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Initial CDAS Request Approval
Sep 1, 2020
Multivariate gene-environment interaction analysis of renal cell carcinoma
It is well known that genetic and environmental risk factors play an important role in carcinogenesis. Previous large-scale genome-wide association studies (GWASs) have identified 13 susceptibility loci for renal cell carcinoma (RCC) in European ancestry, and epidemiological studies have also observed several risk factors for RCC, such as tobacco smoking, excess body weight etc. However, the interaction effect of genetic factors and environmental factors on RCC risk still remains unclear. We have got access to dbGaP dataset of the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial to perform a multivariate gene-environment interaction analysis. We will perform standard quality control and genome-wide imputation process for all participants included in our nested case-control study. Polygenic risk score (PRS) calculated from the weighted sum of the risk allele counts across 13 previously reported SNPs will be used to assess the genetic susceptibility of the participants. We will derive quartiles of the PRS variable based on the distribution among the controls. Logistic regression model will be used to estimate PRS quartiles, environmental factors and RCC risk adjusted for age, sex and significant eigenvectors. Multiplicative interactions are evaluated using a likelihood ratio test, and additive interactions are assessed using relative excess risk due to interaction (RERI) between PRS and environmental factors. Single SNP gene-environment interaction analysis will be used to identify whether the relationship between genetic susceptibility and environmental factors with RCC is driven by a locus.
The primary aim of this proposal is to investigate the interaction effects between genetic factors and various environmental factors on renal cell carcinoma risk.