Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2026
The Multiethnic Cohort: A Resource for the Study of Genetic and Nongenetic Cancer Risk across Populations.
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Bogumil David, Sheng Xin, Wan Peggy, Xia Lucy, Pooler Loreall, Cheng Iona, Streicher Samantha A, Huang Brian Z, Chen Fei, Stram Daniel O, Shen Jiayi, King Gillian, Chiang Charleston W K, Ongaco Chrissie M, Adams Marcia, McMullen Ivy, Zhang Peng, Ling Hua, Mawhinney Michelle, Doheny Kimberly F, Le Marchand Loïc, Wilkens Lynne R, Haiman Christopher A, Conti David V
Аннотация
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 multiancestry analyses of genetic and nongenetic cancer risk, supporting research on disparities, polygenic traits, and integrated risk prediction.
IMPACT: Example analyses using these resources show the relationship between population descriptors, PRSs, and common cancer risk factors that require special consideration in genetic analyses.
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 multiancestry analyses of genetic and nongenetic cancer risk, supporting research on disparities, polygenic traits, and integrated risk prediction.
IMPACT: Example analyses using these resources show the relationship between population descriptors, PRSs, and common cancer risk factors that require special consideration in genetic analyses.