Molecular genetics of early-onset Alzheimer's disease revisited

Alzheimer’s & Dementia - (2016) 1-16 1 2 Review Article 3 4 5 6 7 a,b a,b a,b, 8Q10 a 9 Neurodegenerative Brain Diseases group, Department of Molecular Genetics, VIB, Antwerp, Belgium b 10Q1 Laboratory of Neurogenetics, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium 11 12 13 As the discovery of the Alzheimer disease (AD) genes, APP, PSEN1, and PSEN2, in families with 14Q2 Abstract autosomal dominant early-onset AD (EOAD), gene discovery in familial EOAD came more or less to 15 a standstill. Only 5% of EOAD patients are carrying a pathogenic mutation in one of the AD genes or 16 a APOE risk allele ε4, most of EOAD patients remain unexplained. Here, we aimed at summarizing 17 18 the current knowledge of EOAD genetics and its role in ongoing approaches to understand the 19 biology of AD and disease symptomatology as well as developing new therapeutics. Next, we 20 explored the possible molecular mechanisms that might underlie the missing genetic etiology of 21 EOAD and discussed how the use of massive parallel sequencing technologies triggered novel 22 gene discoveries. To conclude, we commented on the relevance of reinvestigating EOAD patients 23 as a means to explore potential new avenues for translational research and therapeutic discoveries. 24 Ó 2016 The Alzheimer’s Association. Published by Elsevier Inc. All rights reserved. 25 26 27 Keywords: Dementia; Early-onset Alzheimer disease; Genetics; Genome and exome sequencing; Missing genetic etiology; 28 Molecular pathways 29 30 31 32 33 with most of the EOAD patients being diagnosed between 1. Dementia and Alzheimer disease 34 45 and 60 years. Besides the typical clinical presentation 35 The term dementia is used to define a heterogeneous 36Q3 with memory impairment, atypical clinical presentation group of progressive and degenerative brain pathologies, 37 with focal cortical symptoms, for example, visual dysfuncclinically characterized by deterioration in memory, 38 tion, apraxia, dyscalculia, fluent and non-fluent aphasia, 39 learning, orientation, language, comprehension, and judgexecutive dysfunction, has also been reported. This atypical 40 ment. AD (OMIM# 104300), in its typical clinical presentapresentation is more frequently reported in EOAD patients 41 tion with progressive loss of memory and disturbance of 42 compared to LOAD, who mostly present with typical memadditional cognitive functions namely word-finding, spatial 43 ory phenotype [5]. Additionally, a nonmemory phenotype is cognition, reasoning, judgment, and problem solving [1], 44 seen in roughly 25% of EOAD patients in whom visual or 45 is the leading cause of dementia in the elderly. Of all demenapraxic and language phenotypes are more frequent [5]. 46 tia patients, 50% to 75% present with AD, which affects The neuropathologic hallmarks of AD brains are extra47 between 23 and 35 million people worldwide [2]. Age is 48 cellular accumulation of diffuse and neuritic amyloid the most prominent biological risk factor [3], and the age 49 plaques, composed of amyloid-b (Ab) peptide, and of 65 years is often used to classify AD patients in early50 frequently surrounded by dystrophic neurites and the intra51 onset (EOAD) and late-onset (LOAD) groups. Of all AD paneuronal accumulation of neurofibrillary tangles (NFTs) 52 tients, around 10% are diagnosed with EOAD [4], and they composed of hyperphosphorylated protein tau (p-tau) 53 present with their first symptoms between 30 and 65 years 54 [6,7]. These pathologic features are accompanied by

Molecular genetics of early-onset Alzheimer disease revisited Rita Cacace , Kristel Sleegers , Christine Van Broeckhoven

*Corresponding author. Tel.: 132 3 265 1101; Fax 132 3 265 1113. E-mail address: [email protected]

*

gliosis and the loss of neurons and synapses [7]. Although, some studies reported a larger neuropathologic burden [8] or a more widespread pathology extending outside the medial temporal lobe in younger patients [5], overall the

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pathologic features of EOAD and LOAD patients are largely similar, indicating that at the end-stage of disease, it is difficult to distinguish the two AD age groups by any other criterion than onset age. The main incentive for this review was the recent renewed interest in EOAD genetic studies due to the availability of high-throughput, genome-sequencing and exome-sequencing technologies, and bioinformatic tools, permitting new attempts to unravel the missing genetic etiology of EOAD. The expectations are that these new genetic approaches will uncover new molecular pathways or new molecular components of already known pathways. Furthermore, the availability of new genetic markers will help refining the different genetic signatures of clinical AD, allowing a more accurate stratification of patient cohorts, preclinical and clinical, for medical research, and for clinical trials. In the long term, the ability to identify different underlying molecular pathologies of clinical AD patients or at risk individuals will pave the way for personalized medicine and health care. 2. The genetic etiology of EOAD In contrast to LOAD which is a complex disorder with a heterogeneous etiology and an heritability of 70 to 80% [9,10], EOAD is an almost entirely genetically determined disease with a heritability ranging between 92% to 100% [9]. Between 35 to 60% of EOAD patients have at least one affected first-degree relative [11–13], and in 10% to 15% of those familial EOAD patients, the mode of inheritance is autosomal dominant transmission [11,13]. Genetic analysis of exceptionally large and informative monogenic pedigrees was the basis for the identification of high-penetrant mutations in the three EOAD genes, coding for the amyloid precursor protein (APP) and the presenilins 1 and 2 (PSEN1 and PSEN2). 2.1. Identification of causal EOAD genes in extended pedigrees Down syndrome (DS), caused by chromosome 21 (partial) trisomy, played a pivotal role in the early attempts to identify genes for inherited EOAD. DS patients were shown to present with a comparable brain pathology of amyloid plaques and tau tangles as AD patients [14]. The strong homology between the amyloid b (Ab) protein peptides, isolated from vessels [15] and from plaques [16] from DS and AD brains, was a first indication that both diseases shared a common genetic mechanism associated with chromosome 21 [15,17]. Whole-genome-linkage (WGL) studies in AD families provided supporting evidence for a genetic defect located on chromosome 21q [18–20]. Cloning of the gene coding for the amyloid b precursor protein (APP) [21], from which the amyloid b peptides are produced, and its mapping to chromosome 21q21.2–21q21.3 [22,23], encouraged a series of genetic studies aiming at

