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. 2019 Mar 1;9:3266. doi: 10.1038/s41598-019-39796-w

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© The Author(s) 2019

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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VarClass improvement of Risk Production for Intellectual Disability GSE7226-GPL2005 dataset. (A) ROC curve showing classification of intellectual disability and normal samples from GSE7226-GPL2005 dataset. Black and red lines denote logistic binomial regression classification when including and excluding VarClass protective variants. IDI [95% CI]: 0.0344 [0.011–0.058]; p-value: 3.5e-3. (B) Boxplot showing predicted risk mean and standard deviation for disease and control samples when including VarClass variants in the analysis. (C) Boxplot showing predicted risk mean and standard deviation for disease and control samples without including VarClass variants in the analysis. Discrimination slope provides a measure of quantification for the change in statistics. Showing a greater discrimination capacity between disease and normal samples when VarClass variants are included in the risk prediction model (−0.446) and a drop in discrimination slope (−0.439) when excluding the variants from the model. (D) The risk score distribution statistics for disease (grey histogram) and control (black histogram) including VarClass variants in the analysis.