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Activity Number:
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324
- Applications of Functional Data Analysis to Medical Studies
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 1, 2017 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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Abstract #323970
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Title:
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Cerebrospinal Fluid Biomarker Signature in Alzheimer's Disease Genetic Association Landscape by Functional Linear Models.
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Author(s):
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Olga Vsevolozhskaya* and Ilai Keren and David Fardo and Dmitri Zaykin
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Companies:
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and Washington Department of Fish and Wildlife and University of Kentucky and NIH/NIEHS
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Keywords:
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FDA ;
association studies ;
Bayesian inference ;
gene-based analysis ;
Alzheimer's disease ;
ADNI
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Abstract:
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Cerebrospinal fluid (CSF) analytes are potential diagnostic biomarkers for Alzheimer's Disease (AD). Quantitative measures of CSF proteins comprise a set of highly correlated disease endophenotypes. Simultaneous impact of genetic variants on this set may provide additional insights into AD pathology. To determine which genes affect specific endophenotypes, one can employ methods that aggregate genetic signals across multiple variants within each gene. Recently, we proposed a method based on functional linear models that utilizes inverse regression and simultaneously evaluates all variants within a genetic region for an association with multiple correlated phenotypes. Within this framework, allelic effects of multiple variants are estimated as a smooth function varying over consecutive genetic positions in a Bayesian framework. Simulations designed to mimic genetic data structure indicate that Bayesian bands for the estimated smooth allelic effect function have good interval properties with reasonable computation time for moderate datasets. Here we apply our proposed methodology to explore genetic effects of CSL levels of AD-related proteins based on ADNI data.
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Authors who are presenting talks have a * after their name.
