Publications by authors named "R S Rowley"
Article Synopsis
- Polygenic risk scores (PRSs) could enhance disease risk prediction, but their current effectiveness is compromised for non-European populations, creating potential health disparities.
- The PRIMED Consortium aims to improve PRS performance by aggregating diverse genetic data on a cloud platform and evaluating ethical implications related to its implementation.
- Focused on cardiometabolic diseases and cancer, PRIMED seeks to promote equity in polygenic risk assessment through collaboration across multiple research sites and organizations.
Objective: Data from DNA genotyping via a 96-SNP panel in a study of 25,015 clinical samples were utilized for quality control and tracking of sample identity in a clinical sequencing network. The study aimed to demonstrate the value of both the precise SNP tracking and the utility of the panel for predicting the sex-by-genotype of the participants, to identify possible sample mix-ups.
Results: Precise SNP tracking showed no sample swap errors within the clinical testing laboratories.
Polygenic risk scores (PRSs) have improved in predictive performance, but several challenges remain to be addressed before PRSs can be implemented in the clinic, including reduced predictive performance of PRSs in diverse populations, and the interpretation and communication of genetic results to both providers and patients. To address these challenges, the National Human Genome Research Institute-funded Electronic Medical Records and Genomics (eMERGE) Network has developed a framework and pipeline for return of a PRS-based genome-informed risk assessment to 25,000 diverse adults and children as part of a clinical study. From an initial list of 23 conditions, ten were selected for implementation based on PRS performance, medical actionability and potential clinical utility, including cardiometabolic diseases and cancer.
View Article and Find Full Text PDFIntroduction: Phenotyping algorithms enable the interpretation of complex health data and definition of clinically relevant phenotypes; they have become crucial in biomedical research. However, the lack of standardization and transparency inhibits the cross-comparison of findings among different studies, limits large scale meta-analyses, confuses the research community, and prevents the reuse of algorithms, which results in duplication of efforts and the waste of valuable resources.
Recommendations: Here, we propose five independent fundamental dimensions of phenotyping algorithms-complexity, performance, efficiency, implementability, and maintenance-through which researchers can describe, measure, and deploy any algorithms efficiently and effectively.