In the present review, we look back at the recent history of GWAS (genome-wide association studies) in AD (Alzheimer's disease) and integrate the major findings with current knowledge of biological processes and pathways. These topics are essential for the development of animal models, which will be fundamental to our complete understanding of AD.

Introduction

The recent advances in AD (Alzheimer's disease) genetics brought several new players to the pathways involved in this already complex disease. Currently, many groups are working to understand the individual role of these new loci in the disease and how they come together with the factors already known to contribute to the pathological process. Historically, AD models have been based on the finding of APP (amyloid precursor protein), PSEN1 (presenilin 1) and PSEN2 (presenilin 2) mutations in early-onset disease and (to a lesser extent) on the finding of MAPT mutations in tangle disorders. This knowledge resulted in our ability to robustly model plaque pathology and, by crossing with MAPT mice, to model increased tangle formation and cell loss. However, this is of course a highly artificial strategy involving the creation of mice which massively overexpress multiple mutant proteins. Nonetheless, although no model totally replicates the human disease, transgenic mice and other animal models have provided insight into the basic mechanisms underlying disease aetiology as well as serving as an experimental platform for testing new therapeutic compounds [1]. The relevance of these models to the typical sporadic disease is difficult to assess in the absence of a clear predictive value in clinical trials.

In the present review, we discuss the recent genetic findings in GWAS (genome-wide association studies) and try to understand how these findings come together to elucidate the aetiology and pathogenesis of the typical late-onset disorder.

Brief overview of AD genetics: focus on the recent GWAS results

AD is a genetically complex disorder: rare families presenting with early-onset autosomal dominant AD are known to harbour mutations in APP, PSEN1 or PSEN2 genes; on the other hand, the very common form of the disease is sporadic and with a late onset. The genetic background of this common form of AD has been much more complicated to understand. The only well-established key has been the APOE gene [encoding ApoE (apolipoprotein E)] since the ϵ4 allele was reported to represent an increased risk of developing AD: a 3-fold increase in risk of ϵ4 heterozygous and up to 15-fold for ϵ4 homozygous when compared with homozygosity for the ϵ3 allele [2]. It was in 2007, with the advent of genome-wide technologies able to genotype millions of SNPs (single nucleotide polymorphisms) in several thousand samples, that new advances were made. Looking back at the recent history of GWAS in AD we can easily distinguish its several stages: a first wave of smaller genome-wide studies reporting several loci as associated with AD (most of these still today lacking replication) that were very sound in claiming that additional common variation with an effect size similar to that of APOE does not exist [312]. These were followed by a few studies using large numbers of samples (>2000 cases and 2000 controls) that were able to achieve genome-wide significance and identify small genetic risk factors with OR (odds ratio) ~1.2 as CLU, PICALM and CR1 [1315] (for extensive reviews of these studies see [16,17]). Despite the fact that the replication of GWAS hits by subsequent GWAS and meta-analyses are becoming increasingly complicated with the use of complex data-pooling designs that obscure the repetitive use of the same cohorts by different groups [18], these three loci have been replicated in independent samples and are now considered to be true susceptibility loci for AD [15,1921].

Additionally, several other suggestive loci have been reported such as BIN1 and EXOC3L2/BLOC1S3/MARK4 [15,20]. From these, only BIN1 that has been replicated in independent samples seems to be a true risk factor for AD [15,20,22,23]. More recently, two GWAS combining very large numbers of samples have identified five additional loci: EPHA1, MS4A, CD33, CD2AP and ABCA7. Different groups had previously identified some of these loci as suggestive, but they lacked genome-wide significance until now: the MS4A locus was previously identified by Harold et al. [13] and the EPHA1 locus was identified by Seshadri et al. [15]. Bertram et al. [5] also reported the CD33 locus in chromosome 19 as genome-wide significant in a family-based association study (Table 1).

