Genetic factors play a major role in frontotemporal dementia (FTD). The majority of FTD cannot be genetically explained yet and it is likely that there are still FTD risk loci to be discovered. Common variants have been identified with genome-wide association studies (GWAS), but these studies have not systematically searched for rare variants. To identify rare and new common variant FTD risk loci and provide more insight into the heritability of C9ORF72-related FTD, we performed a GWAS consisting of 354 FTD patients (including and excluding N = 28 pathological repeat carriers) and 4209 control subjects. The Haplotype Reference Consortium was used as reference panel, allowing for the imputation of rare genetic variants. Two rare genetic variants nearby C9ORF72 were strongly associated with FTD in the discovery (rs147211831: OR = 4.8, P = 9.2 × 10⁻⁹, rs117204439: OR = 4.9, P = 6.0 × 10⁻⁹) and replication analysis (P < 1.1 × 10⁻³). These variants also significantly associated with amyotrophic lateral sclerosis in a publicly available dataset. Using haplotype analyses in 1200 individuals, we showed that these variants tag a sub-haplotype of the founder haplotype of the repeat expansion that was previously found to be present in virtually all pathological C9ORF72 G₄C₂ repeat lengths. This new risk haplotype was 10 times more likely to contain a C9ORF72 pathological repeat length compared to founder haplotypes without one of the two risk variants (~22% versus ~2%; P = 7.70 × 10⁻⁵⁸). In haplotypes without a pathologic expansion, the founder risk haplotype had a higher number of repeats (median = 12 repeats) compared to the founder haplotype without the risk variants (median = 8 repeats) (P = 2.05 × 10⁻²⁶⁰). In conclusion, the identified risk haplotype, which is carried by ~4% of all individuals, is a major risk factor for pathological repeat lengths of C9ORF72 G₄C₂. These findings strongly indicate that longer C9ORF72 repeats are unstable and more likely to convert to germline pathological C9ORF72 repeat expansions.

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Edge computing can deliver substantial value to the general idea of the Internet of Things (IoT). However, there is a myriad of potential IoT applications for edge computing. Stakeholders are left with uncertainty about how the business potential of edge computing for these IoT applications can be identified. This research contributes in solving this, by designing a business model tool that can be used to identify the business model potential of edge computing for distinct IoT application areas, based on business model viability and feasibility. Through the Design Science Research Methodology (DSRM), the tool has been designed, demonstrated, and evaluated. Based on the STOF ontology, and supplemented by the theoretical domains of business ecosystems and platform theory, nine generic variables have been identified to explain business model viability and feasibility. These generic variables have in turn been contextualized towards the edge computing domain, in terms of 45 contextual input variables. This is the first research that unfolds these business model variables for edge computing.