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Updated: Sep 16, 2025
Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
Published on: July 27, 2021
Phenome-wide association study identifies multiple traits associated with a polygenic risk score for colorectal
Elisabeth A Rosenthal1, Wei-Qi Wei2, Yuan Luo3
1Department of Medicine, Division of Medical Genetics, University of Washington Medical Center, Seattle, WA, USA. erosen@uw.edu.
Background:
Many factors, including environmental and genetic variables, contribute to Colorectal Cancer (CRC) risk. The genetic components of risk can be divided into monogenic and polygenic factors. Just as monogenic factors can increase risk for more than one condition, polygenic factors may also underlie multiple phenotypes, including behavioral traits. In order to understand the biology of CRC risk better, it is important to understand the shared polygenic genetic architecture contributing to CRC risk and other phenotypes, including CRC associated risk factors.
Methods:
We investigated potential shared genetics by performing a Phenome-wide association study (PheWAS) with a multi-ancestry CRC polygenic risk score (PRS). The discovery cohort (N = 426,464) consisted of ancestrally diverse participants from the United Kingdom Biobank. The replication cohort (N = 87,271) consisted of ancestrally diverse participants from the electronic Medical Records and Genomics Network phase 3. We used a mixed-effects model to adjust for the presence of related individuals. To preserve power, we limited the number of tests by restricting analysis to ancestor phecodes derived from the electronic health record (EHR) that were not likely to be a result of CRC or its treatment.
Results:
We discovered and replicated associations between the CRC PRS and breast cancer, prostate cancer, obesity, smoking and alcohol use (discovery p < 1.1e-4; replication p < 0.0019). The association between CRC risk and prostate cancer may be a novel finding, whereas the association with breast cancer has been previously observed using orthogonal methods. The association between CRC risk and behavioral risk factors corroborate previous studies, also using orthogonal methods, and may reveal potential prevention or treatment strategies.
Conclusions:
As these results corroborate findings from other studies using orthogonal methods, we demonstrate that a CRC PRS can be used as a proxy for genetic risk for CRC when investigating shared genetics between CRC and other phenotypes. Further study of the relationship between PRS from multiple traits with EHR data may reveal additional shared genetic factors. Ultimately, understanding these underlying genetic correlations may identify prevention and treatment strategies for CRC.
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