Systematic reanalysis of genomic data improves quality of variant interpretation.

As genomic sequencing expands, so does our knowledge of the link between genetic variation and disease. Deeper catalogs of variant frequencies improve identification of benign variants, while sequencing affected individuals reveals disease-associated variation. Accumulation of human genetic data thus makes reanalysis a means to maximize the benefits of clinical sequencing. We implemented pipelines to systematically reassess sequencing data from 494 individuals with developmental disability. Reanalysis yielded pathogenic or likely pathogenic (P/LP) variants that were not initially reported in 23 individuals, 6 described here, comprising a 16% increase in P/LP yield. We also downgraded 3 LP and 6 variants of uncertain significance (VUS) due to updated population frequency data. The likelihood of identifying a new P/LP variant increased over time, as ~22% of individuals who did not receive a P/LP variant at their original analysis subsequently did after 3 years. We show here that reanalysis and data sharing increase the diagnostic yield and accuracy of clinical sequencing.

PUGS: A novel scale to assess perceptions of uncertainties in genome sequencing.

Expectations of results from genome sequencing by end users are influenced by perceptions of uncertainty. This study aimed to assess uncertainties about sequencing by developing, evaluating, and implementing a novel scale. The Perceptions of Uncertainties in Genome Sequencing (PUGS) scale comprised ten items to assess uncertainties within three domains: clinical, affective, and evaluative. Participants (n=535) from the ClinSeq® NIH sequencing study completed a baseline survey that included the PUGS; responses (mean = 3.4/5, SD=0.58) suggested modest perceptions of certainty. A confirmatory factor analysis identified factor loadings that led to elimination of two items. A revised eight-item PUGS scale was used to test correlations with perceived ambiguity (r = -0.303, p < 0.001), attitudinal ambivalence (r = -0.111, p = 0.011), and ambiguity aversion (r = -0.093, p = 0.033). Results support nomological validity. A correlation with the MICRA uncertainty subscale was found among 175 cohort participants who had received results (r = -0.335, p < 0.001). Convergent and discriminant validity were also satisfied in a second sample of 208 parents from the HudsonAlpha CSER Project who completed the PUGS (mean = 3.4/5, SD = 0.72), and configural invariance was supported across the two datasets. As such, the PUGS is a promising scale for evaluating perceived uncertainties in genome sequencing, which can inform interventions to help patients form realistic expectations of these uncertainties.

Clinical decision-making and secondary findings in systems medicine.

BACKGROUND: Systems medicine is the name for an assemblage of scientific strategies and practices that include bioinformatics approaches to human biology (especially systems biology); "big data" statistical analysis; and medical informatics tools. Whereas personalized and precision medicine involve similar analytical methods applied to genomic and medical record data, systems medicine draws on these as well as other sources of data. Given this distinction, the clinical translation of systems medicine poses a number of important ethical and epistemological challenges for researchers working to generate systems medicine knowledge and clinicians working to apply it. DISCUSSION: This article focuses on three key challenges: First, we will discuss the conflicts in decision-making that can arise when healthcare providers committed to principles of experimental medicine or evidence-based medicine encounter individualized recommendations derived from computer algorithms. We will explore in particular whether controlled experiments, such as comparative effectiveness trials, should mediate the translation of systems medicine, or if instead individualized findings generated through "big data" approaches can be applied directly in clinical decision-making. Second, we will examine the case of the Riyadh Intensive Care Program Mortality Prediction Algorithm, pejoratively referred to as the "death computer," to demonstrate the ethical challenges that can arise when big-data-driven scoring systems are applied in clinical contexts. We argue that the uncritical use of predictive clinical algorithms, including those envisioned for systems medicine, challenge basic understandings of the doctor-patient relationship. Third, we will build on the recent discourse on secondary findings in genomics and imaging to draw attention to the important implications of secondary findings derived from the joint analysis of data from diverse sources, including data recorded by patients in an attempt to realize their "quantified self." This paper examines possible ethical challenges that are likely to be raised as systems medicine to be translated into clinical medicine. These include the epistemological challenges for clinical decision-making, the use of scoring systems optimized by big data techniques and the risk that incidental and secondary findings will significantly increase. While some ethical implications remain still hypothetical we should use the opportunity to prospectively identify challenges to avoid making foreseeable mistakes when systems medicine inevitably arrives in routine care.

Inclusion of pediatric samples in an opt-out biorepository linking DNA to de-identified medical records: pediatric BioVU.

The Vanderbilt DNA repository, BioVU, links DNA from leftover clinical blood samples to de-identified electronic medical records (EMRs). After initiating adult sample collection, pediatric extension required consideration of ethical concerns specific to pediatrics and implementation of specialized DNA extraction methods. In the first year of pediatric sample collection, more than 11,000 samples from individuals younger than 18 years were included. We compared data from the pediatric BioVU cohort with those from the overall Vanderbilt University Medical Center pediatric population and found similar demographic characteristics; however, the BioVU cohort had higher rates of select diseases, medication exposures, and laboratory testing, demonstrating enriched representation of severe or chronic disease. The fact that the sample accumulation is not balanced may accelerate research in some cohorts while limiting the study of relatively benign conditions and the accrual of unaffected and unbiased control samples. BioVU represents a feasible model for pediatric DNA biobanking but involves both ethical and practical considerations specific to the pediatric population.