Leveraging cross-cancer shared heritability to better understand the genetic architecture of cancer

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ABSTRACT Genome-wide association studies (GWAS) and transcriptome-wide association studies (TWAS) have identified hundreds of common, modest-effect alleles and genes associated with cancer risk, but much of cancer heritability remains unexplained. To date, most epidemiological studies of cancer focus on individual cancer types. We propose to leverage the shared heritability across cancers to conduct the largest cross-cancer GWAS and TWAS to date. To achieve our goal, we will use individual and summary GWAS data from 12 solid cancers (breast, colorectal, endometrial, esophageal, glioma, head and neck, lung, melanoma, ovarian, pancreatic, prostate and renal) based on more than 400,000 cases and 900,000 controls expanding our prior work with six new cancer sites and more than 100,000 new cancer cases. We will conduct overall and subset-based cross-cancer GWAS meta-analysis to identify novel cancer risk alleles (Aim 1a). We will also develop statistical methods that explicitly test for pleiotropic effects using summary statistics only and apply these to both known and novel cancer SNPs (Aim 1b). We will develop and apply methods for cross-cancer TWAS, leveraging the genetic regulation of gene expression in both tumor (TCGA) and normal (GTEx) tissue (Aim 2). Finally, we will use novel methods that leverage both GWAS summary statistics and individual-level data from dbGaP and UK Biobank, as well as functional annotation data from the ENCODE and the RoadMap Epigenomics projects to conduct in-depth heritability analysis of cancer. Specifically, we will model the relative effect sizes of risk alleles as a function of allele frequency and genomic annotation (Aim 3a), and for the first time assess the presence of dominance effects across multiple cancers (Aim 3b). The proposed Aims build on our previous success in using large GWAS summary statistics to establish and quantify the shared genetic contribution to multiple cancers. They also build on our proven track record for developing and applying statistical methods to conduct multi-phenotype association studies and heritability estimation. Our application is in response to PA-17-239: “Secondary Analysis and Integration of Existing Data to Elucidate the Genetic Architecture of Cancer Risk and Related Outcomes”. We have brought together investigators from 12 different cancer GWAS consortia, creating an unprecedented opportunity to identify novel cancer susceptibility loci. As part of the proposed research, we will develop a series of new statistical methods that can be broadly applied to other disease groups with a shared genetic basis. Completion of our Aims will lead to discovery of novel cancer risk alleles and identify shared pathways involved in tumor development across cancers. It will also inform the design and analysis of future sequencing studies to identify low- frequency and rare variants associated with cancer risk, by providing guidance on plausible effect sizes, required sample sizes and the genomic features most likely to harbor large-effect low-frequency variants.