Genomic Data Heterogeneity across Molecular Diagnostic Laboratories: A Real-World Connect Myeloid Disease Registry Perspective on Variabilities in Genomic Assay Methodology and Reporting.

Genomic data variability from laboratory reports can impact clinical decisions and population-level analyses; however, the extent of this variability and the impact on the data's value are not well characterized. This pilot study used anonymized genetic and genomic test reports from the Connect Myeloid Disease Registry (NCT01688011), a multicenter, prospective, observational cohort study of patients with newly diagnosed myelodysplastic syndromes, acute myeloid leukemia, or idiopathic cytopenia of undetermined significance, to analyze laboratory test variabilities and limitations. Results for 56 randomly selected patients enrolled in the Registry were independently extracted and evaluated (data cutoff, January 2020). Ninety-five reports describing 113 assay results from these 56 patients were analyzed for discrepancies. Almost all assay results [101 (89%)] identified the sequencing technology applied, and 94 (83%) described the test limitations; 95 (84%) described the limits of detection, but none described the limit of blank for detecting false positives. RNA transcript identifiers were not provided for 20 (43%) variants analyzed by next-generation sequencing and reported by the same laboratory. Of 42 variants with variant allele frequencies ≥30%, 16 (38%) of the variants did not have report text indicating that the variants might be germline. Variabilities and lack of standardization present challenges for incorporating this information into clinical care and render data collation ineffective and unreliable for large-scale use in centralized databases for therapeutic discovery.

Assessment of an automated approach for variant interpretation in screening for monogenic disorders: A single-center study.

BACKGROUND: Automation has been introduced into variant interpretation, but it is not known how automated variant interpretation performs on a stand-alone basis. The purpose of this study was to evaluate a fully automated computerized approach. METHOD: We reviewed all variants encountered in a set of carrier screening panels over a 1-year interval. Observed variants with high-confidence ClinVar interpretations were included in the analysis; those without high-confidence ClinVar entries were excluded. RESULTS: Discrepancy rates between automated interpretations and high-confidence ClinVar entries were analyzed. Of the variants interpreted as positive (likely pathogenic or pathogenic) based on ClinVar information, 22.6% were classified as negative (variants of uncertain significance, likely benign or benign) variants by the automated method. Of the ClinVar negative variants, 1.7% were classified as positive by the automated software. On a per-case basis, which accounts for variant frequency, 63.4% of cases with a ClinVar high-confidence positive variant were classified as negative by the automated method. CONCLUSION: While automation in genetic variant interpretation holds promise, there is still a need for manual review of the output. Additional validation of automated variant interpretation methods should be conducted.

Nutrients and Pharmaceuticals Structure Bacterial Core Communities in Urban and Montane Stream Biofilms.

Bacteria in stream biofilms contribute to stream biogeochemical processes and are potentially sensitive to the substantial levels of pollution entering urban streams. To examine the effects of contaminants on stream biofilm bacteria in situ, we exposed growing biofilms to experimental additions of nutrients [nitrogen (N), phosphorus (P), and iron (Fe)], pharmaceuticals (caffeine and diphenhydramine), nutrients plus pharmaceuticals, or no contaminants using contaminant exposure substrates (CES) in three catchments in northern Utah. We performed our study at montane and urban sites to examine the influence of existing pollution on biofilm response. We identified bacterial core communities (core) for each contaminant treatment at each land-use type (e.g., nutrient addition montane bacterial core, nutrient addition urban bacterial core, pharmaceutical addition montane bacterial core) by selecting all taxa found in at least 75% of the samples belonging to each specific grouping. Montane and urban land-use distinguished bacterial cores, while nutrients and pharmaceuticals had subtle, but nonetheless distinct effects. Nutrients enhanced the dominance of already abundant copiotrophs [i.e., Pseudomonadaceae (Gammaproteobacteria) and Comamonadaceae (Betaproteobacteria)] within bacterial cores at montane and urban sites. In contrast, pharmaceuticals fostered species-rich bacterial cores containing unique contaminant-degrading taxa within Pseudomonadaceae and Anaerolineaceae (Chloroflexi). Surprisingly, even at urban sites containing ambient pharmaceutical pollution, pharmaceutical additions increased bacterial core richness, specifically within DR-16 (Betaproteobacteria), WCHB1-32 (Bacteroidetes), and Leptotrichiaceae (Fusobacteria). Nutrients exerted greater selective force than pharmaceuticals in nutrient plus pharmaceutical addition treatments, creating bacterial cores more closely resembling those under nutrient rather than pharmaceutical addition, and promoting unique Oscillatoriales (Cyanobacteria) taxa in urban streams. Our results show that additions of N, P, and Fe intensified the dominance of already abundant copiotrophs, while additions of caffeine and diphenhydramine enabled unique taxa associated with contaminant degradation to participate in bacterial cores. Further, biofilm bacteria at urban sites remained sensitive to pharmaceuticals commonly present in waters, suggesting a dynamic interplay among pharmaceutical pollution, bacterial diversity, and contaminant degradation.