Detecting Interactions in Association Studies by Using Simple Allele Recoding

This paper aims to describe the benefits of using data recoding methods for the analysis of genetic interactions. By changing the representation of the input data it is possible to model non-additive genetic effects in association analysis software, which has been primarily designed to analyse only additive genetic effects. Similar treatment can be applied also for general-purpose statistical search algorithms available in general statistical packages. Data recoding is illustrated for several interaction models using hypothetical examples and by presenting gene-gene interaction analysis in a real cystic fibrosis dataset using the BAMA software.

[1]  David J Balding,et al.  Logistic regression protects against population structure in genetic association studies. , 2005, Genome research.

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

[3]  J. H. Moore,et al.  Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. , 2001, American journal of human genetics.

[4]  Larry Wasserman,et al.  Using linkage genome scans to improve power of association in genome scans. , 2006, American journal of human genetics.

[5]  Ritsert C. Jansen,et al.  Studying complex biological systems using multifactorial perturbation , 2003, Nature Reviews Genetics.

[6]  M. Sillanpää,et al.  Bayesian mapping of genotype × expression interactions in quantitative and qualitative traits , 2006, Heredity.

[7]  An extension of the ‘marker regression’ method to interactive QTL , 1998, Molecular Breeding.

[8]  Shizhong Xu,et al.  An Empirical Bayes Method for Estimating Epistatic Effects of Quantitative Trait Loci , 2007, Biometrics.

[9]  M. Sillanpää,et al.  Mapping Quantitative Trait Loci From a Single-Tail Sample of the Phenotype Distribution Including Survival Data , 2007, Genetics.

[10]  Mikko J Sillanpää,et al.  Bayesian Association-Based Fine Mapping in Small Chromosomal Segments , 2005, Genetics.

[11]  Comment on “On the Metropolis-Hastings Acceptance Probability to Add or Drop a Quantitative Trait Locus in Markov Chain Monte Carlo-Based Bayesian Analyses” , 2004, Genetics.

[12]  J. Ghosh,et al.  Modifying the Schwarz Bayesian Information Criterion to Locate Multiple Interacting Quantitative Trait Loci , 2004, Genetics.

[13]  N. Schork,et al.  Who's afraid of epistasis? , 1996, Nature Genetics.

[14]  Mikko J Sillanpää,et al.  Bayesian analysis of multilocus association in quantitative and qualitative traits , 2003, Genetic epidemiology.

[15]  Simon C. Potter,et al.  Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls , 2007, Nature.

[16]  Jason H. Moore,et al.  The Ubiquitous Nature of Epistasis in Determining Susceptibility to Common Human Diseases , 2003, Human Heredity.

[17]  Jun S. Liu,et al.  Bayesian inference of epistatic interactions in case-control studies , 2007, Nature Genetics.

[18]  R. Ball Quantifying Evidence for Candidate Gene Polymorphisms: Bayesian Analysis Combining Sequence-Specific and Quantitative Trait Loci Colocation Information , 2007, Genetics.

[19]  A. Zwinderman,et al.  Simultaneous estimation of gene‐gene and gene‐environment interactions for numerous loci using double penalized log–likelihood , 2006, Genetic epidemiology.

[20]  David Reich,et al.  Combining evidence of natural selection with association analysis increases power to detect malaria-resistance variants. , 2007, American journal of human genetics.

[21]  Todd Holden,et al.  A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility. , 2006, Journal of theoretical biology.

[22]  M. Sillanpää,et al.  Association Mapping of Complex Trait Loci With Context-Dependent Effects and Unknown Context Variable , 2006, Genetics.

[23]  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.

[24]  S. Xu,et al.  A penalized maximum likelihood method for estimating epistatic effects of QTL , 2005, Heredity.

[25]  R. Tibshirani Regression Shrinkage and Selection via the Lasso , 1996 .

[26]  Detection of multiple QTL with epistatic effects under a mixed inheritance model in an outbred population , 2003, Genetics Selection Evolution.

[27]  T. Hansen,et al.  A Bayesian Multilocus Association Method: Allowing for Higher-Order Interaction in Association Studies , 2007, Genetics.

[28]  K. Strauch,et al.  A Recoding Scheme for X-Linked and Pseudoautosomal Loci to Be Used with Computer Programs for Autosomal LOD-Score Analysis , 2004, Human Heredity.

[29]  D. Rubin INFERENCE AND MISSING DATA , 1975 .

[30]  P. Chaudhuri,et al.  Identification of polymorphic motifs using probabilistic search algorithms. , 2005, Genome research.

[31]  John Molitor,et al.  Application of Bayesian spatial statistical methods to analysis of haplotypes effects and gene mapping , 2003, Genetic epidemiology.

[32]  Jürgen Brockmöller,et al.  On the value of haplotype-based genotype-phenotype analysis and on data transformation in pharmacogenetics and -genomics. , 2007, Nature reviews. Genetics.

[33]  Heather J Cordell,et al.  Case/pseudocontrol analysis in genetic association studies: A unified framework for detection of genotype and haplotype associations, gene‐gene and gene‐environment interactions, and parent‐of‐origin effects , 2004, Genetic epidemiology.

[34]  R. Tibshirani,et al.  Least angle regression , 2004, math/0406456.

[35]  L. Tsui,et al.  Erratum: Identification of the Cystic Fibrosis Gene: Genetic Analysis , 1989, Science.

[36]  M. Sillanpää,et al.  Replication in genetic studies of complex traits , 2004, Annals of human genetics.

[37]  P. Sasieni From genotypes to genes: doubling the sample size. , 1997, Biometrics.