The genetic architecture of Alzheimer's disease: beyond APP, PSENs and APOE

Abstract

Alzheimer's disease (AD) is a complex disorder with a clear genetic component. Three genes have been identified as the cause of early onset familial AD (EOAD). The most common form of the disease, late onset Alzheimer's disease (LOAD), is, however, a sporadic one presenting itself in later stages of life. The genetic component of this late onset form of AD has been the target of a large number of studies, because only one genetic risk factor (APOE4) has been consistently associated with the disease. However, technological advances allow new approaches in the study of complex disorders. In this review, we discuss the new results produced by genome wide association studies, in light of the current knowledge of the complexity of AD genetics.

Figure 1. Predicted interactions for Clu (A), Picalm (B) and CR1 (C)

The software STRING 8.0 (available at http://string.embl.de/newstring_cgi/show_input_page.pl?UserId=75H_lKjgP5Xi&sessionId=9vx3bNEK6tmB) was used to establish a network of predicted interactions for Clu (A), Picalm (B) and CR1 (C) proteins. Accessed on August 2009. STRING is a database of known and predicted protein interactions. The interactions include direct (physical) and indirect (functional) associations; they are derived from four sources: genomic context, high-throughput experiments, (conserved) coexpression and previous knowledge. STRING quantitatively integrates interaction data from these sources for a large number of organisms, and transfers information between these organisms where applicable. The database currently covers 2,483,276 proteins from 630 organisms. From the three represented proteins, CLU is the one with a more direct relation with AD: APOE is directly connected to CLU. Interestingly, CLU also interacts with proteins present in CR1 network (like C3). No other genes consistently associated with AD are present in PICALM or CR1 networks, indicating that these proteins may be involved in new pathobiological pathways.

Figure 2. Genetic variability and the role of the complement system in AD

Aβ and probably NFTs are able to activate the classical and alternative complement pathways. These are regulated by several membrane (m) and soluble (s) proteins at different stages. The genetic variability in CR1 is now known to be associated with the risk of developing AD. Increased numbers of samples are needed to know if the same is true for any other component of these pathways. Drawn from the work of Tenner [Tenner 2001] and McGeer and McGeer [McGeer and McGeer 2002]