identifying mutations in AD patients and families. Initial genetic studies in large families with autosomal dominant AD were negative [24,25] but could be explained by the observation of a high degree of genetic heterogeneity in familial AD indicating that genes other than APP had to be involved [26]. A segregation study in extended multigenerational families with autosomal dominant cerebral hemorrhage with amyloidosis Dutch type (HCHWA-D), conclusively linked APP to the disease [27]. HCHWA-D brain pathology consists mainly of vascular amyloid depositions of the same Ab observed in AD brains [28]. Sequencing identified a mutation affecting the Ab sequence in patients with HCHWA-D [29] and segregated with disease [30]. The discovery of a APP mutation linked to vascular Ab pathology in HCHWA-D encouraged new mutation studies of APP in AD families. A first mutation was identified confirming a direct role for APP in AD pathogenesis in some AD families [31]. Segregation studies in EOAD pedigrees, negative for APP mutations, led to the identification of a new locus for EOAD on chromosome 14q24.3 [32–35]. Genetic mapping and gene cloning followed by mutation screening of candidate genes [36–38] identified presenilin 1 (PSEN1) as an EOAD gene with at that time unknown functions [39]. Based on protein homology, a second presenilin protein was identified and mapped to chromosome 1q31–q42 [40,41], in the region that was linked to AD in a series of families known as descendants of Volga-Germans [42,43] and was named presenilin 2 (PSEN2). 2.2. Genetic and phenotypic heterogeneity in EOAD To date, 52 pathogenic mutations in APP have been reported in 119 probands of autosomal dominant families (http://www.molgen.vib-ua.be/ADMutations) [44]. Most of the APP mutations are nonsynonymous within or flanking the Ab sequence (Fig. 1A). However, 25 genomic duplications of variable size containing APP have been identified co-segregating with AD in as many autosomal dominant families, as reported in http://www.molgen. vib-ua.be/ADMutations [44] and reviewed in [45], mimicking partial trisomy 21. Furthermore, a recessive one amino acid deletion (p.E693D) [46] and a recessive missense mutation with dominant negative effect on amyloidogenesis (p.A673V) [47,48] were described (Table 1). Q4 Missense mutations are identified at least 4-folds more frequently than APP genomic duplications in AD patients. Disease onset of APP mutation carriers ranged between 45 and 60 years [49,50]. In contrast to the missense mutations, showing a near-complete disease penetrance, APP genomic duplications display reduced penetrance and higher variability in onset age [45]. Besides the pathogenic mutations, a rare protective variant p.A673T in APP was reported that was enriched in the Icelandic population [51]. At the same amino acid position, the p.A673V variant showed

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Fig. 1. APP protein mutations and structure. (A) APP protein sequence from amino acid residue 647 to 730 is presented, sequence in green depicts the extracellular domain, in orange the transmembrane domain and in dark blue the intracellular domain. Known pathogenic mutations are reported in purple and, if available, the mutation alias is shown, genomic duplications are not represented in the figure. In red are the two recessive pathogenic mutations, in gray the nonpathogenic mutations. A circle encloses the residue p.A673 because the change to T is described as protective against AD as well the change to V in the heterozygous state. An equal (5) marks a nonpathogenic silent mutation at residue p.G708. A delta (D) indicates a deletion. The cleavage sites of a, b, and g secretases are marked with black dotted lines. (B) Schematic presentation of the APP proteolytic processes. The nonamyloidogenic and the amyloidogenic pathways are illustrated. For clarity, a general g-secretase cleavage site is reported.

carriers have generally a wider onset age range from 40 to 70 years [49,50]. With a few exceptions, disease penetrance is complete for PSEN1 mutations [50]. In case of PSEN2 mutations, disease penetrance is more difficult to establish because far less families have been reported and the onset age range is much wider [50]. PSEN mutations are commonly inherited in an autosomal dominant manner, but de novo mutations in PSEN1 have been described in EOAD patients with disease onset as early as 28 years [52,53]. Two families have been described that segregate dominant EOAD mutations in a homozygous state, a Italian family with the APP mutation p.A713T [54] and a Colombian family with the PSEN1 mutation p.E280A [55]. In both families, the homozygous carriers did not seem to be more

antiamyloidogenic properties when identified in the heterozygous state [47], suggestive of a protective effect (Fig. 1A). Of all three EOAD genes, PSEN1 is most frequently mutated with 215 mutations (Fig. 2A) in 475 probands (http://www.molgen.vib-ua.be/ADMutations) [44]. In PSEN2, 31 mutations have been identified, 15 pathogenic in 24 probands and 16 with pathogenicity nature unclear (Fig. 2B). The mutation spectrum of the PSEN genes includes missense mutations, small insertions, and deletions (indels) as well as genomic deletions specifically in PSEN1, which lead to inframe exon 9 skipping (http://www.molgen.vib-ua.be/ ADMutations) [44] (Table 1). The onset age of carriers of a PSEN1 mutation ranged from 30 to 50 years. PSEN2 mutation