Table 1
Genetic loci identified by the largest GWAS in AD
Locus SNP OR p-value Potential pathways 
APOE rs2075650 2.53 (2.41–2.66)* 1.0×10−295Cholesterol/lipid metabolism 
CLU rs11136000 0.87 (0.84–0.89)* 9.9×10−19Immune and complement systems/inflammatory response; cholesterol/lipid metabolism 
PICALM rs3851179 0.87 (0.84–0.90)* 4.1×10−15Endocytic pathways 
 rs541458 0.87 (0.83–0.90)* 9.3×10−12 
CR1 rs3818361 1.18 (1.13–1.23)* 1.0×10−11Immune and complement systems/inflammatory response 
 rs6656401 3.5×10−9†   
BIN1 rs744373 1.15 (1.11–1.20)* 1.4×10−12Endocytic pathways 
EPHA1 rs11767557 0.87 (0.83–0.91)‡ 4.9×10−8‡ Immune and complement systems/inflammatory response? 
MS4A rs4938933 0.88 (0.85–0.92)‡ 1.7×10−9‡ Immune and complement systems/inflammatory response? 
CD33 rs3865444 0.89 (0.86–0.93)‡ 2.0×10−7‡ Immune and complement systems/inflammatory response? 
CD2AP rs9349407 1.12 (1.07–1.117)‡ 2.1×10−6‡ Endocytic pathways; Immune and complement systems/inflammatory response? 
ABCA7 rs3752246rs3764650 1.15 (1.09–1.21)‡ 5×10−7‡4.5×10−17§ Cholesterol/lipid metabolism; Immune and complement systems/inflammatory response? 
Locus SNP OR p-value Potential pathways 
APOE rs2075650 2.53 (2.41–2.66)* 1.0×10−295Cholesterol/lipid metabolism 
CLU rs11136000 0.87 (0.84–0.89)* 9.9×10−19Immune and complement systems/inflammatory response; cholesterol/lipid metabolism 
PICALM rs3851179 0.87 (0.84–0.90)* 4.1×10−15Endocytic pathways 
 rs541458 0.87 (0.83–0.90)* 9.3×10−12 
CR1 rs3818361 1.18 (1.13–1.23)* 1.0×10−11Immune and complement systems/inflammatory response 
 rs6656401 3.5×10−9†   
BIN1 rs744373 1.15 (1.11–1.20)* 1.4×10−12Endocytic pathways 
EPHA1 rs11767557 0.87 (0.83–0.91)‡ 4.9×10−8‡ Immune and complement systems/inflammatory response? 
MS4A rs4938933 0.88 (0.85–0.92)‡ 1.7×10−9‡ Immune and complement systems/inflammatory response? 
CD33 rs3865444 0.89 (0.86–0.93)‡ 2.0×10−7‡ Immune and complement systems/inflammatory response? 
CD2AP rs9349407 1.12 (1.07–1.117)‡ 2.1×10−6‡ Endocytic pathways; Immune and complement systems/inflammatory response? 
ABCA7 rs3752246rs3764650 1.15 (1.09–1.21)‡ 5×10−7‡4.5×10−17§ Cholesterol/lipid metabolism; Immune and complement systems/inflammatory response? 
*

From Hollingworth P. et al. (meta-analysis results) [24].

From Lambert J. et al. [14].

From Alzheimer Disease Genetics Consortium [25].

§

From Hollingworth P. et al. [26].

As predicted, we are currently moving towards the third-generation GWAS where comprehensive bioinformatical and statistical analyses are elucidating some aspects of the genetic associations. Additionally, several endophenotypes have been used to replicate the original GWAS findings and to shed some light on the relation between the newly identified loci and several features of AD.

