RESUMEN
To facilitate scientific collaboration on polygenic risk scores (PRSs) research, we created an extensive PRS online repository for 35 common cancer traits integrating freely available genome-wide association studies (GWASs) summary statistics from three sources: published GWASs, the NHGRI-EBI GWAS Catalog, and UK Biobank-based GWASs. Our framework condenses these summary statistics into PRSs using various approaches such as linkage disequilibrium pruning/p value thresholding (fixed or data-adaptively optimized thresholds) and penalized, genome-wide effect size weighting. We evaluated the PRSs in two biobanks: the Michigan Genomics Initiative (MGI), a longitudinal biorepository effort at Michigan Medicine, and the population-based UK Biobank (UKB). For each PRS construct, we provide measures on predictive performance and discrimination. Besides PRS evaluation, the Cancer-PRSweb platform features construct downloads and phenome-wide PRS association study results (PRS-PheWAS) for predictive PRSs. We expect this integrated platform to accelerate PRS-related cancer research.
Asunto(s)
Bancos de Muestras Biológicas/estadística & datos numéricos , Predisposición Genética a la Enfermedad , Genoma Humano , Genómica/métodos , Herencia Multifactorial , Neoplasias/genética , Adulto , Anciano , Femenino , Estudio de Asociación del Genoma Completo , Humanos , Internet , Desequilibrio de Ligamiento , Masculino , Persona de Mediana Edad , Neoplasias/clasificación , Neoplasias/diagnóstico , Neoplasias/epidemiología , Fenotipo , Carácter Cuantitativo Heredable , Factores de Riesgo , Reino Unido/epidemiología , Estados Unidos/epidemiologíaRESUMEN
Polygenic risk scores (PRS) are designed to serve as single summary measures that are easy to construct, condensing information from a large number of genetic variants associated with a disease. They have been used for stratification and prediction of disease risk. The primary focus of this paper is to demonstrate how we can combine PRS and electronic health records data to better understand the shared and unique genetic architecture and etiology of disease subtypes that may be both related and heterogeneous. PRS construction strategies often depend on the purpose of the study, the available data/summary estimates, and the underlying genetic architecture of a disease. We consider several choices for constructing a PRS using data obtained from various publicly-available sources including the UK Biobank and evaluate their abilities to predict not just the primary phenotype but also secondary phenotypes derived from electronic health records (EHR). This study was conducted using data from 30,702 unrelated, genotyped patients of recent European descent from the Michigan Genomics Initiative (MGI), a longitudinal biorepository effort within Michigan Medicine. We examine the three most common skin cancer subtypes in the USA: basal cell carcinoma, cutaneous squamous cell carcinoma, and melanoma. Using these PRS for various skin cancer subtypes, we conduct a phenome-wide association study (PheWAS) within the MGI data to evaluate PRS associations with secondary traits. PheWAS results are then replicated using population-based UK Biobank data and compared across various PRS construction methods. We develop an accompanying visual catalog called PRSweb that provides detailed PheWAS results and allows users to directly compare different PRS construction methods.
Asunto(s)
Predisposición Genética a la Enfermedad , Genómica , Herencia Multifactorial/genética , Neoplasias Cutáneas/genética , Bancos de Muestras Biológicas , Registros Electrónicos de Salud , Estudio de Asociación del Genoma Completo , Genotipo , Humanos , Michigan/epidemiología , Fenotipo , Polimorfismo de Nucleótido Simple/genética , Factores de Riesgo , Neoplasias Cutáneas/patología , Reino Unido/epidemiologíaRESUMEN
BACKGROUND: Prior work has demonstrated how neighborhood poverty and racial composition impact racial disparities in access to the deceased donor kidney transplant waitlist, both nationally and regionally. We examined the association between neighborhood characteristics and racial disparities in time to transplant waitlist in Chicago, a diverse city with continued neighborhood segregation. METHODS: Using data from the United States Renal Data System (USRDS) and the US Census, we investigated time from dialysis initiation to kidney transplant waitlisting for African American and white patients in Chicago using cause-specific proportional hazards analyses, adjusting for individual sociodemographic and clinical characteristics, as well as neighborhood poverty and racial composition. RESULTS: In Chicago, African Americans are significantly less likely than whites to appear on the renal transplant waitlist (HR 0.73, P < .05). Compared to whites in nonpoor neighborhoods, African Americans in poor neighborhoods are significantly less likely to appear on the transplant waitlist (HR 0.61, P < .05). Over 69% of African Americans with ESRD live in these neighborhoods. CONCLUSIONS: Consistent with national data, African Americans in Chicago have a lower likelihood of waitlisting than whites. This disparity is explained in part by neighborhood poverty, which impacts the majority of African American ESRD patients in Chicago.