Context Noninvasive prenatal determination of fetal sex using cell-free fetal DNA provides an alternative to invasive techniques for some heritable disorders. In some countries this testing has transitioned to clinical care, despite the absence of a formal assessment of performance. Objective To document overall test performance of noninvasive fetal sex determination using cell-free fetal DNA and to identify variables that affect performance. Data Sources Systematic review and meta-analysis with search of PubMed (January 1, 1997–April 17, 2011) to identify English-language human studies reporting primary data. References from review articles were also searched. Study Selection and Data Extraction Abstracts were read independently to identify studies reporting primary data suitable for analysis. Covariates included publication year, sample type, DNA amplification methodology, Y chromosome sequence, and gestational age. Data were independently extracted by 2 reviewers. Results From 57 selected studies, 80 data sets (representing 3524 male-bearing pregnancies and 3017 female-bearing pregnancies) were analyzed. Overall performance of the test to detect Y chromosome sequences had the following characteristics: sensitivity, 95.4% (95% confidence interval [CI], 94.7%–96.1%) and specificity, 98.6% (95% CI, 98.1%–99.0%); diagnostic odds ratio (OR), 885; positive predictive value, 98.8%; negative predictive value, 94.8%; area under curve (AUC), 0.993 (95% CI, 0.989–0.995), with significant interstudy heterogeneity. DNA methodology and gestational age had the largest effects on test performance. Methodology test characteristics were AUC, 0.988 (95% CI, 0.979–0.993) for polymerase chain reaction (PCR) and AUC, 0.996 (95% CI, 0.993–0.998) for real-time quantitative PCR (RTQ-PCR) (P=.02). Gestational age test characteristics were AUC, 0.989 (95% CI, 0.965–0.998) (<7 weeks); AUC, 0.994 (95% CI, 0.987–0.997) (7–12 weeks); AUC, 0.992 (95% CI, 0.983–0.996) (13–20 weeks); and AUC, 0.998 (95% CI, 0.990–0.999) (>20 weeks) (P=.02 for comparison of diagnostic ORs across age ranges). RTQ-PCR (sensitivity, 96.0%; specificity, 99.0%) outperformed conventional PCR (sensitivity, 94.0%; specificity, 97.3%). Testing after 20 weeks (sensitivity, 99.0%; specificity, 99.6%) outperformed testing prior to 7 weeks (sensitivity, 74.5%; specificity, 99.1%), testing at 7 through 12 weeks (sensitivity, 94.8%; specificity, 98.9%), and 13 through 20 weeks (sensitivity, 95.5%; specificity, 99.1%). Conclusions Despite interstudy variability, performance was high using maternal blood. Sensitivity and specificity for detection of Y chromosome sequences was greatest using RTQ-PCR after 20 weeks’ gestation. Tests using urine and tests performed before 7 weeks’ gestation were unreliable.

ObjectivesA survey of a population-based sample of U.S adults was conducted to measure their attitudes about, and inform the design of the Precision Medicine Initiative’s planned national cohort study.MethodsAn online survey was conducted by GfK between May and June of 2015. The influence of different consent models on willingness to share data was examined by randomizing participants to one of eight consent scenarios.ResultsOf 4,777 people invited to take the survey, 2,706 responded and 2,601 (54% response rate) provided valid responses. Most respondents (79%) supported the proposed study, and 54% said they would definitely or probably participate if asked. Support for and willingness to participate in the study varied little among demographic groups; younger respondents, LGBT respondents, and those with more years of education were significantly more likely to take part if asked. The most important study incentive that the survey asked about was learning about one’s own health information. Willingness to share data and samples under broad, study-by-study, menu and dynamic consent models was similar when a statement about transparency was included in the consent scenarios. Respondents were generally interested in taking part in several governance functions of the cohort study.ConclusionsA large majority of the U.S. adults who responded to the survey supported a large national cohort study. Levels of support for the study and willingness to participate were both consistent across most demographic groups. The opportunity to learn health information about one’s self from the study appears to be a strong motivation to participate.

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Importance: The All of Us Research Program hypothesizes that accruing one million or more diverse participants engaged in a longitudinal research cohort will advance precision medicine and ultimately improve human health. Launched nationally in 2018, to date All of Us has recruited more than 345,000 participants. All of Us plans to open beta access to researchers in May 2020. Objective: To demonstrate the quality, utility, and diversity of the All of Us Research Programs initial data release and beta launch of the cloud-based analysis platform, the cloud-based Researcher Workbench. Evidence: We analyzed the initial All of Us data release, comprising surveys, physical measurements (PM), and electronic health record (EHR) data, to characterize All of Us participants including self-reported descriptors of diversity. Data depth, density, and quality were evaluated using medication sequencing analyses for depression and type 2 diabetes. Replication of known oncologic associations with smoking exposure ascertained by EHR and survey data and calculation of population-based atherosclerotic cardiovascular disease risk scores demonstrated the utility of data and platform capability. Findings: The beta launch of the All of Us Researcher Workbench contains data on 224,143 participants. Seventy-seven percent of this cohort were identified as Underrepresented in Biomedical Research (UBR) including over forty-eight percent self-reporting non-White race. Medication usage patterns in common diseases depression and type 2 diabetes replicated prior findings previously reported in the literature and showed differences based on race. Oncologic associations with smoking were replicated and effect sizes compared for EHR and survey exposures finding general agreement. A cardiovascular disease score was calculated utilizing multiple data elements curated across sources. The cloud-based architecture built in the Researcher Workbench provided secure access and powerful computational resources at a low cost. All analyses have been made available for replication and reuse by registered researchers. Conclusions and Relevance: The All of Us Research Programs initial release of cohort data contains longitudinal and multidimensional data on diverse participants that replicate known associations. This dataset and the cloud-based Researcher Workbench advance the mission of All of Us to make data widely and securely available to researchers to improve human health and advance precision medicine.