Gliomas account for 80% of all malignant brain tumors and produce enormous clinical and public health burden due to their poor prognosis. Given the lack of known causes for glioma, understanding genetic susceptibility might provide new insights and opportunities for progress in unraveling the biological mechanisms behind this fatal cancer. Genome-wide association studies (GWAS) constitute a popular approach for investigating the association of single nucleotide polymorphisms (SNPs) with disease. On the other hand, studies examining how genetic variants modify gene expression in tissue (i.e., quantitative trait loci (eQTL) studies) focus on the molecular quantitative trait. As the traditionl GWAS analysis is subject to power loss due to its agnostic approach, new strategies are required to identify additional and scientifically meaningful susceptibility loci of glioma risk. Hre we propose to integrate eQTL studies to more powerfully test the SNP effect on disease in GWAS when eQTL studies and GWAS are conducted among different subjects. With a regression model for the joint effect of SNPs and gene expression on disease risk, we have developed an efficient testing procedure for the overall effect of an eQTL SNP set in a gene or a pathway and illustrated its utility in numerical simulation studies and an asthma study. We will pursue the integrated analysis with three specific study aims.
In Aim 1, we will conduct eQTL analyses using genome-wide SNP and expression data collected from brain tissue obtained from neurologically normal subjects and glioma patients.
In Aim 2, we will first form eQTL SNP sets in a gene or an immunomodulating pathway identified from Aim 1 and then conduct eQTL SNP-set analyses to investigate the association of eQTL SNPs with the risk of glioma using the publicly available GWAS data of the glioma risk.
In Aim 3, we will perform integrated analyses of the SNP-set of a gene identified in Aim 2 and its expression value to assess the gene-based effect on the risk of glioma, either through a direct effect of eQTL SNPs or an indirect effect mediated by gene expression. The goal of this project is to build a new framework of conducting and analyzing a GWAS and identify new susceptibility loci for glioma. As different genomic data (i.e., SNPs and gene expression) are integrated in the analysis, we would expect a more statistical power to detect the disease-driving susceptibility loci than the SNP-only analysis. Furthermore, the results from our eQTL-integrated approach will also be more biologically meaningful and interpretable than the conventional agnostic GWAS because eQTL SNPs are more likely to be functional and the eQTL effect on the disease risk is explicitly modeled.

Public Health Relevance

We propose a new integrative approach to investigate the role of genetic susceptibility in risk of glioma. Our approach integrates data examining the association between genetic variants and gene expression in brain tissue with data from genetic variants and risk of glioma. The goal of this study is to identify new areas of the genome that may be associated with risk of glioma and may provide new clues into the etiology of this fatal disease.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Small Research Grants (R03)
Project #
Application #
Study Section
Special Emphasis Panel (ZCA1)
Program Officer
Carrick, Danielle M
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Brown University
Public Health & Prev Medicine
Schools of Public Health
United States
Zip Code
Shih, Stephannie; Huang, Yen-Tsung; Yang, Hwai-I (2018) A multiple mediator analysis approach to quantify the effects of the ADH1B and ALDH2 genes on hepatocellular carcinoma risk. Genet Epidemiol 42:394-404
Wang, Shunping; Zhang, Yi; Mensah, Virginia et al. (2018) Discordant anti-müllerian hormone (AMH) and follicle stimulating hormone (FSH) among women undergoing in vitro fertilization (IVF): which one is the better predictor for live birth? J Ovarian Res 11:60
Huang, Yen-Tsung; Zhang, Yi; Wu, Zhijin et al. (2017) Genotype-based gene signature of glioma risk. Neuro Oncol 19:940-950
Huang, Yen-Tsung; Yang, Hwai-I (2017) Causal Mediation Analysis of Survival Outcome with Multiple Mediators. Epidemiology 28:370-378
Huang, Yen-Tsung; Pan, Wen-Chi (2016) Hypothesis test of mediation effect in causal mediation model with high-dimensional continuous mediators. Biometrics 72:402-13
Huang, Yen-Tsung; Freeman, Joshua R; Yang, Hwai-I et al. (2016) Mediation effect of hepatitis B and C on mortality. Eur J Epidemiol 31:625-33
Huang, Yen-Tsung; Yang, Hwai-I; Liu, Jessica et al. (2016) Mediation Analysis of Hepatitis B and C in Relation to Hepatocellular Carcinoma Risk. Epidemiology 27:14-20
Huang, Yen-Tsung; Cai, Tianxi; Kim, Eunhee (2016) Integrative genomic testing of cancer survival using semiparametric linear transformation models. Stat Med 35:2831-44
Huang, Yen-Tsung; Chu, Su; Loucks, Eric B et al. (2016) Epigenome-wide profiling of DNA methylation in paired samples of adipose tissue and blood. Epigenetics 11:227-36
Huang, Yen-Tsung; Cai, Tianxi (2016) Mediation analysis for survival data using semiparametric probit models. Biometrics 72:563-74

Showing the most recent 10 out of 13 publications