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BioMed Research International
Volume 2015, Article ID 647389, 11 pages
Research Article

Genetic Interactions Explain Variance in Cingulate Amyloid Burden: An AV-45 PET Genome-Wide Association and Interaction Study in the ADNI Cohort

1Institute of Biomedical Engineering, College of Automation, Harbin Engineering University, 145 Nantong Street, Harbin 150001, China
2Center for Bioinformatics, College of Automation, Harbin Engineering University, 145 Nantong Street, Harbin 150001, China
3College of Information Engineering, Northeast Dianli University, 169 Changchun Street, Jilin 132012, China
4Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 355 West 16th Street, Suite 4100, Indianapolis, IN 46202, USA
5Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, 410 West 10th Street, Suite 5000, Indianapolis, IN 46202, USA
6Department of Biohealth Informatics, Indiana University School of Informatics and Computing, Indianapolis, IN 46202, USA

Received 15 December 2014; Accepted 17 March 2015

Academic Editor: Dongchun Liang

Copyright © 2015 Jin Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Alzheimer’s disease (AD) is the most common neurodegenerative disorder. Using discrete disease status as the phenotype and computing statistics at the single marker level may not be able to address the underlying biological interactions that contribute to disease mechanism and may contribute to the issue of “missing heritability.” We performed a genome-wide association study (GWAS) and a genome-wide interaction study (GWIS) of an amyloid imaging phenotype, using the data from Alzheimer’s Disease Neuroimaging Initiative. We investigated the genetic main effects and interaction effects on cingulate amyloid-beta (A) load in an effort to better understand the genetic etiology of A deposition that is a widely studied AD biomarker. PLINK was used in the single marker GWAS, and INTERSNP was used to perform the two-marker GWIS, focusing only on SNPs with for the GWAS analysis. Age, sex, and diagnosis were used as covariates in both analyses. Corrected p values using the Bonferroni method were reported. The GWAS analysis revealed significant hits within or proximal to APOE, APOC1, and TOMM40 genes, which were previously implicated in AD. The GWIS analysis yielded 8 novel SNP-SNP interaction findings that warrant replication and further investigation.