and effective methodology for genomic analysis.