Pancreatic cancer (PanC) is the fourth leading cause of cancer-related death for both men and women in the U.S. Better understanding of the etiology and developing risk prediction models for early detection and prevention are urgently needed for this rapidly fatal disease. The majority of PanC are caused by the interplay of both genetic and environmental factors. Known risk factors for PanC include cigarette smoking, obesity, long-term type II diabetes, and family history. In addition, our previous case-control study has shown that excess body mass index (BMI) in young adulthood confers a higher risk of PanC than weight gain at later age. Recent genome-wide association studies (GWAS) have identified several chromosomal regions and genes in association with risk of PanC (PanScan). Our pathway analyses of the PanScan GWAS data have uncovered several novel biological pathways associated with the risk for PanC. However, it remains unknown how environmental or host risk factors modify the association between genetic factors and the PanC risk, which knowledge is critical to better understanding of the etiology and developing a risk prediction model and early intervention strategies for PanC. The goal of this project is to identify gene-environment interactions and develop and validate a risk prediction model including both common and rare genetic variants using the PanScan GWAS data and the exposure information of over 2,200 case-control pairs and an ongoing ExomeChip-based study of PanC genotyping both common SNPs and >240,000 rare functional exonic variants in over 4,100 cases and 4,700 controls from six case-control studies in the Pancreatic Cancer Case Control Consortium (PanC4) and a nested case-control study from Europe (EPIC). We will validate the absolute risk prediction model in two large prospective cohorts: the Atherosclerosis Risk in Communities (ARIC) cohort of 15,000 individuals and the Kaiser Permanente cohort of 100,000 individuals. We will also develop novel statistical methods to identify genes modifying the association between changing BMI at different age periods and PanC risk using the unique dataset from a case-control study of PanC conducted at MD Anderson Cancer Center. Our proposed project hinges on novel integration of GWAS, ExomeChip, exposure data of a large number of PanC cases and controls, recently developed powerful statistical methods and analysis strategies for detecting genome-wide gene/pathway-environment interactions and polygenic approaches to genetic risk prediction. The work proposed here is expected not only to advance our understanding of the etiology of PanC and delineate how genes and lifestyle or host factors modify the risk of PanC, but also to greatly facilitate identification of high-risk individuals, and thus, contribute to early detection, improved survival and prevention of PanC. The novel statistical methods developed here are also applicable to other cancers and complex disease, and we will develop user-friendly software packages for public use.
Over 43,900 new cases of pancreatic cancer are expected to occur in the US in 2012, almost all rapidly fatal. This project will not only advance our understanding of the etiology of pancreatic cancer and delineate how genes and lifestyle or host factors modify the risk of pancreatic cancer, but also will facilitate identification of high-risk individuals, and thus, contribute to early detection, prevention, and improved survival of pancreatic cancer. The novel statistical methods developed here are also applicable to other cancers and complex disease, and we will develop user-friendly software packages for public use.
|Cao, Ying; Rajan, Suja S; Wei, Peng (2016) Mendelian randomization analysis of a time-varying exposure for binary disease outcomes using functional data analysis methods. Genet Epidemiol 40:744-755|
|Wei, Peng; Cao, Ying; Zhang, Yiwei et al. (2016) On Robust Association Testing for Quantitative Traits and Rare Variants. G3 (Bethesda) 6:3941-3950|
|Kim, Taebeom; Wei, Peng (2016) Incorporating ENCODE information into association analysis of whole genome sequencing data. BMC Proc 10:257-261|
|Hong, Chuan; Ning, Yang; Wei, Peng et al. (2016) A semiparametric model for vQTL mapping. Biometrics :|
|Wei, Peng; Yang, Yang; Guo, Xinjian et al. (2016) Identification of an Association of TNFAIP3 Polymorphisms With Matrix Metalloproteinase Expression in Fibroblasts in an Integrative Study of Systemic Sclerosis-Associated Genetic and Environmental Factors. Arthritis Rheumatol 68:749-60|
|Pan, Wei; Chen, Yue-Ming; Wei, Peng (2015) Testing for polygenic effects in genome-wide association studies. Genet Epidemiol 39:306-16|
|Pan, Wei; Kwak, Il-Youp; Wei, Peng (2015) A Powerful Pathway-Based Adaptive Test for Genetic Association with Common or Rare Variants. Am J Hum Genet 97:86-98|
|Wang, Yaping; Li, Donghui; Wei, Peng (2015) Powerful Tukey's One Degree-of-Freedom Test for Detecting Gene-Gene and Gene-Environment Interactions. Cancer Inform 14:209-18|
|Xu, Li; Tang, Hongwei; El-Naggar, Adel K et al. (2015) Genetic variants in DNA double-strand break repair genes and risk of salivary gland carcinoma: a case-control study. PLoS One 10:e0128753|
|Dong, Chengliang; Wei, Peng; Jian, Xueqiu et al. (2015) Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies. Hum Mol Genet 24:2125-37|
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