Table 1 Genetic heterogeneity in Alzheimer disease (AD): Known causal early-onset AD gene Gene

Chromosome

Inheritance

Gene identification

Mutation spectrum

Mutations (N)

APP

21q21.1–21q21.3

Autosomal dominant Autosomal recessive Protective

Linkage analysis

54*

PSEN1

14q24.3

Autosomal dominant de novo

Linkage analysis

PSEN2

1q31–q42

Autosomal dominant

Linkage and homology mapping

Missense Gene Duplication Amino acid deletion Missense Small indels Genomic deletions Missense

*The total number of APP mutations includes two causal recessive mutations. REV 5.4.0 DTD
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31

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Fig. 2. PSENs protein mutations and structure. (A) PSEN1 and (B) PSEN2 protein sequences. In blue are marked the cytoplasmic domains, in yellow the transmembrane domains, in red the luminal domains, and in green the one intermembrane/IX transmembrane domain. Pathogenic or predicted pathogenic mutations are in purple. In orange are mutations with unclear pathogenicity and in gray are reported nonpathogenic mutations. Different nucleotide variants which lead to the same amino acid change, are reported only once. A delta (D) indicates deletions. An asterisk (*) marks two aspartates (D), which are the catalytic amino acids located in transmembrane domain VI (PSEN1 is D257 and PSEN2 is D263) and in transmembrane domain VII (PSEN1 is D385 and PSEN2 is D366). Arabic numbers indicate the last amino acid residue of every topological domain based on Uniprot database (PSEN1 P49768 and PSEN2 P49810). (C) Schematic presentation of APP and PSEN complexes. For PSENs, alternative predicted protein conformations are shown. The region with variable topology is framed in dotted line and includes either an intermembrane domain with intracellular C-terminus or a ninth transmembrane domain with extracellular C-terminus. The black dots on the transmembrane domains indicate the catalytic aspartates location. APP is inserted between PSENs transmembrane domains VI and VII. The a, b, and g secretases cleavage sites are schematically indicated.

severely affected [54,55]. The major implication of these findings is that the homozygous state of autosomal dominant mutations in AD is not lethal as initially hypothesized. This may also suggest the possible presence of additional protective genetic factors, which may modify the disease presentation. In an EOAD patient cohort, the estimated mutation frequencies for the three genes were ,1% for APP, 6% for PSEN1, and 1% for PSEN2 [56]. Together, they explain only 5%–10% of EOAD patients [9,56], whereas depending on the study, 23% to 88.2% of autosomal dominant patients remain genetically unexplained [9,11,57–59] (Fig. 3). The wide divergences in unexplained EOAD patients and families might result from differences in study design or sampling biases. Besides the genetic heterogeneity, phenotypic heterogeneity is also reported in EOAD. This complicates the clinical diagnosis in younger patients. Atypical presentation with language impairment was reported for specific PSEN1 mutations [49,50]. Prominent

behavioral symptoms (also presenting symptoms) like delusion, hallucinations, and apathy, are described for a subset of PSEN1 and PSEN2 mutations [49,50]. In some cases, the clinical symptoms fulfilled the criteria for a diagnosis of frontotemporal dementia, for example, in the case of the PSEN1 mutation p.G183V. In this specific example, the autoptic pathologic and immunohistochemistry analysis of the brain showed pick-type tauopathy, normally caused by MAPT mutations, in absence of extracellular Ab deposits [60]. The neurologic symptom of myoclonus is present in most of monogenic EOAD patients and increases with the disease duration; seizures were also reported as presenting symptom for some PSEN1 mutations and are very common in APP duplication carriers (including Down syndrome patients) [49,50]. Extrapyramidal symptoms are not uncommon in PSEN1 mutations carriers but very rare for PSEN2 and APP and tend to appear after several years into the disease course [49,50]. Spastic paraparesis has been associated with specific PSEN1

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Fig. 3. Missing genetic etiology of early-onset Alzheimer disease (EOAD). The pie charts indicate the distribution of EOAD and late-onset Alzheimer disease (LOAD) patients (dark blue), the fraction of sporadic and familial EOAD patients with the sub-fraction of autosomal dominant patients (light emerald). The orange pie chart depicts the fraction of unexplained autosomal dominant families. The possible mechanisms that may explain the missing genetic etiology of EOAD are divided in two groups arising from the red pie chart: (1) possible undetected genetic alterations due to different causes, listed arising from the red pie chart (right side) and (2) possible undetected epigenetic dysregulation (left side). For both scenarios, some examples of study designs (e.g., family based, extreme trait design and so forth or investigation of DNA(de) methylation and so forth) and technological approaches such as next-generation sequencing (NGS), chromatin immunoprecipitation assay combined with sequencing (ChIP-Seq) and RNA sequencing (RNA-Seq) are schematically suggested.

