This administrative supplement is focused on adding targeted research questions related to Downs Syndrome (DS) across the Vanderbilt Kennedy Center?s (VKC) IDDRC, primarily enhancing Aim 3 of the Biostatistics and Bioinformatics Core, and also Aim 2 of the Clinical Translational Core. The proposed supplement focuses on Component 2 of NOT-OD-18-194 by proposing to conduct comprehensive molecular and phenotypic analysis in DS. Research activities will capitalize on Vanderbilt?s 20+ year mineable database of electronic medical records (named the Synthetic Derivative; SD) and BioVU (de-identified DNA samples that can be linked to data in the SD). SD/BioVU data allow for phenotypic and biological markers of DS to be examined, including longitudinally, in order to understand the sequela of disorders/conditions and/or biological markers associated with DS. While these are rich datasets, they are complex and require unique considerations. To this end, we have developed tools and approaches to leverage the depth of this data in order to study Autism Spectrum Disorder (ASD). In the supplement, we will apply these already developed tools and approaches to DS. Such data could be used, for example, in a predictive analytics framework to identify the best predictors of needing heart surgery. Also, we will capitalize on the BioVU DNA repository and the ongoing extensive efforts of the Vanderbilt Genetics Institute (VGI) to use a novel genetic approach to identify genetic risk factors for congenital heart disease in DS. The genotyping data developed from this will effort will also be useful for future studies of genetic risk factors for other co-morbid conditions in DS. The proposed work fits within the overarching IDDRC Aims to:
(Aim 1, Overview) build an innovative research infrastructure [with] new technological or other advances to the cores and provide essential services not otherwise available to IDDRC scientists and (Aim 2, Overview) to improve the health, education, and well-being of people with autism, learning disabilities, acquired disabilities, and genetic syndromes. More explicitly, though, it directly enhances the Biostatistics and Bioinformatics Core?s Aim 3, which focuses on developing electronic medical record (EMR) and genomic databases in a range of IDD conditions, including identifying specific variables of interest in the EMR; designing and programming optimal algorithms for identifying patients of interest from the SD; and, linking these data to existing patient DNA sequence or genotype data.
Aim 3 of the Biostatistics and Bioinformatics Core crosses with the Clinical and Translational Core?s Aim 2 goal of facilitating recruitment and data mining of longitudinal social, cognitive, and medical phenotypes in IDDs. Thus, the proposed activities enhance existing IDDRC aims, but rest squarely within the scope of them. The proposed work is targeted to be completed within a one-year timeframe, and the expected outcomes are to: (1) determine phenotypic associations with DS; (2) determine longitudinal predictors of outcomes; (3) determine how genetic variants within DS are related to these outcomes; and (4) increase VKC DS Registry and DSConnect registrants.
This IDDRC administrative supplement application for Vanderbilt University's Intellectual and Developmental Disabilities Research Center (IDDRC), based in Vanderbilt's Kennedy Center (VKC), will use leverage existing electronic medical record information and biological samples to develop a deeper understanding of critical issues in Down Syndrome (DS) and provide and infrastructure for future analyses. Specifically, this work will: 1) identify unrecognized clinical features in DS and determine whether there are clinical features that differ between sexes and demographic groups; 2) develop predictive models based on EMR data for risk of clinical features or interventions in DS; 3) Identify genetic risk factors for congenital heart disease in DS. Finally, we will expand the prospective data collection in order to allow for future substantial and substantive DS research.
| Wilkey, Eric D; Cutting, Laurie E; Price, Gavin R (2018) Neuroanatomical correlates of performance in a state-wide test of math achievement. Dev Sci 21: |
| Gao, Yurui; Schilling, Kurt G; Stepniewska, Iwona et al. (2018) Tests of cortical parcellation based on white matter connectivity using diffusion tensor imaging. Neuroimage 170:321-331 |
| West, Kathryn L; Kelm, Nathaniel D; Carson, Robert P et al. (2018) Myelin volume fraction imaging with MRI. Neuroimage 182:511-521 |
| Williams, Zachary J; Failla, Michelle D; Gotham, Katherine O et al. (2018) Psychometric Evaluation of the Short Sensory Profile in Youth with Autism Spectrum Disorder. J Autism Dev Disord 48:4231-4249 |
| Bermudez, Camilo; Plassard, Andrew J; Davis, Taylor L et al. (2018) Learning Implicit Brain MRI Manifolds with Deep Learning. Proc SPIE Int Soc Opt Eng 10574: |
| Juárez, A Pablo; Weitlauf, Amy S; Nicholson, Amy et al. (2018) Early Identification of ASD Through Telemedicine: Potential Value for Underserved Populations. J Autism Dev Disord 48:2601-2610 |
| Thurm, Audrey; Powell, Elizabeth M; Neul, Jeffrey L et al. (2018) Loss of skills and onset patterns in neurodevelopmental disorders: Understanding the neurobiological mechanisms. Autism Res 11:212-222 |
| Schilling, Kurt G; Janve, Vaibhav; Gao, Yurui et al. (2018) Histological validation of diffusion MRI fiber orientation distributions and dispersion. Neuroimage 165:200-221 |
| Bottema-Beutel, Kristen; Malloy, Caitlin; Lloyd, Blair P et al. (2018) Sequential Associations Between Caregiver Talk and Child Play in Autism Spectrum Disorder and Typical Development. Child Dev 89:e157-e166 |
| Key, Alexandra P; Dykens, Elisabeth M (2018) Eye Tracking as a Marker of Hyperphagia in Prader-Willi Syndrome. Dev Neuropsychol 43:152-161 |
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