title: Comparison of the power of haplotype-based versus single- and multilocus association methods for gene x environment (gene x sex) interactions and application to gene x smoking and gene x sex interactions in rheumatoid arthritis.
abstract: Accounting for interactions with environmental factors in association studies may improve the power to detect genetic effects and may help identifying important environmental effect modifiers. The power of unphased genotype-versus haplotype-based methods in regions with high linkage disequilibrium (LD), as measured by D', for analyzing gene x environment (gene x sex) interactions was compared using the Genetic Analysis Workshop 15 (GAW15) simulated data on rheumatoid arthritis with prior knowledge of the answers. Stepwise and regular conditional logistic regression (CLR) was performed using a matched case-control sample for a HLA region interacting with sex. Haplotype-based analyses were performed using a haplotype-sharing-based Mantel statistic and a test for haplotype-trait association in a general linear model framework. A step-down minP algorithm was applied to derive adjusted p-values and to allow for power comparisons. These methods were also applied to the GAW15 real data set for PTPN22.For markers in strong LD, stepwise CLR performed poorly because of the correlation/collinearity between the predictors in the model. The power was high for detecting genetic main effects using simple CLR models and haplotype-based methods and for detecting joint effects using CLR and Mantel statistics. Only the haplotype-trait association test had high power to detect the gene x sex interaction.In the PTPN22 region with markers characterized by strong LD, all methods indicated a significant genotype x sex interaction in a sample of about 1000 subjects. The previously reported R620W single-nucleotide polymorphism was identified using logistic regression, but the haplotype-based methods did not provide any precise location information.
authors.affiliation: Institute of Medical Biometry and Epidemiology, Philipps-University Marburg, 35037 Marburg, Germany.
authors.forename: Astrid
authors.initials: A
authors.lastname: Dempfle
authors.affiliation: Division of Cancer Epidemiology, German Cancer Research Center DKFZ, 69120 Heidelberg, Germany.
authors.forename: Rebecca
authors.initials: R
authors.lastname: Hein
authors.affiliation: Division of Cancer Epidemiology, German Cancer Research Center DKFZ, 69120 Heidelberg, Germany.
authors.forename: Lars
authors.initials: L
authors.lastname: Beckmann
authors.affiliation: Institute of Medical Biometry and Epidemiology, Philipps-University Marburg, 35037 Marburg, Germany.|Institute of Medical Informatics, Biometry and Epidemiology, University of Duisburg-Essen, 45122 Essen, Germany.
authors.forename: André
authors.initials: A
authors.lastname: Scherag
authors.affiliation: Institute of Medical Biometry and Epidemiology, Philipps-University Marburg, 35037 Marburg, Germany.
authors.forename: Thuy Trang
authors.initials: TT
authors.lastname: Nguyen
authors.affiliation: Institute of Medical Biometry and Epidemiology, Philipps-University Marburg, 35037 Marburg, Germany.
authors.forename: Helmut
authors.initials: H
authors.lastname: Schäfer
authors.affiliation: Division of Cancer Epidemiology, German Cancer Research Center DKFZ, 69120 Heidelberg, Germany.
authors.forename: Jenny
authors.initials: J
authors.lastname: Chang-Claude