mutations; in these cases, a peculiar pathologic finding of Ab plaques without a dense core or neuritic dystrophy (“cotton wool plaques”) is reported in the frontal cortex of the patients presenting the phenotype [61]. Cerebellar ataxia is a rare event but is present for specific PSEN1 mutations, like in the case of the p.S170F mutation co-segregating with a Cathepsin D variant, suggesting a deleterious epistatic effect on the disease course [62]. Taken together, this indicates that it is reasonable to continue efforts to both unravel the genetic etiology of EOAD and to search for modifier of the phenotype in presence of known pathogenic mutations. 2.3. APOE in EOAD A genome-wide linkage study in families with LOAD and subsequent association studies in patient/control cohorts

identified the ε4-allele of the apolipoprotein E gene (APOE) as a major genetic risk factor for LOAD [63–65]. The presence of one or two copies of the APOE ε4-allele increased the risk to develop LOAD by a factor 3 up to 15-fold in a dose-dependent manner [66,67]. The APOE ε4-allele also increased risk for EOAD in carriers of at least one ε4-allele and was highest in those with a positive family history (Table 2) [12]. In carriers, the homozygosity for the APOE ε4-allele was sufficient to significantly increase risk for EOAD independent of other genetic factors. In contrast, in carriers, heterozygous for the APOE ε4-allele risk was only significantly increased in the presence of a positive family history of disease, indicating that the presence of one ε4-allele was insufficient to increase risk for AD before the age of 65 years. It also suggested that the APOE ε4-allele may modify the expression of other genetic factors contributing to disease. However,

Table 2 Risk variants and genetic modifiers in EOAD Gene

Variant

Effect on EOAD

References

PSEN1 APOE

Promoter SNVs ε4-allele ε2-allele p.A23T p.M129V Octapeptide repeat insertions Nonsynonymous rare variants

Increased risk Increase risk—highest in the presence of positive family history—and earlier AAO Delays AAO Delays AAO Increased risk—VV homozygosity and highest in presence of positive family history Earlier AAO Increased risk—signal driven by positive family history

[68,69] [12,70,71] [70–73] [74] [75] [76] [77]

CCL11 PRNP SORL1

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familial aggregation of EOAD cannot fully be explained by APOE as both carriers and noncarriers of a ε4-allele have an increased risk in the presence of a positive family history. Contrary to the risk-increasing effect of the APOE ε4allele, it was demonstrated that the ε2-allele exerted a protective effect [72,73]. Different from the mutations in APP, PSEN1, and PSEN2, the APOE ε4-allele was considered neither necessary nor sufficient to cause AD. However, this concept might need to be reconsidered based on a more recent study [78], which showed that the effect of APOE on AD risk is comparable to the effect of a major genetic factor with semidominant inheritance [78]. Analysis of the APOE genotypes in families segregating dominant APP (p.V717I) [79], PSEN1 (p.E280A) [70], or PSEN2 (p.N141I) mutations, showed that onset age decreased in presence of the ε4-allele in mutation carriers, whereas those with the ε2-allele had later onset ages [70,71]. A larger study of samples of the PSEN1 (p.E280A) family [73] confirmed the APOE ε2-allele as an age-at-onset (AAO) modifier (w12 years of delay) compared to the ε4-allele (Table 3) [73]. Nevertheless, these APOE genotype effects were not observed in all families with a pathogenic mutation [90]. Genome-wide searches in the Volga German families (PSEN2 p.N141I) identified at least three additional genetic loci (1q23.3, 17p13.2, and 7q33) [91], which could harbor modifier genes. Furthermore, a whole genome-sequencing approach of a large Colombian kindred (PSEN1 p.E280A), detected a protective variant (p.A23T, rs1129844) in the gene CCL11 encoding eotaxin-1 that delayed onset age of AD by a decade [74]. Autosomal dominant families, segregating mutations in one of the 3 causal EOAD genes, show often variability in clinical presentation, even in the presence of the same mutation. A meta-analysis study showed that the phenotypic heterogeneity could be partially explained by the mutated gene, the type of mutation, and the family history. But, this is not always the case, indicating that in certain families, additional modifier factors (i.e., gene-gene and geneenvironment interactions) may be involved [90]. 3. The amyloid cascade APP and its processed peptides are the cornerstone of the “amyloid cascade hypothesis” [92]. This hypothesis states that the accumulation of Ab is the causative agent of AD Table 3 Genetic heterogeneity in AD: Neurodegenerative brain diseases genes presenting with AD-like phenotype

Gene

Chromosome

Typical phenotype

GRN MAPT C9orf72 PRNP

17q21.31 17q21.31 9p21.2 20p13

FTLD FTLD FTLD Prion disease

Mutation frequency in AD (%)