Although the first-generation GWAS in AD indicated that psychiatric and cognitive function used as endophenotypes do not provide a powerful means of mapping disease loci [27,28], which was not completely a surprise given the extensively negative results of GWAS in several psychiatric disorders [29,30], the incorporation of clinical and neuropathological information into GWAS has proven successful. Several studies have now reported on data from the ADNI (Alzheimer's Disease Neuroimaging Initiative) database, which is ideal for endophenotype studies in AD, since it brings together MRI (magnetic resonance imaging), PET (positron emission tomography), genetic factors, other biological markers and clinical and neuropsychological assessments of the same samples. In general, these studies intended to identify specific genetic variants influencing different brain structure measures [3133]. More specifically, Biffi et al. [22] assessed the role of genome-wide variability, regarding phenotypes as hippocampal volume, amygdala volume, white-matter lesion volume, entorhinal cortex thickness, parahippocampal gyrus thickness and temporal lobe-cortex thickness. They were able to establish APOE as associated with all the mentioned phenotypes except white-matter lesions and at the same time validate already-known variants in CR1 and PICALM and identify two novel loci as associated with multiple MRI characteristics (BIN1 and CNTN5) [22].

Two groups have now reported on the effects of whole genome variability in cerebrospinal fluid AD-associated markers assessed in the ADNI cohort. Han et al. [34] reported that in normal subjects CYP19A1, NCAM2 and ARL5B were associated with Aβ1–42 (Aβ is amyloid β-peptide) levels. In AD subjects, ApoE E2/E3 and E2/E4 alleles were associated with elevated total tau levels, whereas ApoE E4/E4 alleles were associated with elevated levels of total tau and phosphorylated tau [34]. Kim et al. [35] used the same cohort to test the same thing and reported four genes reaching genome-wide significance (APOE, LOC100129500, TOMM40 and EPC2) and several potential candidates (CCDC134, ABCG2, SREBF2 and NFATC4). The fact that all ADNI data are publicly available allows different groups to work on the same data improving publication speed and quality. However, it also generates confusion when several groups report different results when performing the same tests on the same data. This clearly calls for clarity and thoroughness when describing the samples and methodologies used and for an easy way of comparing all these studies (maybe a study section dedicated to these aspects would be useful in the ADNI webpage http://adni.loni.ucla.edu/). The most obvious subphenotypic information to be used in AD studies is the age at disease onset, as segregation analyses have shown that loci other than ApoE influence age of developing AD [36]. Until now, no genome-wide study has established a significant association of this kind. From these first reports (and similar to the first-generation GWAS), it is evident that in order to obtain statistical significance, studies involving endophenotypes in AD will need to include larger numbers of samples.

Increasing sample size, performing better statistical analyses and fine-tuning the phenotypes of each study will probably result in an increase in the number of new AD loci during the next year. Even so, it is already possible to infer that the newly identified loci are not completely independent and seem to fit into three wide pathways: immune and complement systems/inflammatory response; endocytic pathways; and cholesterol and lipid metabolism [3739].

The complement system and inflammatory response (CR1 and CLU)

The idea that inflammation is associated with AD is not new; the speculation that AD may be an immunological disorder has been around since early 1980s and epidemiological surveys of the effects of anti-inflammatory treatment in AD indicate that non-steroidal anti-inflammatory medication may have an effect on disease susceptibility [40]. Accordingly, the work of McGeer and McGeer [41], Tenner and co-workers [42] and several other authors have alerted the field to the important role of inflammation, the complement and the immune systems in AD. What has now become evident is the fact that genetic data point towards a primary role of inflammation in the development of AD in contrast with the hypothesis that immune and inflammatory related processes are secondary to AD occurrence. We now know that two genes that code for proteins acting as regulators of the complement system (CLU and CR1) are risk factors for the development of the most common form of AD.

Clusterin is a lipoprotein expressed in most mammalian tissues. It interacts with a variety of molecules and has been proposed to be involved in a number of physiological processes, including inhibition of the complement system. Clusterin is able to modulate the MAC (membrane attack complex) and can act by preventing the inflammatory response associated with the complement activation after protein aggregation [43].

