title: Should we consider gene x environment interaction in the hunt for quantitative trait loci?
abstract: We address the question of whether one can obtain increased power for finding a quantitative trait locus (QTL) if a gene x environment (G x E) interaction is incorporated directly into the linkage analysis. We consider both parametric and nonparametric analysis approaches to including G x E interaction. For the former, we utilize joint segregation and linkage analysis to estimate simultaneously the recombination fraction and a G x E interaction effect, as well as the remaining model parameters. The nonparametric approach is based on an extension of the Haseman-Elston method applied to sib pairs to include a regression of the squared trait difference on marker-identity-by-descent (IBD) probability (pi), the sibling covariate sum (z), and pi x z. We utilize 50 replicates of the simulated data and compare empirical power of the various approaches to detect MG4, a locus that is involved in a strong interaction with age for Q4 and in a weaker interaction with environmental factor E2 for Q3. Using the parametric approach, including a G x age effect does increase power for detecting linkage between MG4 and Q4 compared with ignoring the interaction (powers 58% and 38%, respectively, to exceed a lod score of 3.0). On the other hand, including a G x E2 interaction has little effect on the power to detect linkage between MG4 and Q3. The nonparametric approach leads to qualitatively similar findings. We conclude that it is beneficial to incorporate G x E interaction into a linkage analysis, provided the interaction effect is of sufficiently strong magnitude.
authors.affiliation: Department of Preventive Medicine, University of Southern California, 1540 Alcazar Street, Suite 220, Los Angeles, CA 90033, USA.
authors.forename: W J
authors.initials: WJ
authors.lastname: Gauderman
authors.forename: J L
authors.initials: JL
authors.lastname: Morrison
authors.forename: K D
authors.initials: KD
authors.lastname: Siegmund
mesh_terms: D002874:Chromosome Mapping / Q000706:statistics & numerical data*; D004781:Environmental Exposure / Q000009:adverse effects*; D005819:Genetic Markers / Q000235:genetics; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D005838:Genotype; D006801:Humans; D008126:Lod Score; D008957:Models, Genetic; D015233:Models, Statistical; D010641:Phenotype; D019655:Quantitative Trait, Heritable; D018709:Statistics, Nonparametric
chemical_list: D005819:Genetic Markers