Abstract 1649: Integrating genome, transcriptome and methylome data to identify novel genes for lung cancer: Data from over 50,000 European participants (2019)

Genetic factors contribute significantly to lung cancer risk. Genome-wide association studies (GWAS) have identified 18 susceptibility loci for lung cancer. However, these loci explain only approximately 12% of its familial relative risk. In addition, the causal genes remain largely unknown. The present study aimed at identifying novel risk loci and potential functional genes for lung cancer by integrating multi-omics data.Genome-wide genotyping and gene expression data of 6,124 samples from 369 participants of European ancestry included in the Genotype-Tissue Expression were used to build cross-tissue models to predict gene expression. Genome-wide DNA methylation and genetic data of white blood cell samples from 1,595 individuals of European ancestry in the Framingham Heart Study (FHS) were used to build models to predict DNA methylation. The gene expression and DNA methylation prediction models were then applied to a GWAS dataset of lung cancer, including 27,162 cases and 23,619 controls of European ancestry to investigate predicted levels of gene expression and methylation in association with lung cancer risk. For CpG sites (CpGs) showing an association with lung cancer risk, we further estimated the methylation levels in correlation with the expressions of their proximal genes using data from the FHS (N=1,367).Of the 15,810 genes investigated, significant associations with lung cancer risk were observed for 22 genes at P<3.16×10-6 (Bonferroni-corrected threshold). Among them, fourteen genes were in GWAS-identified loci and the remaining eight genes were in five novel genomic regions not yet reported as risk loci for lung cancer, including ZFP57, HLA-J, PAIP1P1, HCG18, CCL24, RNASEH2B, RP11-958N24.2, and ZNF565. Among 62,938 CpGs investigated, 177 were significantly associated with lung cancer risk at a Bonferroni-corrected threshold of P<7.94×10-7. Among them, 86 CPGs were located at GWAS-identified loci and 91 CpGs were in five novel genomic regions not yet reported as risk loci for lung cancer, including 85 CpGs at 6p22.1, cg11395890 at 1p36.21, cg09461792 at 7p21.1, cg05712524 at 7q22.1, and three CpGs,cg09414264, cg16129132 and cg12851049, at 11p15.4. Of these 177 CpGs, significant correlations were observed between 35 CpGs and 14 genes at a false discovery rate-corrected P<0.05. Taking these results together, we proposed a putative genetic variant-DNA methylation-gene expression-lung cancer risk pathway for 22 CpGs and eight potential functional genes including FUBP1, MOG, HLA-F, ZNRD1, FLOT1, DDR1, C4B and UCKL1. Among them, three genes, MOG, HLA-F and ZNRD1 and 15 CpGs were in 6p22.1, a locus not previously reported for lung cancer risk.Our findings highlight the power of integrating multi-omics data to identify novel risk loci and prioritize putative causal genes at known loci and provide new insights into the etiology of this malignancy.Citation Format: Yaohua Yang, Qiuyin Cai, Lang Wu, Xiang Shu, Xingyi Guo, Ran Tao, Bingshan Li, Xiao-Ou Shu, Wei Zheng, Jirong Long. Integrating genome, transcriptome and methylome data to identify novel genes for lung cancer: Data from over 50,000 European participants [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1649.