Genome targeting methods enable cost-effective capture of specific subsets of the genome for sequencing. We present here an automated, highly scalable method for carrying out the Solution Hybrid Selection capture approach that provides a dramatic increase in scale and throughput of sequence-ready libraries produced. Significant process improvements and a series of in-process quality control checkpoints are also added. These process improvements can also be used in a manual version of the protocol.
Although the intent of community-based participatory research (CBPR) is to include community voices in all phases of a research initiative, community partners appear less frequently engaged in data analysis and interpretation than in other research phases. Using 4 brief case studies, each with a different data collection methodology, we provide examples of how community members participated in data analysis, interpretation, or both, thereby strengthening community capacity and providing unique insight. The roles and skills of the community and academic partners were different from but complementary to each other. We suggest that including community partners in data analysis and interpretation, while lengthening project time, enriches insights and findings and consequently should be a focus of the next generation of CBPR initiatives.
The Healthy Environments Partnership (HEP) is a community-based participatory research effort investigating variations in cardiovascular disease risk, and the contributions of social and physical environments to those variations, among non-Hispanic black, non-Hispanic white, and Hispanic residents in three areas of Detroit, Michigan. Initiated in October 2000 as a part of the National Institute of Environmental Health Sciences’ Health Disparities Initiative, HEP is affiliated with the Detroit Community–Academic Urban Research Center. The study is guided by a conceptual model that considers race-based residential segregation and associated concentrations of poverty and wealth to be fundamental factors influencing multiple, more proximate predictors of cardiovascular risk. Within this model, physical and social environments are identified as intermediate factors that mediate relationships between fundamental factors and more proximate factors such as physical activity and dietary practices that ultimately influence anthropomorphic and physiologic indicators of cardiovascular risk. The study design and data collection methods were jointly developed and implemented by a research team based in community-based organizations, health service organizations, and academic institutions. These efforts include collecting and analyzing airborne particulate matter over a 3-year period; census and administrative data; neighborhood observation checklist data to assess aspects of the physical and social environment; household survey data including information on perceived stressors, access to social support, and health-related behaviors; and anthropometric, biomarker, and self-report data as indicators of cardiovascular health. Through these collaborative efforts, HEP seeks to contribute to an understanding of factors that contribute to racial and socioeconomic health inequities, and develop a foundation for efforts to eliminate these disparities in Detroit.
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