CR1 is a polymorphic protein that also acts as a negative regulator of the complement system (inhibiting both the classical and alternative pathways). CR1 on erythrocytes acts as a vehicle for clearance of C3b-coated immune complexes, being involved in immune adherence and phagocytosis [44].

It is worth noting that other GWAS-identified loci such as BIN1, MS4A gene family, CD33, ABCA7, CD2AP and EPHA1 may also be potentially related to the immune and inflammatory responses. BIN1 knockout mosaic mice have been reported to show reduced inflammation with aging [45]. The MS4A locus contains several genes belonging to the membrane-spanning four-domains subfamily A, which have been identified after recognition of similarities in their structure to CD20 [46]. This family is relatively poorly characterized at the protein level; however, MS4A1 (CD20) has been demonstrated to have a function in regulating calcium influx downstream of the activated B-cell antigen receptor and consequently members of this protein family are expected to have similar roles in the immune response [47]. CD33 [also known as Siglec (sialic acid-binding Ig-like lectin)-3] encodes a cell-surface receptor on cells of monocytic or myeloid lineage. It is a member of the Siglec family of lectins that bind sialic acid and regulate the innate immune system via the activation of caspase-dependent and caspase-independent cell-death pathways [48]. ABCA7 is known to modulate phagocytosis of apoptotic cells by macrophages mediated through the C1q complement-receptor protein on the apoptotic cell surface [49]. CD2AP gene encodes a scaffolding molecule involved in cytoskeletal reorganization and intracellular trafficking that is required for the formation of the immunological synapse [50]. EPHA1 is a member of the ephrin receptor subfamily with main functions in cell and axon guidance as well as synaptic plasticity, but that may also have roles in inflammation [5153].

Endocytic pathways (BIN1, PICALM and CD2AP)

Endocytosis is crucial for several cellular functions. Eukaryotic cells use multiple pathways for the endocytic entry of molecules (mostly lipids and proteins) at the plasma membrane. The best characterized of these pathways is the clathrin-mediated endocytosis [54].

This pathway is not only essential for synapses but also for APP processing with subsequent β-amyloid formation. Endocytic pathway activation has been shown to be a prominent and early feature of neurons in vulnerable regions of the brain in sporadic AD [55]. Both BIN1 and PICALM are directly involved in clathrin-mediated endocytosis [56], clearly pointing to a primary role of this pathway in AD. BIN1 encodes members of the BAR (Bin/amphiphysin/Rvs) adapter family that have been implicated in membrane dynamics, such as vesicle fusion and trafficking, specialized membrane organization and actin organization [57,58].

PICALM recruits clathrin and AP-2 (adaptor protein 2) to the plasma membrane and, along with AP-2, recognizes target proteins. The attached clathrin triskelions cause membrane deformation around the target proteins enclosing them within clathrin-coated vesicles to be processed in lysosomes or endosomes [59]. PICALM is also essential in the fusion of synaptic vesicles to the presynaptic membrane by directing the trafficking of VAMP2 (vesicle-associated membrane protein 2) [60].

Changes in the expression of either one or both of these genes may modulate neuronal function and behaviour. It is possible that the gain or loss of BIN1 and PICALM alleles would contribute to modifying AD pathology. It is interesting to note that knockout mice of PICALM have no overt neurological phenotype, but show dysfunctional haemopoiesis and abnormal iron metabolism, which may directly relate to APP [61,62]. On the other side, the loss of amphiphysin 1 in amphiphysin 1 knockout mice causes a parallel reduction of BIN1, selectively in the brain, with mice exhibiting defects in synaptic-vesicle recycling that correlate with increased mortality due to rare irreversible seizures and with major learning deficits, suggesting a critical role of amphiphysin for higher brain functions [63]. It will be important to understand how BIN1, PICALM and APP interact.

