Skip to main content

COVID-19 is an emerging, rapidly evolving situation.

What people with cancer should know: https://www.cancer.gov/coronavirus

Guidance for cancer researchers: https://www.cancer.gov/coronavirus-researchers

Get the latest public health information from CDC: https://www.coronavirus.gov

Get the latest research information from NIH: https://www.nih.gov/coronavirus

Grant Details

Grant Number: 1R03CA245886-01A1 Interpret this number
Primary Investigator: Han, Jiali
Organization: Indiana Univ-Purdue Univ At Indianapolis
Project Title: Integrative Functional Characterization of Genetic Loci for Cutaneous Basal Cell Carcinoma
Fiscal Year: 2020


Abstract

Cutaneous basal cell carcinoma (BCC) is the most common malignancy diagnosed in the USA and is becoming more frequent in younger individuals. The heavy public health burden associated with BCC underscores the importance of efficient management and prevention efforts directed toward this malignancy, especially targeting the high-risk population. We recently published a large genome-wide association study (GWAS) on BCC with 17,187 cases and 287,054 controls. We yield a list of 31 loci for further investigation to elucidate true causal variants and the specific genes or DNA functional elements through which the genetic variants exert their effects. In this application, we aim to carry out the first well-positioned post-GWAS investigation of BCC to integrate complementary strategies including fine- mapping and transcriptome-wide association study (TWAS), targeting potentially causal variants and functionally relevant genes. We propose the following specific aims: (1) Perform empirical Bayes fine- mapping using the Probabilistic Annotation INTegratOR (PAINTOR) software to predict causal single- nucleotide polymorphisms (SNPs). PAINTOR will summarize GWAS data, information on local linkage disequilibrium (LD) patterns, as well as functional annotations for SNPs from publicly available databases (e.g. ENCODE, Epigenome Roadmap, GTEx, and Metabolomic GWAS Server). (2) Perform TWAS analysis, which integrates current GWAS-meta data on BCC with data from expression quantitative trait loci (eQTL) studies to identify genes whose expression levels are significantly associated with BCC risk. Several novel and emerging robust approaches will be used in our TWAS (including an extension of pleiotropy-robust MR-Egger regression that we have developed) to reduce the risk of false positives due to these confounders. We will conduct a series of TWAS analyses using eQTL data from blood samples and skin tissue samples to distinguish SNPs with skin-specific eQTL effects and those with cross-tissue effects. This study will not only greatly advance our understanding of BCC pathogenesis, but also provide a robust scientific basis for developing clinically useful genetic risk prediction model for precision prevention. Moreover, this proposed study may pinpoint some potential/actionable targets for therapeutic intervention and risk reduction.



Publications


None


Back to Top