|Grant Number:||1R01CA169122-01A1 Interpret this number|
|Primary Investigator:||Wei, Peng|
|Organization:||University Of Texas Hlth Sci Ctr Houston|
|Project Title:||Genetic Susceptibility and Risk Model for Pancreatic Cancer|
DESCRIPTION (provided by applicant): 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.
Powerful Tukey's One Degree-of-Freedom Test for Detecting Gene-Gene and Gene-Environment Interactions.
Authors: Wang Y, Li D, Wei P
Source: Cancer Inform, 2015;14(Suppl 2), p. 209-18.
EPub date: 2015 Jun 4.
Genetic variants in DNA double-strand break repair genes and risk of salivary gland carcinoma: a case-control study.
Authors: Xu L, Tang H, El-Naggar AK, Wei P, Sturgis EM
Source: PLoS One, 2015;10(6), p. e0128753.
EPub date: 2015 Jun 2.
Testing for polygenic effects in genome-wide association studies.
Authors: Pan W, Chen YM, Wei P
Source: Genet Epidemiol, 2015 May;39(4), p. 306-16.
EPub date: 2015 Apr 6.
Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies.
Authors: Dong C, Wei P, Jian X, Gibbs R, Boerwinkle E, Wang K, Liu X
Source: Hum Mol Genet, 2015 Apr 15;24(8), p. 2125-37.
EPub date: 2014 Dec 30.
A family-based joint test for mean and variance heterogeneity for quantitative traits.
Authors: Cao Y, Maxwell TJ, Wei P
Source: Ann Hum Genet, 2015 Jan;79(1), p. 46-56.
EPub date: 2014 Nov 13.
Functional logistic regression approach to detecting gene by longitudinal environmental exposure interaction in a case-control study.
Authors: Wei P, Tang H, Li D
Source: Genet Epidemiol, 2014 Nov;38(7), p. 638-51.
EPub date: 2014 Sep 12.
A powerful and adaptive association test for rare variants.
Authors: Pan W, Kim J, Zhang Y, Shen X, Wei P
Source: Genetics, 2014 Aug;197(4), p. 1081-95.
EPub date: 2014 May 15.
A versatile omnibus test for detecting mean and variance heterogeneity.
Authors: Cao Y, Wei P, Bailey M, Kauwe JS, Maxwell TJ, Alzheimer's Disease Neuroimaging Initiative
Source: Genet Epidemiol, 2014 Jan;38(1), p. 51-9.
Axonal guidance signaling pathway interacting with smoking in modifying the risk of pancreatic cancer: a gene- and pathway-based interaction analysis of GWAS data.
Authors: Tang H, Wei P, Duell EJ, Risch HA, Olson SH, Bueno-de-Mesquita HB, Gallinger S, Holly EA, Petersen G, Bracci PM, McWilliams RR, Jenab M, Riboli E, Tjønneland A, Boutron-Ruault MC, Kaaks R, Trichopoulos D, Panico S, Sund M, Peeters PH, Khaw KT, Amos CI, Li D
Source: Carcinogenesis, 2014 May;35(5), p. 1039-45.
EPub date: 2014 Jan 13.
Genes-environment interactions in obesity- and diabetes-associated pancreatic cancer: a GWAS data analysis.
Authors: Tang H, Wei P, Duell EJ, Risch HA, Olson SH, Bueno-de-Mesquita HB, Gallinger S, Holly EA, Petersen GM, Bracci PM, McWilliams RR, Jenab M, Riboli E, Tjønneland A, Boutron-Ruault MC, Kaaks R, Trichopoulos D, Panico S, Sund M, Peeters PH, Khaw KT, Amos CI, Li D
Source: Cancer Epidemiol Biomarkers Prev, 2014 Jan;23(1), p. 98-106.
EPub date: 2013 Oct 17.