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Multivariate Detection of Gene‐Gene Interactions
Author(s) -
Rajapakse Indika,
Perlman Michael D.,
Martin Paul J,
Hansen John A.,
Kooperberg Charles
Publication year - 2012
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.21656
Subject(s) - biology , linkage disequilibrium , single nucleotide polymorphism , genetics , locus (genetics) , univariate , gene , logistic regression , disease , phenotype , multivariate statistics , computational biology , genotype , medicine , statistics , mathematics
Unraveling the nature of genetic interactions is crucial to obtaining a more complete picture of complex diseases. It is thought that gene‐gene interactions play an important role in the etiology of cancer, cardiovascular, and immune‐mediated disease. Interactions among genes are defined as phenotypic effects that differ from those observed for independent contributions of each gene, usually detected by univariate logistic regression methods. Using a multivariate extension of linkage disequilibrium (LD), we have developed a new method, based on distances between sample covariance matrices for groups of single nucleotide polymorphisms (SNPs), to test for interaction effects of two groups of genes associated with a disease phenotype. Since a disease‐associated interacting locus will often be in LD with more than one marker in the region, a method that examines a set of markers in a region collectively can offer greater power than traditional methods. Our method effectively identifies interaction effects in simulated data, as well as in data on the genetic contributions to the risk for graft‐versus‐host disease following hematopoietic stem cell transplantation.