Archived
This content is available here strictly for research, reference, and/or recordkeeping and as such it may not be fully accessible. If you work or study at University of Kentucky and would like to request an accessible version, please use the SensusAccess Document Converter.
Abstract
Cerebrospinal fluid (CSF) analytes harbor potential as diagnostic biomarkers for Alzheimer’s Disease (AD). Quantitative measures of CSF proteins comprise a set of often highly correlated endophenotypes that have previously shown promise in genetic analyses (Cruchaga et al., 2013; Kauwe et al., 2014). Pleiotropic impact of genetic variations on this set may provide additional insights into AD pathology at its earliest stages. To determine which specific endophenotypes are pleiotropic, one can employ methods based on the reverse regression of genotype on phenotypes. Recently, we proposed a method based functional linear models (Vsevolozhskaya et al, 2016) that utilizes reverse regression and simultaneously evaluates all variants within a genetic region for an association with multiple correlated phenotypes. Here we apply our novel methodology to explore pleiotropic effects of CSF analtyes using Alzheimer's Disease Neuroimaging Initiative (ADNI) data.
Document Type
Presentation
Publication Date
10-2016
Repository Citation
Vsevolozhskaya, Olga A.; Keren, Ilai; Fardo, David W.; and Zaykin, Dmitri V., "Pleiotropic Effects of CSF Levels of Alzheimer’s Disease Proteins" (2016). Biostatistics Presentations. 4.
https://uknowledge.uky.edu/biostatistics_present/4
Notes/Citation Information
A poster presentation at the American Society of Human Genetics 66th Annual Meeting in Vancouver, BC, Canada.