Additionally, it is also interesting to note the potential involvement of CD2AP in intracellular trafficking, since a role for CD2AP in endocytosis was suggested by its association with Cbl and the key endocytic proteins, endophilin and synaptojanin. In cells from CD2AP-deficient mice the receptor trafficking to lysosomes was reported to be impaired, suggesting that CD2AP may be important in regulating vesicular trafficking to the lysosome, particularly in early stages of the formation of multivesicular bodies [64,65].

Cholesterol and lipid metabolism (APOE, CLU and ABCA7)

Epidemiological studies have implicated hypercholesterolaemia in mid-life as a risk factor for AD, and have indicated that cholesterol lowering medications may have a protective effect against the development of dementia [66]. ApoE was identified as a risk factor for AD in the early 1990s, but it is still not clear how it contributes to disease risk and progression [67]. Nonetheless, it is known to have a fundamental role as a cholesterol carrier in the brain: the brain requires de novo cholesterol synthesis, which occurs in microglia and astrocytes. Cholesterol then needs to be transported to neurons and oligodendrocytes and this occurs via ApoE particles [68]. Additionally, membrane cholesterol promotes Aβ production, whereas Aβ40 inhibits cholesterol synthesis [69]. Clusterin is, like ApoE, a lipoprotein. It circulates in plasma as a high-density lipoprotein complex with ApoA1 (apolipoprotein A-I), which may serve not only as an inhibitor of the lytic terminal complement cascade but also as a regulator of lipid transport and local lipid redistribution [70].

ABCA7 is part of the ABC transporter superfamily and this family of proteins is known to have roles in transporting substrates across cell membranes [71]. ABCA7 is highly expressed in brain, particularly in hippocampal CA1 neurons and in microglia [72]. It is known to be involved in the efflux of lipids from cells to lipoprotein particles and, in this way, may interact with the effects of ApoE and clusterin in AD. It has also been reported to regulate APP processing, inhibiting β-amyloid secretion in cultured cells overexpressing APP [73].

Amyloid clearance

As mentioned above, ApoE has an important role in cholesterol metabolism and this role may involve the systemic clearance of Aβ. Aβ in the blood is transported in cholesterol-rich HDL (high-density lipoprotein) particles, which have ApoA1 or ApoE as associated lipoproteins, before elimination by the liver [74]. The lipidation state of ApoE is critical to the transport of Aβ across the blood–brain barrier with some studies reporting that ApoE4 is the least efficient transporter [75]. Clusterin is able to bind soluble Aβ through a specific, reversible and high-affinity interaction in cerebrospinal fluid [76,77] to form complexes that are able to cross the blood–brain barrier by a high-affinity receptor-mediated process involving transcytosis [78]. Reduced levels of ApoE and increased levels of clusterin have been correlated with the number of E4 alleles, suggesting a compensatory induction of clusterin in the brain of AD individuals with the E4 allele of ApoE presenting low brain levels of ApoE [79]. Thus clusterin may modulate Aβ clearance from the brain in concert with ApoE. It has been proposed that binding of C1q to misfolded proteins in early AD, together with C4-binding protein (which decreases the activation of MAC providing the required physiological system balance) enables clearance of misfolded Aβ-associated material [80]. Several complement components have been detected in AD amyloid plaques [81] and the phagocytotic action of both microglia and blood-derived macrophages has been implicated in Aβ clearance [82]. In fact, the accumulation of extracellular plaques is an immunologically interesting phenomenon since microglia should be able to clean the aggregating material from brain. After the identification of CD33 locus as a risk factor for AD, the theory postulating that aggregating amyloid plaques are masked in AD by sialylated glycoproteins and gangliosides became more noteworthy. Microglial cells contain only one type of Siglec receptor (Siglec-11), which mediates immunosuppressive signals and inhibits the function of other microglial pattern recognition receptors, such as TLRs (Toll-like receptors). In this way, binding of CD33 related Siglec-11 to highly sialylated proteins and lipids such as clusterin, ApoE and gangliosides that are abundantly present in AD plaques could in turn mediate an inhibitory signalling cascade. Thereby, microglial phagocytosis is possibly suppressed and the AD plaques might be left untouched [83].

