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AbstractProponents of the validity of the classical MZ-DZ twin comparison model for calculating heritability claim that the environments influencing MZ and DZ twin individuals are essentially identical. This 'equal environments assumption' may or may not be universally true when applied to the analysis of subjective traits. We examined the validity of this assumption as applied to the propensity for smoking cigarettes, reasoning that equality of environments should lead to equal smoking prevalences in MZ and DZ twin individuals. We identified 8 twin populations with data on smoking. We compiled odds ratios (ORs) for ever smoking in MZ and DZ twin individuals in these 8 studies and overall, using a fixed-effects meta-analytic method based on the Mantel-Haenszel procedure. The prevalence of smoking was less in MZ twin individuals than in DZ twin individuals in 7 of 8 studies. The overall OR was 0.86 (95% confidence interval 0.84, 0.89). ORs were virtually unchanged when the analyses were stratified for gender and age, and no differences were found in relation to the location of the study, the date of the study or the birth years of the cohorts. For cigarette smoking, the environments of MZ and DZ twins may not be co-equal. For subjective traits, heritability estimates may be influenced by these unequal environmental factors that differentially affect their development and characteristics in MZ and DZ twins.

PURPOSE: Institutional efforts toward the democratization of cloud-scale data and analysis methods for cancer genomics are proceeding rapidly. As part of this effort, we bridge two major bioinformatic initiatives: the Global Alliance for Genomics and Health (GA4GH) and Bioconductor. METHODS: We describe in detail a use case in pancancer transcriptomics conducted by blending implementations of the GA4GH Workflow Execution Services and Tool Registry Service concepts with the Bioconductor curatedTCGAData and BiocOncoTK packages. RESULTS: We carried out the analysis with a formally archived workflow and container at dockstore.org and a workspace and notebook at app.terra.bio. The analysis identified relationships between microsatellite instability and biomarkers of immune dysregulation at a finer level of granularity than previously reported. Our use of standard approaches to containerization and workflow programming allows this analysis to be replicated and extended. CONCLUSION: Experimental use of dockstore.org and app.terra.bio in concert with Bioconductor enabled novel statistical analysis of large genomic projects without the need for local supercomputing resources but involved challenges related to container design, script archiving, and unit testing. Best practices and cost/benefit metrics for the management and analysis of globally federated genomic data and annotation are evolving. The creation and execution of use cases like the one reported here will be helpful in the development and comparison of approaches to federated data/analysis systems in cancer genomics.