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Publication Detail

Title: Should we consider gene x environment interaction in the hunt for quantitative trait loci?

Authors: Gauderman, W J; Morrison, J L; Siegmund, K D

Published In Genet Epidemiol, (2001)

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.

PubMed ID: 11793788 Exiting the NIEHS site

MeSH Terms: Chromosome Mapping/statistics & numerical data*; Environmental Exposure/adverse effects*; Genetic Markers/genetics; Genetic Predisposition to Disease/genetics*; Genotype*; Humans; Lod Score; Models, Genetic*; Models, Statistical; Phenotype; Quantitative Trait, Heritable*; Research Support, U.S. Gov't, P.H.S.; Statistics, Nonparametric

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