Use of genetic variation as biomarkers for Alzheimer's disease

Abstract

Late-onset Alzheimer's disease (LOAD) is the most common cause of late-onset dementia in western societies. Despite remarkable achievements in human genetics throughout the years, in particular technological advances in gene mapping and in statistical methods that relate genetic variants to disease, to date only a small proportion of the genetic contribution to LOAD can be explained leaving several remaining genetic risk factors to be identified. A possible explanation for the difficulty in gene identification is that LOAD is a multifactorial complex disorder with both genetic and environmental components. Multiple genes with small effects each ("quantitative trait loci"[QTLs]) are likely to contribute to the quantitative traits associated with the disease, such as memory performance, amyloid/tau pathology, or hippocampal atrophy. The motivation for identifying the genetics of LOAD is clear. Not only could it shed light on disease pathogenesis, but it may also provide potential targets for effective treatment, screening, and prevention. Here, we review the usefulness of genetic variation as diagnostic tools and biomarkers in LOAD and discuss the potentials and difficulties researchers face in designing appropriate studies for gene discovery.

Conflict of interest statement

Conflicts of Interest

The authors declare no conflicts of interest.

Figures

Figure 1

Pooled odds ratios (95% CI) of the 40 studies included in the meta-analysis by Farrer et al. relating APOE genotype with LOAD (ε4 allele versus ε3 allele). †No data provided; ‡Hardy-Weinberg-Equilibrium (HWE) deviation in controls (P ≤ 0.05). (In color in Annals online.)

Figure 2

Protein sorting as a key mechanism in Alzheimer’s cell biology. (A) Aβ (red bar) is liberated from its parent protein, APP (multicolor bar), in two enzymatic steps. In the first β-cleavage step, BACE (blue bar) splits full-length APP into an sAPPβ fragment (brown bar) and a C-terminal fragment (CTFβ, red/green bar). Then, in the γ-cleavage step, the γ-secretase (star) splits CTFβ into Aβ (red bar) and amyloid intracellular domain (AICD, green bar). β-cleavage is the committed step in APP processing and may be upregulated in LOAD. (B) Both APP (red bar) and BACE (blue bar) are type-I transmembrane proteins that are sorted through multiple membranous compartments of the cell. The sorting triangle that interconnects the trans-Golgi network (TGN), cell surface, and the endosome is critically important for APP and BACE sorting. As indicated, clathrin is the coat complex that regulates transport from the cell surface and the TGN to the endosome, whereas the retromer is the coat complex that regulates transport from the endosome back to the TGN. APP and BACE interact within the membranes of the endosomal system, initiating the amyloidogenic pathway. (Illustration adapted from Small and Gandy.75) (In color in Annals online.)

Figure 3

Role of SORL1 in transmembrane sorting of APP. The green arrows track re-entry of APP from the cell surface when SORL1 is present. The red arrows show that, when SORL1 is absent, more APP moves into domains, such as the late endosome/lysosome, where the black arrows show how it is subsequently cut by beta-secretase (BACE1) and gamma-secretase (PS1 γ-sec), generating the neurotoxic amyloid beta-peptide (Aβ). (Illustration adapted from Rogaeva et al.77) (In color in Annals online.)