References

,2% ,1% ,1% ,1%

[80–82] [83,84] [85–87] [88,89]

by destroying the synapses, inducing the formation of NFTs, and ultimately inducing neuronal loss [92]. Two distinct and mutually exclusive pathways cleave APP (Figure 1B). In one pathway, the cleavage by a-secretase and g-secretase produces three fragments: a secreted C-terminal fragment (sAPPa), p3, and the APP intracellular domain (AICD; Figure 1B). In the second, and thus amyloidogenic pathway, APP is cleaved by the b-secretase (beta-site APP cleaving enzyme 1, BACE1), followed by g-secretase cleavage. The cleavage by b-secretase generates a large soluble extracellular secreted domain (sAPPb). The remaining membrane bound APP fragment, C99, is processed by multiple g-secretase cleavages. The first occurs at the cleavage site epsilon (ε-site, Figure 1B) to produce the AICD and then, after g-secretase cleavages trim the membrane bound producing Ab species that differ in protein length including Ab38, Ab40 (most common fragment), Ab42 (self-aggregative fragment), and Ab43 [93]. Genetic mutations in APP are located in or near the Ab sequence and in the proximity of the cleavage sites of the secretases (Fig. 1A), whereas PSEN1 and PSEN2 mutations are scattered all over the protein (Fig. 2A and 2B). The mutations are predicted to cause AD through aberrant APP processing determining either increased Ab levels or increased production of Ab42 (and Ab43) peptide over Ab40, triggering Ab aggregation [93]. From its postulation, the “amyloid cascade hypothesis” has been supported and questioned (as reviewed by Morris et al. [94]) acknowledging that Ab has an important role in AD pathogenesis although its role in the disease process is most likely much more multifaceted. This was corroborated by the observation of a subgroup of clinically diagnosed AD patients with neurodegeneration and cognitive deficits but near absence of Ab deposits, defined as “suspected non-AD pathology” (SNAP) [95]. In a clinical trial of anti-Ab passive immunotherapy, 16.3% of the clinically diagnosed AD patients had a negative-baseline amyloid positron emission tomography scan [96]. Studies on mild cognitive impairment cohorts are ongoing to phenotypically classify the SNAP patients [97,98], to better understand, if they represent a subgroup of AD with pathologic features independent from the amyloid cascade [99]. 4. The role of other neurodegenerative brain diseases genes in EOAD expression Possibly, some of the SNAP patients might be associated with mutations in genes involved in other neurodegenerative brain diseases (NBD). Mutations have been identified, at low frequencies (less than 1%–2%), in genes causing frontotemporal lobar degeneration (FTLD), that is, mutations in MAPT [83,84] and in granulin (GRN) [80–82], and the repeat expansion in C9orf72 [85–87] (Table 3). The genetic heterogeneity of the phenotypic presentation of AD supports that both diseases form part of an AD-FTLD disease continuum [100,101]. An AD-like phenotype was also described in the presence of a nonsense mutation in the prion protein

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(PRNP p.Q160*) [88,89], gene responsible for inherited neurodegenerative spongiform encephalopathies. Histopathologic analysis of nonsense mutation carrier showed neuritic plaque-like pathology and severe NTFs, the plaques were immunonegative for Ab but immunopositive to PrP leading to a pathological diagnosis of prion disease [88] (Table 3). Moreover, a common coding polymorphism, M to V at position 129 of PRNP has been associated with EOAD when identified in homozygous state (MM and VV; Table 2) [75]. The risk was higher for the VV and increased in patients with positive family history [75]. In addition, increasing number of octapeptide repeat insertions in PRNP have also been associated with younger onset age in AD patients (Table 2) [76]. The genetic heterogeneity in some dementia patients leads to a later clinical diagnosis and to flawed genetic testing. With the current availability of massive parallel sequencing (MPS), causal genes across NBD clinical diagnostic dementia subgroups can be screened simultaneously, increasing the chances to identify pathologic mutations independent of the clinical diagnosis. It can be expected that the breadth and depth of this etiological disease heterogeneity will be clarified more fully in the coming years, as discussed in section 5 and 6. 5. The role of EOAD genes in AD susceptibility There is substantial evidence that apart from pathogenic mutations, the EOAD genes also harbor genetic variations that contribute to increased susceptibility for AD. For example, single nucleotide variants (SNVs) in the promoter region of APP or PSEN1 have been associated with increased risk of LOAD [68,69,102]. Some APP promotor SNVs, increase neuron-specific APP transcriptional activity by near two-fold [103,104], and as such increase risk to develop LOAD. PSEN1 promotor SNVs [68,69,102] could alter protein expression in neurons [69,102] leading to increased risk of both EOAD (Table 2) [68,69] and LOAD [69]. Likewise, coding variants in the known causal genes might influence AD risk; however, to date, there is little information available because causal EOAD genes are rarely screened in LOAD patients [56,105]. Moreover, in EOAD patients, the routine genetic testing paradigm was based on mutation frequency (PSEN1 . APP . PSEN2) and location of known mutations (exons 16 and 17 in APP covering the Ab peptide cleavages sites) and was stopped once a pathogenic mutation was identified. Today, the availability of extended gene panels and MPS technology, allows near unlimited screening of full coding sequences of multiple disease genes in parallel across clinical phenotype ([105,106] and unpublished data). Systematic screening of EOAD and LOAD patient cohorts showed rare or novel mutations in causal AD genes in LOAD patients ([105] and unpublished data) mutations in APP outside the Ab coding exons and the occurrence in one individual of mutations in more than one NBD genes (unpublished data). This suggests that mutations in known

