Predicting Alzheimer’s Disease Using Combined Imaging-Whole Genome SNP Data

Article type: Research Article

Authors: Kong, Dehan^{a; 1} | Giovanello, Kelly S.^{b; c; 1} | Wang, Yalin^d | Lin, Weili^{c; e} | Lee, Eunjee^f | Fan, Yong^g | Murali Doraiswamy, P^{h; 2} | Zhu, Hongtu^{a; c; e; 2; *} | and for the Alzheimer’s Disease Neuroimaging Initiative³

Affiliations: [a] Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA | [b] Department of Psychology, University of North Carolina, Chapel Hill, NC, USA | [c] Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA | [d] School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA | [e] Department of Radiology, University of North Carolina, Chapel Hill, NC, USA | [f] Department of Statistics, University of North Carolina, Chapel Hill, NC, USA | [g] Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA | [h] Departments of Psychiatry and Duke Institute for Brain Sciences, Duke University, Durham, NC, USA

Correspondence: [*] Correspondence to:Hongtu Zhu, 3105-C McGavran-Greenberg Hall, UNC Gillings School of Global Public Health, 135 Dauer Drive, Campus Box 7420, Chapel Hill, NC 27599-7420, USA. Tel.: +1 919 966 7272; Fax: +1 919 966 3804; E-mail: htzhu@email.unc.edu

Note: [1] These authors contributed equally to this work.

Note: [2] These authors are co-senior authors of this work.

Note: [3] Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNIinvestigators can be found at: https://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

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.

Keywords: Alzheimer’s disease, genetics, magnetic resonance imaging, proportional hazards models, risk

DOI: 10.3233/JAD-150164

Journal: Journal of Alzheimer's Disease, vol. 46, no. 3, pp. 695-702, 2015