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Methods: Adults aged 45–75 were recruited in California and Hawaii (1993–1996). Cancer outcomes used state tumor registries; the MEC Genetics Database includes germline genotype data for 73,139 participants. Results: The cohort spans multiple ancestry groups, enabling diverse principal-component patterns, replicated GWAS findings, polygenic risk score variation, and time-to-event associations with nSES and genetic similarity. Conclusions: The MEC Genetics Database supports integrated, multiancestry genetic and nongenetic cancer-risk analyses and research on disparities.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/the-multiethnic-cohort-a-resource-for-the-study-of-genetic-and-nongenetic-cancer-risk-across-populations/349403/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/the-multiethnic-cohort-a-resource-for-the-study-of-genetic-and-nongenetic-cancer-risk-across-populations/349403.png","ImageObject",300,407,{"name":92,"@type":93},"Aldword","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-23","2026-09-22",true,{"@type":102,"interactionType":103,"userInteractionCount":81},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the Multiethnic Cohort Study (MEC) designed to investigate?","Question",{"text":112,"@type":113},"It is designed to study how cancer risk factors vary across diverse racial and ethnic populations, integrating genetic and nongenetic information. The MEC supports analyses of disease variation and disparities across groups.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were cancer outcomes and genetic data obtained for the MEC?",{"text":117,"@type":113},"Cancer diagnoses were identified using state tumor registries. The MEC Genetics Database includes germline genotype data for participants and enables genetic similarity and ancestry-related analyses.",{"name":119,"@type":110,"acceptedAnswer":120},"What kinds of analyses does the MEC Genetics Database enable?",{"text":121,"@type":113},"It enables multiancestry genetic and nongenetic cancer-risk analyses, including genomewide association studies, polygenic risk score evaluation, and time-to-event models relating cancer incidence to nSES and genetic similarity.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},349403,1790141774,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":81,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":41},2336478940917,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","The Multiethnic Cohort: A Resource for the Study of Genetic and Nongenetic Cancer Risk across Populations  \nDavid Bogumil1, Xin Sheng1, Peggy Wan1, Lucy Xia1, Loreall Pooler1, Iona Cheng2,  \nSamantha A. Streicher3, Brian Z. Huang1, Fei Chen1, Daniel O. Stram1,4,5, Jiayi Shen1, Gillian King1, Charleston W. K. Chiang1,4, Chrissie M. Ongaco6, Marcia Adams6, Ivy McMullen6, Peng Zhang6, Hua Ling6, Michelle Mawhinney6, Kimberly F. Doheny6, Lo Le Marchand3, Lynne R. Wilkens3, Christopher A. Haiman1,4,5, and David V. Conti1,4,5  \n|  |  | A |  | B | S |  | T | R |  | A |  | C | T |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n|  | 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.\u003Cbr>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 selfreported race/ethnicity, and baseline characteristics, including neighborhood socioeconomic status (nSES) . Using breast, colorectal, and prostate cancer as examples, we conducted genomewide association studies (GWAS), assessed nongenetic risk factors, and performed time-to-event analyses.\u003Cbr>􀀶 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | 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.\u003Cbr>Conclusions: The MEC Genetics Database enables multiancestry analyses of genetic and nongenetic cancer risk, supporting research on disparities, polygenic traits, and integrated risk prediction.\u003Cbr>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. |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |\n\nIntroduction  \nThe lack of diversity in genomic data resources available to the scientific community greatly limits the translational impact of findings that may result in an exacerbation of health disparities due to differential benefits of study findings across populations (1–7) . Among the available resources for genomics research, there are limited options about study design and the availability of n","cbCaii5GyWuOPj3y","https://ap.wps.com/l/cbCaii5GyWuOPj3y","pdf",7507621,12,"English","# Background\n# Methods\n# Results\n# Conclusions\n# Impact\n# Introduction","[{\"question\":\"What is the Multiethnic Cohort Study (MEC) designed to investigate?\",\"answer\":\"It is designed to study how cancer risk factors vary across diverse racial and ethnic populations, integrating genetic and nongenetic information. The MEC supports analyses of disease variation and disparities across groups.\"},{\"question\":\"How were cancer outcomes and genetic data obtained for the MEC?\",\"answer\":\"Cancer diagnoses were identified using state tumor registries. The MEC Genetics Database includes germline genotype data for participants and enables genetic similarity and ancestry-related analyses.\"},{\"question\":\"What kinds of analyses does the MEC Genetics Database enable?\",\"answer\":\"It enables multiancestry genetic and nongenetic cancer-risk analyses, including genomewide association studies, polygenic risk score evaluation, and time-to-event models relating cancer incidence to nSES and genetic similarity.\"}]","The Multiethnic Cohort: A Resource for the Study of Genetic and Nongenetic Cancer Risk across Populations | PDF",1790083157]