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Introduction The relevance of the cerebrospinal fluid (CSF) biomarkers for the diagnosis of Alzheimer's disease (AD) and related disorders is clearly established. However, the question remains on how to use these data which are often heterogeneous (not all biomarkers being pathological). The objective of this study is to propose to physicians in memory clinics a biological scale of probabilities that the patient with cognitive impairments has an Alzheimer's disease (AD) pathological process. Methods For that purpose, we took advantage of the multicenter data of our Paris-North, Lille, and Montpellier (PLM) study which has emerged through the initial sharing of information from these memory centers. Different models combining the CSF levels of amyloid-β 42, tau and p-tau(181) were tested to generate categories of patients with very low (<10%), low (<25%), high (>75%) and very high predictive values (>90%) for positive AD. A total of 1,273 patients (646 AD and 627 non-AD) from six independent memory clinic cohorts were included. Results A prediction model based on logistic regressions achieved a very good stratification of the population but had the disadvantages of needing mathematical optimization and being difficult to use in daily clinical practice. Remarkably, a simple and intuitive model based on the number (from 0 to 3) of three pathological CSF biomarkers resulted in a very efficient predictive scale for AD in patients seen in memory clinics. The scale's overall predictive value for AD for the different categories were: class 0: 9.6% (95% confidence interval, CI: 6.0-13.2%), class 1: 24.7% (95% CI: 18.0-31.3%), class 2: 77.2% (95% CI: 67.8-86.5%) and class 3: 94.2% (95% CI: 90.7-97.7%). In addition, with this scale, a significantly higher number of patients were correctly classified than with the logistic regression. Its superiority in model performance was validated by the computation of the net reclassification index (NRI). The model was also validated in an independent multicenter dataset of 408 patients (213 AD and 195 non-AD). Conclusions In conclusion, we defined a new scale which could be used to facilitate the interpretation, and routine use of multivariate CSF data, as well as helping the stratification of patients in clinical research trials. [ABSTRACT FROM AUTHOR]

Background: Whole exome sequencing (WES) has become the strategy of choice to identify a coding allelic variant for a rare human monogenic disorder. This approach is a revolution in medical genetics history, impacting both fundamental research, and diagnostic methods leading to personalized medicine. A plethora of efficient algorithms has been developed to ensure the variant discovery. They generally lead to ~20,000 variations that have to be narrow down to find the potential pathogenic allelic variant(s) and the affected gene(s). For this purpose, commonly adopted procedures which implicate various filtering strategies have emerged: exclusion of common variations, type of the allelics variants, pathogenicity effect prediction, modes of inheritance and multiple individuals for exome comparison. To deal with the expansion of WES in medical genomics individual laboratories, new convivial and versatile software tools have to implement these filtering steps. Non-programmer biologists have to be autonomous combining themselves different filtering criteria and conduct a personal strategy depending on their assumptions and study design. Results: We describe EVA (Exome Variation Analyzer), a user-friendly web-interfaced software dedicated to the filtering strategies for medical WES. Thanks to different modules, EVA (i) integrates and stores annotated exome variation data as strictly confidential to the project owner, (ii) allows to combine the main filters dealing with common variations, molecular types, inheritance mode and multiple samples, (iii) offers the browsing of annotated data and filtered results in various interactive tables, graphical visualizations and statistical charts, (iv) and finally offers export files and cross-links to external useful databases and softwares for further prioritization of the small subset of sorted candidate variations and genes. We report a demonstrative case study that allowed to identify a new candidate gene related to a rare form of Alzheimer disease. Conclusions: EVA is developed to be a user-friendly, versatile, and efficient-filtering assisting software for WES. It constitutes a platform for data storage and for drastic screening of clinical relevant genetics variations by nonprogrammer geneticists. Thereby, it provides a response to new needs at the expanding era of medical genomics investigated by WES for both fundamental research and clinical diagnostics. [ABSTRACT FROM AUTHOR]