title: Predicting Alzheimer's Disease Using Combined Imaging-Whole Genome SNP Data.
abstract: The growing public threat of Alzheimer's disease (AD) has raised the urgency to discover and validate prognostic biomarkers in order to predicting time to onset of AD. It is anticipated that both whole genome single nucleotide polymorphism (SNP) data and high dimensional whole brain imaging data offer predictive values to identify subjects at risk for progressing to AD. The aim of this paper is to test whether both whole genome SNP data and whole brain imaging data offer predictive values to identify subjects at risk for progressing to AD. In 343 subjects with mild cognitive impairment (MCI) enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI-1), we extracted high dimensional MR imaging (volumetric data on 93 brain regions plus a surface fluid registration based hippocampal subregion and surface data), and whole genome data (504,095 SNPs from GWAS), as well as routine neurocognitive and clinical data at baseline. MCI patients were then followed over 48 months, with 150 participants progressing to AD. Combining information from whole brain MR imaging and whole genome data was substantially superior to the standard model for predicting time to onset of AD in a 48-month national study of subjects at risk. Our findings demonstrate the promise of combined imaging-whole genome prognostic markers in people with mild memory impairment.
authors: Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA.
authors: Dehan
authors: D
authors: Kong
authors: Department of Psychology, University of North Carolina, Chapel Hill, NC, USA.|Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA.
authors: Kelly S
authors: KS
authors: Giovanello
authors: School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
authors: Yalin
authors: Y
authors: Wang
authors: Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA.|Department of Radiology, University of North Carolina, Chapel Hill, NC, USA.
authors: Weili
authors: W
authors: Lin
authors: Department of Statistics, University of North Carolina, Chapel Hill, NC, USA.
authors: Eunjee
authors: E
authors: Lee
authors: Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
authors: Yong
authors: Y
authors: Fan
authors: Departments of Psychiatry and Duke Institute for Brain Sciences, Duke University, Durham, NC, USA.
authors: P
authors: P
authors: Murali Doraiswamy
authors: Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA.|Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA.|Department of Radiology, University of North Carolina, Chapel Hill, NC, USA.
authors: Hongtu
authors: H
authors: Zhu
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000150:complications / Q000175:diagnosis* / Q000235:genetics*; D053327:Apolipoprotein E4 / Q000235:genetics; D060825:Cognitive Dysfunction / Q000209:etiology; D005260:Female; D056726:Genetic Association Studies; D016678:Genome / Q000235:genetics*; D006624:Hippocampus / Q000473:pathology*; D006801:Humans; D008279:Magnetic Resonance Imaging; D008297:Male; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D025341:Principal Component Analysis; D011569:Psychiatric Status Rating Scales
chemical_list: D053327:Apolipoprotein E4
keywords: Alzheimer’s disease; genetics; magnetic resonance imaging; proportional hazards models; risk