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Identifying causal factors for Mendelian and common diseases is an ongoing challenge in medical genetics1. Population bottleneck events, such as those that occurred in the history of the Finnish population, enrich some homozygous variants to higher frequencies, which facilitates the identification of variants that cause diseases with recessive inheritance2,3. Here we examine the homozygous and heterozygous effects of 44,370 coding variants on 2,444 disease phenotypes using data from the nationwide electronic health records of 176,899 Finnish individuals. We find associations for homozygous genotypes across a broad spectrum of phenotypes, including known associations with retinal dystrophy and novel associations with adult-onset cataract and female infertility. Of the recessive disease associations that we identify, 13 out of 20 would have been missed by the additive model that is typically used in genome-wide association studies. We use these results to find many known Mendelian variants whose inheritance cannot be adequately described by a conventional definition of dominant or recessive. In particular, we find variants that are known to cause diseases with recessive inheritance with significant heterozygous phenotypic effects. Similarly, we find presumed benign variants with disease effects. Our results show how biobanks, particularly in founder populations, can broaden our understanding of complex dosage effects of Mendelian variants on disease.

On-line diagnosis system for power plant components and subprocesses is presented. The system is based on fault selective diagnosis algorithms applied to on-line process measurements. The algorithms calculate specific characteristics from monitored components in order to determine their operating state. User interface, system updating and system configuration are implemented on an expert system. The procedural algorithm level and the expert system level are separated into different computers. Different type of knowledge are divided into hierarchical levels in order to keep the system consistent, easily maintainable and fast enough for real time operation.

Complicated electro- mechanical systems require more progressive features for condition monitoring and operative fault diagnosis. Conventional representation and modelling methods for electronic control systems are not adequate for the implenintation of efficient and flesible fault diagnosis. Therefore, a new multi-model approach to fault diagnosis has been developed. Diagnostic functions are implemented by diagnostic tasks, which apply data and knowledge from the models of the mechatronic system and its control. The approach has remarkably shortened the development time of a fault diagnosis system and resulted in improved fault diagnosis features.