Despite the widespread use of genotype imputation tools and the availability of different approaches, late developments of currently used programs have not been compared comprehensively. We therefore assessed the performance of 35 combinations of phasing and imputation programs, including versions of SHAPEIT, Eagle, Beagle, minimac, PBWT, and IMPUTE, for genetic imputation of completely missing SNPs with a HRC reference panel regarding quality and speed. We used a data set comprising 1,149 fully sequenced individuals from the German population, subsetting the SNPs to approximate the Illumina Infinium-Omni5 array. Five hundred fifty-three thousand two hundred and thirty-four SNPs across two selected chromosomes were utilized for comparison between imputed and sequenced genotypes. We found that all tested programs with the exception of PBWT impute genotypes with very high accuracy (mean error rate < 0.005). PBTW hardly ever imputes the less frequent allele correctly (mean concordance for genotypes including the minor allele <0.0002). For all programs, imputation accuracy drops for rare alleles with a frequency <0.05. Even though overall concordance is high, concordance drops with genotype probability, indicating that low genotype probabilities are rare. The mean concordance of SNPs with a genotype probability <95% drops below 0.9, at which point disregarding imputed genotypes might prove favorable. For fast and accurate imputation, a combination of Eagle2.4.1 using a reference panel for phasing and Beagle5.1 for imputation performs best. Replacing Beagle5.1 with minimac3, minimac4, Beagle4.1, or IMPUTE4 results in a small gain in accuracy at a high cost of speed.

In self-adapting embedded real-time systems, operating systems and software provide mechanisms to self-adapt to changing requirements. Autonomous adaptation decisions introduce novel risks as they may lead to unforeseen system behavior that could not have been specified within a design-time model. However, as part of its functionality the operating system has to ensure the reliability of the entire self-x system during run-time.In this paper, we present our work in progress for an operating system framework which aims to identify anomalous or malicious system states at run-time without a sophisticated specificationtime model. Inspired by the Artificial Immune Systems Danger Theory, we propose an anomaly detection mechanism that operates not only on the local system behavior information of the monitored component. Furthermore, to ensure an efficient behavior evaluation, the anomaly detection mechanism implies system-wide input signals that indicate e.g the existence of a potential danger within the overall system or the occurrence of a system adaption. Due to the ability of this framework to cope with dynamically changing behavior and to identify unintended behavioral deviations, it seems to be a promising approach to enhance the run-time dependability of a self-x system.

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