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EOAD genes might act differently on disease expression, which ranges from high penetrance (5 causal allele) and early-onset age to low penetrance (5 risk allele) and lateonset age, depending on the effect of the mutant allele on protein function ([105] and unpublished data). The new challenge that we are facing when using these gene panels is the high potential of identifying variants of uncertain significance (VUSs). These VUSs lack supportive functional data and can only be classified based on their likelihood of pathogenicity using in silico information obtained using bioinformatics prediction tools (e.g., predicting the impact of the mutated amino acid residue on protein stability/function) [107], data which are insufficient in a clinical diagnostic setting. 6. The missing genetics of EOAD Rare high-penetrant mutations in APP, PSEN1, and PSEN2 could explain only a small fraction of EOAD families, leaving a large group of autosomal dominant pedigrees genetically unexplained [9,11,57–59] (Fig. 3). Additionally, it was estimated that only between 5%–10% of EOAD [9,56] can be explained by mutations in the three known EOAD genes (Fig. 3). The limited number of resolved pedigrees and large number of genetically unexplained EOAD patients, indicated that additional causal genes remain to be identified. Next generation sequencing technologies (NGS), like whole-genome-sequencing (WGS) and wholeexome-sequencing (WES), offered a next step to gain new insights into the molecular genetic etiology of AD. Particularly, these NGS technologies made it conceivable to reconsider those small families that had been excluded from WGL studies because of their limited genetic information content and thus lack statistical power. Additionally, it became possible to examine, in an unbiased manner, groups of unrelated AD patients selected for their extreme phenotypic characteristics, for example, very early-onset age, familial history of disease, or atypical clinical and/or neuropathological phenotypes. An early NGS study produced WES data of a clinically diagnosed EOAD patient and identified in NOTCH3, a known mutation that had been associated with cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy [108], a dementia disorder that shares clinical similarities with EOAD. The index patient belonged to a consanguineous Turkish family with a complex clinical history of neurologic and immunologic disorders [108]. The study demonstrated that WES is a valid tool to unravel the genetic etiology of complex diseases [108]. A WES study in EOAD probands of autosomal dominant families, who were negative for mutations in the three EOAD genes, identified seven mutations on screening of 29 probands (five missense and two nonsense mutations) in the sorting protein-related receptor gene (SORL1) [109]. Previous studies have shown reduced expression of the SORL1 transcript in AD patients using

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microarray screening [110] and the involvement of the protein in APP and Ab trafficking [111]. One of the EOAD missense mutations in SORL1 (p.G511R) that cosegregates with autosomal dominant AD in one family [109], is located within the VPS10P domain of SORL1. This specific mutation was shown to reduce the capability of the VPS10P domain to bind Ab, resulting in accumulation of the peptide and lower turnover [112]. More, the investigation of an EOAD patient-control cohort confirmed the role of rare nonsynonymous coding variants in SORL1 as risk factor for EOAD (Table 2), with the association signal driven by familial patients [77]. Also, both common and rare, noncoding and coding variants in SORL1 have been associated with increased risk of LOAD [113–115]. Three SORL1 coding mutations (p.E270K and p.T947M, p.A528T) identified in a family-based and cohort-based association study on LOAD have been shown to increase Ab40 and/or Ab42 secretion when expressed in vitro [115]. These studies reinforced the role of rare variants in SORL1 in both EOAD and LOAD risk. The screening of SORL1 in larger patient/ control groups will help defining the contribution of rare genetic variants in SORL1 to AD etiology. 6.1. Causal genes in unexplained EOAD families Scarcity of sufficiently informative EOAD families, the small number of family members available for genetic analyses and the clinical and genetic heterogeneity, has hampered WGL studies in identifying the actual underlying gene defects [116]. An example is provided by the familybased GWL study in the Swedish series, which includes both EOAD and LOAD patients. The original WGL study in multiple autosomal dominant families detected a suggestive linkage at chromosome 5q35 [117]. The inclusion of additional families to the WGL analysis did not confirm the 5q35 locus [118], but the investigation of a selected number of families with autopsy confirmed AD revealed a suggestive locus on chromosome 8q24 [119]. An independent locus on chromosome 8q was identified in a Swedish family in another WGL study [120]. In the aforementioned examples, the underlying genetic defect has not yet been pinpointed. We also identified in one extended Dutch family, a significant WGL peak at chromosome 7q36 but candidate gene screening in this locus did not identify a mutation that could explain the linkage [121]. Today, NGS technologies are used to examine unexplained EOAD families [122]. In large extended families, the NGS approach can be preceded by a WGL analysis to prioritize chromosomal regions that are more likely to contain the disease gene. NGS data of two, distantly related, patients can be sufficient to identify shared, protein affecting, genetic variants in genes located in the linked loci. An advantage of this combined approach of WGL and NGS is a significant decrease in complexity of the NGS data under analysis. Furthermore, the success rate of

