The Multiethnic Cohort: A Resource for the Study of Genetic and Nongenetic Cancer Risk across Populations

作者：David Bogumil, Xin Sheng, Peggy Wan, Lucy Xia, Loreall Pooler, Iona Cheng, Samantha A. Streicher, Brian Z. Huang, Fei Chen, Daniel O. Stram, Sylvia S. Shen, Gillian King, Charleston W. K. Chiang, Chrissie M. Ongaco, Marcia Adams, Ivy McMullen, Peng Zhang, Hua Ling, Michelle Mawhinney, Kimberly F. Doheny, Loı̈c Le Marchand, Lynne R. Wilkens, Christopher A. Haiman, David V. Conti · 发表于：Cancer Epidemiology Biomarkers & Prevention · 年份：2026 · DOI：10.1158/1055-9965.epi-25-1458 · 被引用次数：1 · 研究领域：Genetic Associations and Epidemiology、BRCA gene mutations in cancer、Forensic and Genetic Research

BACKGROUND: The Multiethnic Cohort Study (MEC) is a US prospective cohort of more than 215,000 participants, designed to investigate variation in risk factors and disease across diverse racial and ethnic groups. More than 74,000 participants contributed biospecimens for genetic studies. We describe this subcohort and demonstrate the types of analyses it enables. METHODS: The MEC recruited adults aged 45 to 75 in California and Hawaii between 1993 and 1996. Cancer diagnoses were identified via state tumor registries. The MEC Genetics Database includes 73,139 participants with germline genotype data. We evaluated genetic similarity, its relationship with self-reported race/ethnicity, and baseline characteristics, including neighborhood socioeconomic status (nSES). Using breast, colorectal, and prostate cancer as examples, we conducted genome-wide association studies (GWAS), assessed nongenetic risk factors, and performed time-to-event analyses. RESULTS: Participants included 10,962 African Americans, 24,234 Japanese Americans, 17,242 Latinos, 5,488 Native Hawaiians, 14,649 Whites, and 564 others. Principal component analysis showed substantial diversity. Multiethnic GWAS replicated known variants with effective control of population stratification. Polygenic risk score (PRS) effects varied across groups. Time-to-event models revealed associations between cancer incidence and nSES, population descriptors, and genetic similarity. CONCLUSIONS: The MEC Genetics Database enables mul...