Identification of Susceptibility Gene Targets from Integrative Analysis of Genome-Wide Association Study and Expression Quantitative Trait Loci Study in Nasopharyngeal Carcinoma

Genome-wide association studies (GWASs) have identified thousands of loci robustly associated with complex diseases. However, most of the causal variants at GWAS loci remain unknown. Recent advancement in systematic investigation of genetic variants that directly affect gene expression name expression quantitative trait loci (eQTLs) has shown that eQTLs are enriched in GWAS loci and GWAS risk variants usually co-localize with eQTLs. These observations suggest that GWAS risk variants could be eQTLs and may influence disease status by regulate expression levels of their target genes. Consequently, the causal variants could be identified by matching GWAS variants with eQTLs. Previously, we have performed GWAS and a preliminary eQTL analysis in NPC. In this research proposal, we will integrate GWAS and eQTL data sets and try to identify causal variants in NPC GWAS loci. On the other hand, we will also use the eQTL data set to discover novel NPC-associated genes through transcriptome-wise association study (TWAS). For all the GWAS variants identified, they can only explain a small fraction of heritability, therefore, there are still many disease-associated genes remain unknown. TWAS use eQTL data to predict gene expression levels for GWAS samples and then test for association between the predicted expressions and traits. Thus, TWAS may discover novel disease-associated genes which may have been missed by single variant GWAS analysis. Use those two systematic approaches, we aim to further understand the genetic architecture and the hereditary components underneath the etiology of NPC. For clinical application, the genetic components discovered can be used in the evaluation of personal cancer risk which can help in cancer prevention. A complete understanding of how genetic variants influence gene expression and cell biology will be useful for improving the predictive models for personalized medicine. With the increasing emphasis on personalized medicine and the life quality of cancer patient, our research will provide significant contribution to cancer treatment and prevention.

Project ID:PC10708-0954
External Project ID:MOST107-2314-B182-051