Psychiatry Investig.
2015 Jan;12(1):125-135. 10.4306/pi.2015.12.1.125.
Gene Interactions and Structural Brain Change in Early-Onset Alzheimer's Disease Subjects Using the Pipeline Environment
- Affiliations
-
- 1Department of Psychiatry, Konkuk University School of Medicine, Chungju, Republic of Korea. hessem@naver.com
- 2Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA.
- 3Statistics Online Computational Resource, UMSM, University of Michigan, Ann Arbor, MI, USA.
- KMID: 2316398
- DOI: http://doi.org/10.4306/pi.2015.12.1.125
Abstract
OBJECTIVE
This article investigates subjects aged 55 to 65 from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to broaden our understanding of early-onset (EO) cognitive impairment using neuroimaging and genetics biomarkers.
METHODS
Nine of the subjects had EO-AD (Alzheimer's disease) and 27 had EO-MCI (mild cognitive impairment). The 15 most important neuroimaging markers were extracted with the Global Shape Analysis (GSA) Pipeline workflow. The 20 most significant single nucleotide polymorphisms (SNPs) were chosen and were associated with specific neuroimaging biomarkers.
RESULTS
We identified associations between the neuroimaging phenotypes and genotypes for a total of 36 subjects. Our results for all the subjects taken together showed the most significant associations between rs7718456 and L_hippocampus (volume), and between rs7718456 and R_hippocampus (volume). For the 27 MCI subjects, we found the most significant associations between rs6446443 and R_superior_frontal_gyrus (volume), and between rs17029131 and L_Precuneus (volume). For the nine AD subjects, we found the most significant associations between rs16964473 and L_rectus gyrus (surface area), and between rs12972537 and L_rectus_gyrus (surface area).
CONCLUSION
We observed significant correlations between the SNPs and the neuroimaging phenotypes in the 36 EO subjects in terms of neuroimaging genetics. However, larger sample sizes are needed to ensure that the effects will be detectable for a reasonable false-positive error rate using the GSA and Plink Pipeline workflows.
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