@article{szatkiewicz_marceau_yilmaz_bulik_crowley_mattheisen_sullivan_lu_maity_tzeng_et al._2019, title={VARIANCE COMPONENT TEST FOR CROSS-DISORDER PATHWAY ANALYSIS}, volume={29}, ISSN={["1873-7862"]}, DOI={10.1016/j.euroneuro.2018.08.252}, journal={EUROPEAN NEUROPSYCHOPHARMACOLOGY}, author={Szatkiewicz, Jin and Marceau, Rachel and Yilmaz, Zeynep and Bulik, Cynthia and Crowley, James and Mattheisen, Manuel and Sullivan, Patrick and Lu, Wenbin and Maity, Arnab and Tzeng, Jung-Ying and et al.}, year={2019}, pages={1204–1205} }
 @article{marceau_lu_holloway_sale_worrall_williams_hsu_tzeng_2015, title={A Fast Multiple-Kernel Method With Applications to Detect Gene-Environment Interaction}, volume={39}, ISSN={["1098-2272"]}, DOI={10.1002/gepi.21909}, abstractNote={Kernel machine (KM) models are a powerful tool for exploring associations between sets of genetic variants and complex traits. Although most KM methods use a single kernel function to assess the marginal effect of a variable set, KM analyses involving multiple kernels have become increasingly popular. Multikernel analysis allows researchers to study more complex problems, such as assessing gene‐gene or gene‐environment interactions, incorporating variance‐component based methods for population substructure into rare‐variant association testing, and assessing the conditional effects of a variable set adjusting for other variable sets. The KM framework is robust, powerful, and provides efficient dimension reduction for multifactor analyses, but requires the estimation of high dimensional nuisance parameters. Traditional estimation techniques, including regularization and the “expectation‐maximization (EM)” algorithm, have a large computational cost and are not scalable to large sample sizes needed for rare variant analysis. Therefore, under the context of gene‐environment interaction, we propose a computationally efficient and statistically rigorous “fastKM” algorithm for multikernel analysis that is based on a low‐rank approximation to the nuisance effect kernel matrices. Our algorithm is applicable to various trait types (e.g., continuous, binary, and survival traits) and can be implemented using any existing single‐kernel analysis software. Through extensive simulation studies, we show that our algorithm has similar performance to an EM‐based KM approach for quantitative traits while running much faster. We also apply our method to the Vitamin Intervention for Stroke Prevention (VISP) clinical trial, examining gene‐by‐vitamin effects on recurrent stroke risk and gene‐by‐age effects on change in homocysteine level.}, number={6}, journal={GENETIC EPIDEMIOLOGY}, author={Marceau, Rachel and Lu, Wenbin and Holloway, Shannon and Sale, Michele M. and Worrall, Bradford B. and Williams, Stephen R. and Hsu, Fang-Chi and Tzeng, Jung-Ying}, year={2015}, month={Sep}, pages={456–468} }
 @article{zhao_marceau_zhang_tzeng_2015, title={Assessing gene-environment interactions for common and rare variants with binary traits using gene-trait similarity regression}, volume={199}, number={3}, journal={Genetics}, author={Zhao, G. L. and Marceau, R. and Zhang, D. W. and Tzeng, J. Y.}, year={2015}, pages={695-} }