Current limitations of the GWAS approaches

Although it is very tempting to speculate how the different identified loci may come together and contribute to the disease process, one must keep in mind that GWAS do not identify genes or functional variants. Let us take as an example the EXOC3L2/BLOC1S3/MARK4 locus that has not been replicated in any subsequent study but is an interesting representation of the general type of results obtained by GWAS: although some hits are intragenic (CLU and CR1), most are located in intergenic regions. These may be located in gene-desert regions, close to one gene (PICALM), or in high-density gene regions. rs597668 (the most significant SNP in the EXOC3L2 locus) is within 60 kb of six genes, including EXOC3L2, BLOC1S3 and MARK4, three genes considered to be biologically plausible to be involved in AD pathogenesis [15]. If this locus had been replicated, the integration of these genes in biological pathways may have been complex and potentially point future research of the locus in the wrong direction. This clearly illustrates that the exact functional variant(s) contributing to the disease in each of these new loci are still unknown. We have been able to flag several genomic loci contributing to AD; we now have to identify the specific variants associated with the disease to understand which exact pathological mechanisms are being affected. Moreover, we currently do not know the gene effects for each locus, but in a very general manner, risk may be driven by (i) missense changes having a direct impact in protein function or (ii) by expression changes [84]. Until now, we have been able to test resting expression changes, but it may be that these expression changes occur as damage-induced response. This premise is even more interesting, since one of the features of most genes involved in AD is the fact that they may be damage-response genes (including APP) [85,86].

Conclusion

The great challenge of the current GWAS stage is to understand how the newly-identified loci influence and relate to disease. Animal models will be pivotal in our approach to this problem. Clearly, these recent findings do not exclude a central role of the amyloid cascade in AD pathogenesis. In fact, all the processes and pathways discussed in the present paper are somehow Aβ-related with a common theme being Aβ clearance.

Once again, developments in AD genetics open new avenues and raise new questions: will it be possible to recapitulate the sporadic disorder since so many small risk factors seem to be playing a role? Will it be more useful to have models of individual pathways/genes or complex interaction models? How exactly do these new genes and loci exert their risk? And how will we be able to integrate what we already know about disease mechanisms with these recent developments? These are essential questions that will carve AD research in the near future with implications for drug development and clinical trials.

Models of Dementia: the Good, the Bad and the Future: A Biochemical Society Focused Meeting held at Robinson College, Cambridge, U.K., 15–17 December 2010. Organized and Edited by Stuart Allan (Manchester, U.K.), Christian Hölscher (University of Ulster, Coleraine, U.K.), Karen Horsburgh (Edinburgh, U.K.), Simon Lovestone (King's College London, U.K.) and Calum Sutherland (Dundee, U.K.).

Abbreviations

     
  • amyloid β-peptide

  •  
  • AD

    Alzheimer's disease

  •  
  • ADNI

    Alzheimer's Disease Neuroimaging Initiative

  •  
  • AP-2

    adaptor protein 2

  •  
  • ApoA1

    apolipoprotein A-I

  •  
  • ApoE

    apolipoprotein E

  •  
  • APP

    amyloid precursor protein

  •  
  • GWAS

    genome-wide association studies

  •  
  • MAC

    membrane attack complex

  •  
  • MRI

    magnetic resonance imaging

  •  
  • OR

    odds ratio

  •  
  • PSEN1

    presenilin 1

  •  
  • PSEN2

    presenilin 2

  •  
  • Siglec

    sialic acid-binding, Ig-like lectin

  •  
  • SNP

    single nucleotide polymorphism

Funding

Our genetic analysis work is supported by Alzheimer's Research UK and a Wellcome Trust/Medical Research Council programme grant.

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