Literature DB >> 24983251

Epi2Loc: an R package to investigate two-locus epistatic models.

Raymond K Walters1, Charles Laurin1, Gitta H Lubke1.   

Abstract

Epistasis is a growing area of research in genome-wide studies, but the differences between alternative definitions of epistasis remain a source of confusion for many researchers. One problem is that models for epistasis are presented in a number of formats, some of which have difficult-to-interpret parameters. In addition, the relation between the different models is rarely explained. Existing software for testing epistatic interactions between single-nucleotide polymorphisms (SNPs) does not provide the flexibility to compare the available model parameterizations. For that reason we have developed an R package for investigating epistatic and penetrance models, Epi2Loc, to aid users who wish to easily compare, interpret, and utilize models for two-locus epistatic interactions. Epi2Loc facilitates research on SNP-SNP interactions by allowing the R user to easily convert between common parametric forms for two-locus interactions, generate data for simulation studies, and perform power analyses for the selected model with a continuous or dichotomous phenotype. The usefulness of the package for model interpretation and power analysis is illustrated using data on rheumatoid arthritis.

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Year:  2014        PMID: 24983251     DOI: 10.1017/thg.2014.38

Source DB:  PubMed          Journal:  Twin Res Hum Genet        ISSN: 1832-4274            Impact factor:   1.587


  2 in total

1.  A heuristic method for simulating open-data of arbitrary complexity that can be used to compare and evaluate machine learning methods.

Authors:  Jason H Moore; Maksim Shestov; Peter Schmitt; Randal S Olson
Journal:  Pac Symp Biocomput       Date:  2018

2.  Heuristic identification of biological architectures for simulating complex hierarchical genetic interactions.

Authors:  Jason H Moore; Ryan Amos; Jeff Kiralis; Peter C Andrews
Journal:  Genet Epidemiol       Date:  2014-11-13       Impact factor: 2.135

  2 in total

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