Mixed-effects polygenic risk score Phenome-wide association study detects genetic correlation between colorectal cancer risk and phenotype data extracted from the electronic health record.

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Tác giả: David R Crosslin, Edward D Esplin, Adam Samuel Gordon, William M Grady, Li Hsu, Gail P Jarvik, Leah Kottyan, Iftikhar J Kullo, Michael Lape, Yuan Luo, Bahram Namjou-Khales, Jennifer Allen Pacheco Allen Pacheco, Ulrike Peters, Elisabeth A Rosenthal, Daniel J Schaid, Wei-Qi Wei, Chunhua Weng

Ngôn ngữ: eng

Ký hiệu phân loại: 133.594 Types or schools of astrology originating in or associated with a

Thông tin xuất bản: United States : medRxiv : the preprint server for health sciences , 2025

Mô tả vật lý:

Bộ sưu tập: NCBI

ID: 683494

 Many factors, including environmental and genetic variables, contribute to Colorectal Cancer (CRC) risk. Some of these risk factors may share underlying genetics with CRC. 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. We used a mixed-effects model to adjust for the presence of related individuals in both datasets. To preserve power, we limited testing to ancestor phecodes derived from the electronic health record (EHR), which were not likely to be a result of CRC or its treatment. 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). 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.
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