||5R01CA194393-07 Interpret this number
||University Of Washington
||Leveraging Cross-Cancer Shared Heritability to Better Understand the Genetic Architecture of Cancer
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
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.
If you are accessing this page during weekend or evening hours, the database may currently be offline for maintenance and should operational within a few hours. Otherwise, we have been notified of this error and will be addressing it immediately.
Please contact us
if this error persists.
We apologize for the inconvenience.
- The DCCPS Team.