this approach can be substantially increased by including additional family members both affected and/or unaffected. The main strength of NGS technologies, however, is their capability to identify putative disease genes by direct analysis of the data obtained of related family members belonging to small nuclear families. Because of the familial nature, these NGS studies are often limiting their first analysis to variants in coding sequences, particularly variants predicted to have a high impact on the protein functioning and thus identifying high-penetrant causal mutations. But silent, noncoding and genomic variations can also exert an effect on disease risk or causality. For example, the intron 4 variant (c.358–304 C . G) in SOD1 defines the activation of a cryptic splice site with the inclusion of a pseudo-exon leading to amyotrophic lateral sclerosis [123]. Also, genomic duplications of the APP locus have been linked to EOAD (as reviewed in section 2.2), and an inversion polymorphism comprising MAPT has been associated to increased risk for neurodegenerative brain diseases like progressive supranuclear palsy [124,125]. In case, a candidate gene is identified in this NGS approach, the next major step is to generate confirmatory evidence of its pathogenic role in the disease. This can be obtained by cosegregation studies of the putative disease causing mutation in the family, by the screening of the candidate gene for mutations in a patient cohort and its absence in a control cohort. Limiting factors to the segregation analysis are the size of the pedigree as well as the number of available samples. Also, incomplete or age-dependent penetrance of the genetic variants can complicate the interpretation of both the segregation data as well as the presence of the variant in control individuals. Alternatively, an extreme trait design can be used to sequence a small number of individuals at one or both extremes of the disease (endo) phenotype [126] as shown by Nho et al. [127]. This approach, though it identified frequent variants that may be associated with AD risk, provides an example of using NGS with quantifiable traits as endophenotype. 6.2. Other mechanisms In most of the EOAD families, the disease inheritance is consistent with autosomal dominant transmission. Occasionally, other transmission patterns were reported like recessive mutations in APP [46,47] and de novo mutations in PSEN1 [52,53] (described in section 2.2), but they have not been systematically explored. A potential study design to optimize their detection is the use of WGS in small nuclear families or trios with neurologically healthy parents and EOAD patients negative for the APOE ε4-allele. It is possible to approach the data analysis, based on the family history of disease, to identify either recessive (positive family history) or de novo (negative family history) mutations. In specific cases, that is, when the family history is unknown, both hypotheses can be

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followed in parallel. The WGS data of the affected offspring can be used to define a genome-wide map of runs of homozygosity (ROH), and these ROH regions can be analyzed together with the WGS data of the parents to identify recessive mutations. Recessive inheritance of AD has already been examined before using either polymorphic simple-tandem-repeat markers or high-density single nucleotide polymorphism genotyping arrays, identifying homozygous regions harboring potential candidate genes [128–131]. Conversely, the WGS data can be analyzed for de novo mutations present in the offspring but absent in the parents. Limitations to these study designs are the unavailability of both parents and, the possibility that EOAD in the offspring resulted from a dominant mutation that was undetected in the parent due to incomplete penetrance or an AAO modifier. Alternative to recessive or de novo mutations, sporadic EOAD patients can also be explained by germline or somatic mosaicism [132,133] as shown for the PSEN1 p.P436Q mutation [132], with the degree of mosaicism affecting onset age and clinical presentation. Also, epigenetic dysregulation can affect gene expression in AD. Deoxyribonucleic acid (DNA) (de)methylation, chromatin remodeling, and noncoding RNAs (ncRNAs) expression could play a more impacting role than initially thought in AD onset and expression (Fig. 3). Alteration in methylation in the promoter region of APP, PSEN1, and APOE have been described in AD patients as well as histone acetylation which was altered in both AD patients and in AD transgenic mouse models (as reviewed by Lardenoije et al., [134]). Imbalanced expression of noncoding RNAs, miRNA particularly, has been identified in relation to AD (as reviewed by van den Hove et al. [135]). These mechanisms affect gene expression without altering the DNA sequence and are currently underinvestigated in EOAD patients. Two epigenetic studies in LOAD [136,137], adopted novel designs to control for confounding factors like the different methylation pattern in different cells/tissues, interindividual variability within the same tissue [138]. Both studies independently detected changes in methylation in four CpG sites close to ANK1, RPL13, CDH23, and RHBDF2 [136,137]. This confirmed the respective findings and supported the strength of the study designs. Additionally, the recent availability of the epigenome roadmap [139], a public data resource containing epigenetic information from different cell types and tissues, is paving the way to more comprehensive research. This effort resulted, among others, in the identification of conserved epigenomic signals at orthologous regions between mice and humans, which upregulate the immune response genes and downregulate the synaptic plasticity genes in the CKp25 mouse model of AD [140]. The major challenge of this type of studies, in the years to come, will be to determine whether the epigenetic change is a cause or a consequence of the disease and how the disease risk is influenced by the alteration of the chromatin structure

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[141]. Moreover, it will be relevant to comprehend how the epigenome signature varies between populations and how the epigenetic findings will translate from mouse models (e.g., CK-p25) to humans. The availability of epigenome data will allow the integration of genetic results with epigenome regulation to better understand disease signatures which may represent a potential target for future diagnostic and treatment interventions. 7. Translational research in autosomal dominant families On the one hand, the investigation of unexplained EOAD families and patients aims at identifying novel causal genes and pathways. On the other hand, an international effort is ongoing to better understand the temporal sequence and the earliest events, which lead to the endpoint disease clinical manifestation. Longitudinal studies on families segregating a monogenic form of AD and patients with a known causal mutation are currently performed to investigate the prodromal phase of the neurodegenerative processes which ultimately lead to AD [142]. The understanding of the post-genomic consequences of the genetic mutations aims at improving the AD diagnosis through the identification of novel-sensitive biomarkers (Box 1), which correlate with earliest pathologic events and with disease conversion and progression. This will also help in the definition of targets for therapeutic screening and the selection of uniform patients groups for targeted pharmaceutical approaches. Longitudinal studies in asymptomatic mutation carriers have already demonstrated that at least 20 years before the clinical manifestation of cognitive deficits it is possible to detect the pathologic changes happening in the brain using both biochemical and instrumental biomarkers (Box 1) [142,144]. The longitudinal data investigation of cognitively unimpaired mutations carriers from the Colombian PSEN1 kindred included in the Alzheimer’s Prevention Initiative (API), for example, led to the identification of a composite cognitive test score that has improved power to detect preclinical AD decline [150]. A direct application of this score could be in identifying preclinical patients to include in prevention trials [150]. In the API cohort, the anti-Ab monoclonal antibody crenezumab testing is ongoing [151]. Another example of translational research is provided by the dominantly inherited Alzheimer’s network trials unit, which is currently testing in presymptomatic known mutation carriers two monoclonal anti-Ab antibodies (gantenerumab and solanezumab) that target different Ab species [151]. Ab42-reducing approaches may have more efficacy in familial patients, because the increased production of Ab42 is caused by genetic mutations, in this way, it is possible to attempt to increase the clearance of the toxic peptide before the accumulation is too extensive. Taken together, these examples demonstrate that families segregating a monogenic form of AD and patients with a known causal

