Epi2Loc: An R Package to Investigate Two-Locus Epistatic Models

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.

[1]  J. Rice,et al.  Two‐Locus models of disease , 1992, Genetic epidemiology.

[2]  Qiang Yang,et al.  Predictive rule inference for epistatic interaction detection in genome-wide association studies , 2010, Bioinform..

[3]  Qiuying Sha,et al.  Application of seventeen two-locus models in genome-wide association studies by two-stage strategy , 2009, BMC proceedings.

[4]  Joseph E. Powell,et al.  Detection and replication of epistasis influencing transcription in humans , 2014, Nature.

[5]  J. Cheverud,et al.  Epistasis and its contribution to genetic variance components. , 1995, Genetics.

[6]  W. Bateson.  Mendel's Principles of Heredity , 1910, Nature.

[7]  I. Borecki,et al.  Linkage analysis of complex traits using affected sibpairs: Effects of single‐locus approximations on estimates of the required sample size , 1997, Genetic epidemiology.

[8]  R. Fisher.  XV.—The Correlation between Relatives on the Supposition of Mendelian Inheritance. , 1919, Transactions of the Royal Society of Edinburgh.

[9]  D. Falconer,et al.  Introduction to Quantitative Genetics. , 1962 .

[10]  T. Reich,et al.  A perspective on epistasis: limits of models displaying no main effect. , 2002, American journal of human genetics.

[11]  Lin He,et al.  SHEsisEpi, a GPU-enhanced genome-wide SNP-SNP interaction scanning algorithm, efficiently reveals the risk genetic epistasis in bipolar disorder , 2010, Cell Research.

[12]  P. Donnelly,et al.  Genome-wide strategies for detecting multiple loci that influence complex diseases , 2005, Nature Genetics.

[13]  Debbie S. Yuster,et al.  A complete classification of epistatic two-locus models , 2006, BMC Genetics.

[14]  D. Falconer.  Introduction to quantitative genetics. 1. ed. , 1984 .

[15]  L. Penrose,et al.  THE CORRELATION BETWEEN RELATIVES ON THE SUPPOSITION OF MENDELIAN INHERITANCE , 2022 .

[16]  Jason H. Moore,et al.  Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions , 2003, Bioinform..

[17]  Z. Zeng,et al.  Modeling Quantitative Trait Loci and Interpretation of Models , 2005, Genetics.

[18]  Cheng Soon Ong,et al.  GWIS - model-free, fast and exhaustive search for epistatic interactions in case-control GWAS , 2013, BMC Genomics.

[19]  K. Mather,et al.  The Description of Genic Interactions in Continuous Variation , 1955 .

[20]  Manuel A. R. Ferreira,et al.  PLINK: a tool set for whole-genome association and population-based linkage analyses. , 2007, American journal of human genetics.

[21]  Qiang Yang,et al.  BOOST: A fast approach to detecting gene-gene interactions in genome-wide case-control studies , 2010, American journal of human genetics.

[22]  Masao Ueki,et al.  Improved Statistics for Genome-Wide Interaction Analysis , 2012, PLoS genetics.

[23]  David Heckerman,et al.  CORRIGENDUM: An Exhaustive Epistatic SNP Association Analysis on Expanded Wellcome Trust Data , 2013, Scientific Reports.

[24]  E. Lander,et al.  The mystery of missing heritability: Genetic interactions create phantom heritability , 2012, Proceedings of the National Academy of Sciences.

[25]  P. Lachenbruch.  Statistical Power Analysis for the Behavioral Sciences (2nd ed.) , 1989 .

[26]  Sara Marsal,et al.  Identification of a two-loci epistatic interaction associated with susceptibility to rheumatoid arthritis through reverse engineering and multifactor dimensionality reduction. , 2007, Genomics.

[27]  Wentian Li,et al.  A Complete Enumeration and Classification of Two-Locus Disease Models , 1999, Human Heredity.

[28]  H. Cordell.  Detecting gene–gene interactions that underlie human diseases , 2009, Nature Reviews Genetics.

[29]  V. Vieland,et al.  Two-locus heterogeneity cannot be distinguished from two-locus epistasis on the basis of affected-sib-pair data. , 2003, American journal of human genetics.

[30]  Jacob Cohen.  Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[31]  Okada Naoya,et al.  A model from this study. , 2013 .

[32]  O. Carlborg,et al.  A Unified Model for Functional and Statistical Epistasis and Its Application in Quantitative Trait Loci Analysis , 2007, Genetics.

[33]  O. Kempthorne,et al.  A Model for the Study of Quantitative Inheritance. , 1954, Genetics.

[34]  Tim Becker,et al.  INTERSNP: genome-wide interaction analysis guided by a priori information , 2009, Bioinform..