Late-onset Alzheimer's disease is the most common dementia type, yet no treatment exists to stop the neurodegeneration. Evidence from monogenic lysosomal diseases, neuronal pathology and experimental models suggest that autophagic and endolysosomal dysfunction may contribute to neurodegeneration by disrupting the degradation of potentially neurotoxic molecules such as amyloid-β and tau. However, it is uncertain how well the evidence from rare disorders and experimental models capture causal processes in common forms of dementia, including late-onset Alzheimer's disease. For this reason, we set out to investigate if autophagic and endolysosomal genes were enriched for genetic variants that convey increased risk of Alzheimer's disease; such a finding would provide population-based support for the endolysosomal hypothesis of neurodegeneration. We quantified the collective genetic associations between the endolysosomal system and Alzheimer's disease in three genome-wide associations studies (combined n = 62 415). We used the Mergeomics pathway enrichment algorithm that incorporates permutations of the full hierarchical cascade of SNP-gene-pathway to estimate enrichment. We used a previously published collection of 891 autophagic and endolysosomal genes (denoted as AphagEndoLyso, and derived from the Lysoplex sequencing platform) as a proxy for cellular processes related to autophagy, endocytosis and lysosomal function. We also investigated a subset of 142 genes of the 891 that have been implicated in Mendelian diseases (MenDisLyso). We found that both gene sets were enriched for genetic Alzheimer's associations: an enrichment score 3.67 standard deviations from the null model (P = 0.00012) was detected for AphagEndoLyso, and a score 3.36 standard deviations from the null model (P = 0.00039) was detected for MenDisLyso. The high enrichment score was specific to the AphagEndoLyso gene set (stronger than 99.7% of other tested pathways) and to Alzheimer's disease (stronger than all other tested diseases). The APOE locus explained most of the MenDisLyso signal (1.16 standard deviations after APOE removal, P = 0.12), but the AphagEndoLyso signal was less affected (3.35 standard deviations after APOE removal, P = 0.00040). Additional sensitivity analyses further indicated that the AphagEndoLyso Gene Set contained an aggregate genetic association that comprised a combination of subtle genetic signals in multiple genes. We also observed an enrichment of Parkinson's disease signals for MenDisLyso (3.25 standard deviations) and for AphagEndoLyso (3.95 standard deviations from the null model), and a brain-specific pattern of gene expression for AphagEndoLyso in the Gene Tissue Expression Project dataset. These results provide evidence that a diffuse aggregation of genetic perturbations to the autophagy and endolysosomal system may mediate late-onset Alzheimer's risk in human populations.

ObjectivesTo date, many artificial intelligence (AI) systems have been developed in healthcare, but adoption has been limited. This may be due to inappropriate or incomplete evaluation and a lack of internationally recognised AI standards on evaluation. To have confidence in the generalisability of AI systems in healthcare and to enable their integration into workflows, there is a need for a practical yet comprehensive instrument to assess the translational aspects of the available AI systems. Currently available evaluation frameworks for AI in healthcare focus on the reporting and regulatory aspects but have little guidance regarding assessment of the translational aspects of the AI systems like the functional, utility and ethical components.MethodsTo address this gap and create a framework that assesses real-world systems, an international team has developed a translationally focused evaluation framework termed ‘Translational Evaluation of Healthcare AI (TEHAI)’. A critical review of literature assessed existing evaluation and reporting frameworks and gaps. Next, using health technology evaluation and translational principles, reporting components were identified for consideration. These were independently reviewed for consensus inclusion in a final framework by an international panel of eight expert.ResultsTEHAI includes three main components: capability, utility and adoption. The emphasis on translational and ethical features of the model development and deployment distinguishes TEHAI from other evaluation instruments. In specific, the evaluation components can be applied at any stage of the development and deployment of the AI system.DiscussionOne major limitation of existing reporting or evaluation frameworks is their narrow focus. TEHAI, because of its strong foundation in translation research models and an emphasis on safety, translational value and generalisability, not only has a theoretical basis but also practical application to assessing real-world systems.ConclusionThe translational research theoretic approach used to develop TEHAI should see it having application not just for evaluation of clinical AI in research settings, but more broadly to guide evaluation of working clinical systems.

Large-scale epidemiological and population data provide opportunities to identify subgroups of people who are at risk of disease or exposed to adverse environments. Clustering algorithms are popular data-driven tools to identify these subgroups; however, relying exclusively on algorithms may not produce the best results if the dataset does not have a clustered structure. For this reason, we propose a framework (the R-library Numero) that combines the self-organizing map algorithm, permutation analysis for statistical evidence and a final expert-driven subgrouping step. We used Numero to define subgroups in two examples without an obvious clustering structure: a biomedical dataset of kidney disease and another dataset of community-level socioeconomic indicators. We benchmarked the Numero subgroupings against popular clustering algorithms (principal components, K-means and hierarchical clustering). The Numero subgroupings were more intuitive and easier to interpret without losing mathematical quality. Therefore, we expect Numero to be useful for exploratory analyses of population-based epidemiological datasets.

BackgroundIn therian mammals heteromorphic sex chromosomes are subject to meiotic sex chromosome inactivation (MSCI) during meiotic prophase I while the autosomes maintain transcriptional activity. The evolution of this sex chromosome silencing is thought to result in retroposition of genes required in spermatogenesis from the sex chromosomes to autosomes. In birds sex chromosome specific silencing appears to be absent and global transcriptional reductions occur through pachytene and sex chromosome-derived autosomal retrogenes are lacking. Egg laying monotremes are the most basal mammalian lineage, feature a complex and highly differentiated XY sex chromosome system with homology to the avian sex chromosomes, and also lack autosomal retrogenes. In order to delineate the point of origin of sex chromosome specific silencing in mammals we investigated whether MSCI exists in platypus.ResultsOur results show that platypus sex chromosomes display only partial or transient colocalisation with a repressive histone variant linked to therian sex chromosome silencing and surprisingly lack a hallmark MSCI epigenetic signature present in other mammals. Remarkably, platypus instead feature an avian like period of general low level transcription through prophase I with the sex chromosomes and the future mammalian X maintaining association with a nucleolus-like structure.ConclusionsOur work demonstrates for the first time that in mammals meiotic silencing of sex chromosomes evolved after the divergence of monotremes presumably as a result of the differentiation of the therian XY sex chromosomes. We provide a novel evolutionary scenario on how the future therian X chromosome commenced the trajectory toward MSCI.Electronic supplementary materialThe online version of this article (doi:10.1186/s12915-015-0215-4) contains supplementary material, which is available to authorized users.