A Pilot Study to Determine MBSE Utility for Process Modeling of Complex Interfaces

Modeling a full system or a complete interface between systems in a MBSE environment is a very large task and not all organizations will benefit enough from using MBSE to offset the effort that is required to do this. Completely modeling a system or interface is not necessary to evaluate the utility of MBSE for a specific application or organization. A small pilot can be executed over a short period of time that only models small portions of a system or interface and, if structured properly, this pilot can successfully demonstrate the utility of MBSE for an organization before having to invest a larger amount of resources to fully implement and deploy MBSE. This paper documents one such pilot that was conducted for NASAs Launch Services Program

A Pilot Study to Determine MBSE Utility for Process Modeling of Complex Interfaces

Modeling a full system or a complete interface between systems in a MBSE environment is a very large task and not all organizations will benefit enough from using MBSE to offset the effort that is required to do this. Completely modeling a system or interface is not necessary to evaluate the utility of MBSE for a specific application or organization. A small pilot can be executed over a short period of time that only models small portions of a system or interface and, if structured properly, this pilot can successfully demonstrate the utility of MBSE for an organization before having to invest a larger amount of resources to fully implement and deploy MBSE. This paper documents one such pilot that was conducted for NASA's Launch Services Program

A Pilot Study to Determine MBSE Utility for Process Modeling of Complex Interfaces

Modeling a full system or a complete interface between systems in a MBSE environment is a very large task and not all organizations will benefit enough from using MBSE to offset the effort that is required to do this. Completely modeling a system or interface is not necessary to evaluate the utility of MBSE for a specific application or organization. A small pilot can be executed over a short period of time that only models small portions of a system or interface and, if structured properly, this pilot can successfully demonstrate the utility of MBSE for an organization before having to invest a larger amount of resources to fully implement and deploy MBSE. This paper documents one such pilot that was conducted for NASA's Launch Services Program

Improving recruitment to clinical trials with a register of a million patients who agree to the use of their clinical records for research in the Scottish Health Research Register (SHARE)

The UK's technical ability to identify people eligible for medical research is not yet matched by a practical capability to approach them directly to ask them to consider participation in those studies. The consequence is that recruitment to research is more difficult than necessary and some projects fail. This makes Britain a less attractive location to undertake clinical research than it should be. In order to overcome this increasingly important obstacle, we wanted to develop a register of Scottish residents who wish to be considered for participation in a range of studies. This was an oral presentation given at the Clinical Trials Methodology conference 2011, 4-5 October 2011, Bristol, UK

Evaluation of the activity of CYP2C19 in Gujrati and Marwadi subjects living in Mumbai (Bombay)

BACKGROUND: Inherited differences in the metabolism and disposition of drugs, and genetic polymorphisms in the targets of drug therapy (e.g., receptors), can greatly influence efficacy and toxicity of medications. Marked interethnic differences in CYP2C19 (a member of the cytochrome P-450 enzyme superfamily catalyzing phase I drug metabolism) which affects the metabolism of a number of clinically important drugs have been documented. The present study evaluated the activity of CYP2C19 in normal, healthy Gujrati and Marwadi subjects by phenotyping (a western Indian population). METHODS: All subjects received 20 mg of omeprazole, which was followed by blood collection at 3 hrs to estimate the metabolic ratio of omeprazole to 5-hydroxyomeprazole. The analysis was done by HPLC. RESULTS: It was seen that 10.36% of this population were poor metabolizers(PM) whereas 89.63% were extensive metabolizers(EM). CONCLUSION: A genotyping evaluation would better help in identifying population specific genotypes and thus help individualize drug therapy

Testing for an Unusual Distribution of Rare Variants

Technological advances make it possible to use high-throughput sequencing as a primary discovery tool of medical genetics, specifically for assaying rare variation. Still this approach faces the analytic challenge that the influence of very rare variants can only be evaluated effectively as a group. A further complication is that any given rare variant could have no effect, could increase risk, or could be protective. We propose here the C-alpha test statistic as a novel approach for testing for the presence of this mixture of effects across a set of rare variants. Unlike existing burden tests, C-alpha, by testing the variance rather than the mean, maintains consistent power when the target set contains both risk and protective variants. Through simulations and analysis of case/control data, we demonstrate good power relative to existing methods that assess the burden of rare variants in individuals

zCall: a rare variant caller for array-based genotyping

Summary: zCall is a variant caller specifically designed for calling rare single-nucleotide polymorphisms from array-based technology. This caller is implemented as a post-processing step after a default calling algorithm has been applied. The algorithm uses the intensity profile of the common allele homozygote cluster to define the location of the other two genotype clusters. We demonstrate improved detection of rare alleles when applying zCall to samples that have both Illumina Infinium HumanExome BeadChip and exome sequencing data available

Extremely low-coverage sequencing and imputation increases power for genome-wide association studies

Genome wide association studies (GWAS) have proven a powerful method to identify common genetic variants contributing to susceptibility to common diseases. Here we show that extremely low-coverage sequencing (0.1–0.5x) captures almost as much of the common (>5%) and low-frequency (1–5%) variation across the genome as SNP arrays. As an empirical demonstration, we show that genome-wide SNP genotypes can be inferred at a mean r2 of 0.71 using off-target data (0.24x average coverage) in a whole-exome study of 909 samples. Using both simulated and real exome sequencing datasets we show that association statistics obtained using ultra low-coverage sequencing data attain similar P-values at known associated variants as genotyping arrays, without an excess of false positives. Within the context of reductions in sample preparation and sequencing costs, funds invested in ultra low-coverage sequencing can yield several times the effective sample size of SNP-array GWAS, and a commensurate increase in statistical power

A framework for the interpretation of de novo mutation in human disease

Spontaneously arising (‘de novo’) mutations play an important role in medical genetics. For diseases with extensive locus heterogeneity – such as autism spectrum disorders (ASDs) – the signal from de novo mutations (DNMs) is distributed across many genes, making it difficult to distinguish disease-relevant mutations from background variation. We provide a statistical framework for the analysis of DNM excesses per gene and gene set by calibrating a model of de novo mutation. We applied this framework to DNMs collected from 1,078 ASD trios and – while affirming a significant role for loss-of-function (LoF) mutations – found no excess of de novo LoF mutations in cases with IQ above 100, suggesting that the role of DNMs in ASD may reside in fundamental neurodevelopmental processes. We also used our model to identify ~1,000 genes that are significantly lacking functional coding variation in non-ASD samples and are enriched for de novo LoF mutations identified in ASD cases