Mitchell J Machiela

Associations of self-identified race and ethnicity and genetic ancestry with mortality among cancer survivors

Abstract Self-identified race and ethnicity (SIRE) and genetic ancestry are potentially associated with disparities in health outcomes; however, independent effects of SIRE and genetic ancestry on mortality in cancer survivors including when adjusting for multiple risk factors are understudied. Among 23 445 cancer survivors in the Prostate, Lung, Colorectal, and Ovarian Screening Trial, SIRE was associated with mortality among prostate, colorectal, lung, ovarian, and breast cancer survivors; genetic ancestry was associated with mortality among prostate, colorectal, and breast cancer survivors. Associations were strong when adjusting for age at cancer diagnosis, sex, and tumor characteristics but attenuated when adjusting for individual-level factors and population-level socioeconomic status. For example, mortality risk was higher among Black vs White prostate cancer survivors and African genetic ancestry vs European genetic ancestry, but associations were attenuated after multilevel adjustment. Results suggest that SIRE and genetic ancestry do not solely reflect biologic variation; rather, social factors may drive mortality differences by SIRE and genetic ancestry.

GWAS Explorer: an open-source tool to explore, visualize, and access GWAS summary statistics in the PLCO Atlas

AbstractThe Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial is a prospective cohort study of nearly 155,000 U.S. volunteers aged 55–74 at enrollment in 1993–2001. We developed the PLCO Atlas Project, a large resource for multi-trait genome-wide association studies (GWAS), by genotyping participants with available DNA and genomic consent. Genotyping on high-density arrays and imputation was performed, and GWAS were conducted using a custom semi-automated pipeline. Association summary statistics were generated from a total of 110,562 participants of European, African and Asian ancestry. Application programming interfaces (APIs) and open-source software development kits (SKDs) enable exploring, visualizing and open data access through the PLCO Atlas GWAS Explorer website, promoting Findable, Accessible, Interoperable, and Re-usable (FAIR) principles. Currently the GWAS Explorer hosts association data for 90 traits and >78,000,000 genomic markers, focusing on cancer and cancer-related phenotypes. New traits will be posted as association data becomes available. The PLCO Atlas is a FAIR resource of high-quality genetic and phenotypic data with many potential reuse opportunities for cancer research and genetic epidemiology.