Predicting Alzheimer's disease using combined imaging-whole genome SNP data

Dehan Kong, Kelly S. Giovanello, Yalin Wang, Weili Lin, Eunjee Lee, Yong Fan, P. Murali Doraiswamy, Hongtu Zhu

Research output: Contribution to journalArticle

10 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)695-702
Number of pages8
JournalJournal of Alzheimer's Disease
Volume46
Issue number3
DOIs
StatePublished - Jun 25 2015

Fingerprint

Single Nucleotide Polymorphism
Alzheimer Disease
Genome
Neuroimaging
Genome-Wide Association Study
Biomarkers
Brain

Keywords

  • Alzheimer's disease
  • genetics
  • magnetic resonance imaging
  • proportional hazards models
  • risk

ASJC Scopus subject areas

  • Psychiatry and Mental health
  • Geriatrics and Gerontology
  • Clinical Psychology

Cite this

Predicting Alzheimer's disease using combined imaging-whole genome SNP data. / Kong, Dehan; Giovanello, Kelly S.; Wang, Yalin; Lin, Weili; Lee, Eunjee; Fan, Yong; Murali Doraiswamy, P.; Zhu, Hongtu.

In: Journal of Alzheimer's Disease, Vol. 46, No. 3, 25.06.2015, p. 695-702.

Research output: Contribution to journalArticle

Kong, D, Giovanello, KS, Wang, Y, Lin, W, Lee, E, Fan, Y, Murali Doraiswamy, P & Zhu, H 2015, 'Predicting Alzheimer's disease using combined imaging-whole genome SNP data', Journal of Alzheimer's Disease, vol. 46, no. 3, pp. 695-702. https://doi.org/10.3233/JAD-150164
Kong, Dehan ; Giovanello, Kelly S. ; Wang, Yalin ; Lin, Weili ; Lee, Eunjee ; Fan, Yong ; Murali Doraiswamy, P. ; Zhu, Hongtu. / Predicting Alzheimer's disease using combined imaging-whole genome SNP data. In: Journal of Alzheimer's Disease. 2015 ; Vol. 46, No. 3. pp. 695-702.
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