This is the ROO activation of the funded K99 proposal 'Statistical Methods for Analysis of High-Dimensional Gene and Environment Data' (K99ES017744) by Dr. Arnab Maity, a statistically trained Assistant Professor in the Department of Statistics at North Carolina State University (NCSU). Dr. Maity is committed to a research career in the development of statistical methodology for the analysis of high-dimensional gene and environment data. This application includes updated specific aims and research plans for the ROO phase, a description of progress during the K99 phase, evaluation reports from the K99 phase mentors, and a letter from Dr. Peter Bloomfield, Interim Head of Department of Statistics, NCSU, detailing the institutional commitment and details for Dr. Maity's career development plan. The proposed research concerns two major aims: (1) analyzing DNA methylation in the human genome and developing statistical methodology to investigate its association to environmental exposure to heavy metals and air particles, and various markers of cardiovascular disease, and (2) developing robust and efficient statistical testing procedures for genetic and environmental effects in high-dimensional genome-wide association studies (GWAS) in the presence of gene-gene and gene-environment interactions and incorporating longitudinal measures of phenotypes. The applicant has readily available data sets on genome-wide DNA methylation study in the Normative Aging Study and the genome-wide association studies of Framingham Heart Study. The proposed methods will be applied to these data sets to draw valuable conclusions regarding the interplay of DNA methylation and other genetic variants, and environmental exposures in relation to susceptibility to cardiovascular disease. Career development for Dr. Maity and accomplishment of the research aims will be facilitated by excellent research and career supporting resources available within the Statistics department at NCSU and the University, participation in various gene and environment research projects with other researchers within NCSU and outside such as UNC School of Public Health, NIEHS and SAMSI, various scientific meetings and seminars, and the rich research community in NCSU and Research Triangle Area.
We will develop statistical methodology and software to analyze high-dimensional gene and environmental data and their interplay in relation to human health. We will identify genetic and environmental exposure factors that are associated with chronic diseases, such as heart disease, stroke, diabetes, and hypertension.
|Usset, Joseph; Staicu, Ana-Maria; Maity, Arnab (2016) Interaction Models for Functional Regression. Comput Stat Data Anal 94:317-329|
|Zhang, Hongmei; Maity, Arnab; Arshad, Hasan et al. (2016) Variable selection in semi-parametric models. Stat Methods Med Res 25:1736-52|
|Kong, Dehan; Maity, Arnab; Hsu, Fang-Chi et al. (2016) Testing and estimation in marker-set association study using semiparametric quantile regression kernel machine. Biometrics 72:364-71|
|Urrutia, Eugene; Lee, Seunggeun; Maity, Arnab et al. (2015) Rare variant testing across methods and thresholds using the multi-kernel sequence kernel association test (MK-SKAT). Stat Interface 8:495-505|
|Zhao, Ni; Bell, Douglas A; Maity, Arnab et al. (2015) Global analysis of methylation profiles from high resolution CpG data. Genet Epidemiol 39:53-64|
|Davenport, Clemontina A; Maity, Arnab; Wu, Yichao (2015) Parametrically guided estimation in nonparametric varying coefficient models with quasi-likelihood. J Nonparametr Stat 27:195-213|
|Wang, Zhi; Maity, Arnab; Luo, Yiwen et al. (2015) Complete effect-profile assessment in association studies with multiple genetic and multiple environmental factors. Genet Epidemiol 39:122-33|
|Maity, Arnab; Williams, Paige L; Ryan, Louise et al. (2014) Analysis of in vitro fertilization data with multiple outcomes using discrete time-to-event analysis. Stat Med 33:1738-49|
|Gertheiss, J; Maity, A; Staicu, A-M (2013) Variable Selection in Generalized Functional Linear Models. Stat 2:86-103|
|Sofer, Tamar; Baccarelli, Andrea; Cantone, Laura et al. (2013) Exposure to airborne particulate matter is associated with methylation pattern in the asthma pathway. Epigenomics 5:147-54|
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