The Multiethnic Cohort: A Resource for the study of Genetic and non-Genetic Cancer Risk Across Populations
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Introduction
The Multiethnic Cohort Study (MEC) is a U.S. prospective cohort of over 215,000 participants, designed to investigate variation in risk factors and disease across diverse racial and ethnic groups. Over 74,000 participants contributed biospecimens for genetic studies. We describe this sub-cohort and demonstrate the types of analyses it enables.
Methods
The MEC recruited adults aged 45–75 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. Using breast, colorectal, and prostate cancer as examples, the database supports multi-ancestry genome-wide association studies (GWAS), evaluation of non-genetic factors, and 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 other. Principal component analysis revealed substantial diversity in ancestry. Multiethnic GWAS demonstrated effective control of population stratification while replicating many previously discovered variants. Polygenic risk score (PRS) effects varied by racial and ethnic group. Time-to-event analysis showed associations between cancer incidence and neighborhood socioeconomic status, population descriptors, and genetic similarity.
Discussion
The MEC Genetics Database enables comprehensive assessment of genetic and non-genetic cancer risk, revealing differences in absolute risk by race and ethnicity. Studying both types of risk factors in diverse and admixed populations is critical for improving risk characterization and reducing disparities. This resource supports future research in polygenic traits, gene-environment interactions, and integrated risk prediction.
