Affiliations: [a] Department of Neurology, University at Buffalo, SUNY, Buffalo, NY, USA | [b] Alzheimer's Disease and Memory Disorders Center, Department of Neurology, Baylor College of Medicine, Houston, TX, USA | [c] Department of Bioinformatics, University at Buffalo, SUNY, Buffalo, NY, USA | [d] Roswell Park Cancer Institute, Buffalo, NY, USA
Correspondence:
[*]
Correspondence to: Kinga Szigeti, MD, PhD, University of Buffalo SUNY, 100 High Street, Buffalo, NY 14203, USA. Tel.: +1 716 859 3484; Fax: +1 716 859 7833; E-mail: szigeti@buffalo.edu.
Abstract: Genetic heterogeneity is a common problem for genome-wide association studies of complex human diseases. Ordered-subset analysis (OSA) reduces genetic heterogeneity and optimizes the use of phenotypic information, thus improving power under some disease models. We hypothesized that in a genetically heterogeneous disorder such as Alzheimer's disease (AD), utilizing OSA by age at onset (AAO) of AD may increase the power to detect relevant loci. Using this approach, 8 loci were detected, including the chr15 : 30,44 region harboring CHRFAM7A. The association was replicated in the NIA-LOAD Familial Study dataset. CHRFAM7A is a dominant negative regulator of CHRNA7 function, the receptor that facilitates amyloid-β1-42 internalization through endocytosis and has been implicated in AD. OSA, using AAO as a quantitative trait, optimized power and detected replicable signals suggesting that AD is genetically heterogeneous between AAO subsets.
Keywords: Age at onset, Alzheimer's disease, copy number variation