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Box 1 Genetic, biochemical, neuroanatomic, and metabolic markers in AD diagnosis

Genetic markers: APP, PSEN1, and PSEN2 APP (OMIM# 104760) contains 18 exons and spans a genomic region of 6290 kb at chromosome 21q21.1–21q21.3. APP has several isoforms generated by alternative splicing. The APP770 is used as canonical isoform for mutation nomenclature. Reference sequences are Gene NG_007376.1, cDNA NM_000484.2, and Protein NP_000475.1. The Ab protein is encoded by exons 16 and 17. APP is a type I transmembrane protein (Figure 1). PSEN1 (OMIM# 104311) counts 13 exons and spans 6 84 kb at chromosome 14q24.3. The first 4 exons contain untranslated sequence, and exons 1 and 2 represent alternate transcription initiation sites. PSEN1 counts 467 amino acids residues. Reference sequences are Gene NG_007386.2, cDNA NM_000021. 2, and protein NP_000012.1. PSEN2 (OMIM# 600759) contains 12 exons and spans a region of 625 kb at chromosome 1q42.13. The first two exons encode the 5-prime untranslated region. PSEN2 has 448 amino acids. Reference sequences are Gene NG_007381.1, cDNA NM_000447.1, and protein NP_000438.1. The PSENs share an overall amino acid homology of 67% and are multimeric protein predicted to cross the membrane 7–9 times (Fig. 2C). Diagnostic molecular genetic screenings have been performed so far by sequencing exon 16 and 17 (Ab coding exons) of APP and all coding exons of PSEN1 and PSEN2, after a decision paradigm based on gene mutation frequency PSEN1 . APP . PSEN2. NGS technologies are sequencing in parallel the entire coding sequence of all three causal genes either by gene-panels approach [106] or by whole-exome sequencing [143]. Specific (or core) biochemical markers are Ab42, total tau (t-tau), and phosphorylated tau (p-tau). These are dosed in cerebrospinal fluid (CSF) on lumbar puncture. These biomarkers are measured to provide a reflection of the cerebral metabolic process of the protein transport between the brain and the CSF. General consensus is that in AD patients, the CSF levels of Ab42 are reduced, because of amyloid plaque formation in the brain, and that t-tau and p-tau are increased because of neuronal cell loss [144,145]. Furthermore, t-tau CSF levels increase with AD progression [145]. The better understanding of the pathologic processes occurring in AD brains are triggering the validation of novel CSF biomarkers (as reviewed in [146]) as the APP-cleaving enzyme 1 (BACE1) levels, the dosage of truncated amyloid-b isoforms, both linked to amyloidogenesis and result increased in patients. APP cleavage products, which are produced during APP processing can be measured to assess drug efficacy.

F2-isoprostane a marker of mithocandrial dysfunction as well as markers of synaptic degeneration (e.g., synaptotagmin, synapsin, synaptophysin and so forth) and markers of brain injury (i.e., visinin-like protein 1 (VILIP-1) [147]), are under investigation to assess their applicability in diagnostic. Neuroanatomic markers such as computer-assisted tomography (CT) and volumetric magnetic resonance imaging (MRI) are used to measure structural brain atrophy. Alteration in brain metabolic processed are measured using metabolic markers, that is, positronemission tomography and single photon-emission computed tomography (SPECT) with radiopharmaceutical agents, for example, fluorodeoxyglucose (FDG). It is also possible to image in vivo amyloid deposition, imaged by PET with the use of Pittsburgh compound B (PiB). A recent study shows that also tau tangles correlate well with AD diagnosis, showing the strongest predictive value for progression to AD [148]. Although tau is a biomarker of brain injury, synapse loss, and progression to neurodegeneration, selective tau tracers for PET are not yet available [149]. In vivo tau imaging will help in clarifying the role of tau in AD onset and progression and will support the clinical disease diagnosis [149]. mutation are still in the central core of preclinical and clinical research. The knowledge of the molecular and cellular biology of AD has already supported the development of tools for disease diagnosis (Box 1), but efforts are still ongoing to identify novel biomarkers for disease prediction and prognosis. These will allow the selection of homogeneous cohorts of participants for clinical trials with a close monitoring of the drug response.

8. Lesson learned from research in LOAD Locus heterogeneity and limitations in both pedigree size and technologies were probably the major contributors to the lack of additional genetic breakthroughs in EOAD. Meanwhile, the investigation of LOAD, using genome-wide association studies (GWAS), led to the identification of common risk variants within .20 genetic loci [114,152–156]. These genetic loci could be grouped in three major biological pathways: immune system; lipid metabolism; and synaptic dysfunction/cell membrane processes (e.g., endocytosis) [116,157,158]. Additionally, NGS resequencing efforts are identifying rare variants (minor allele frequency ,1% in the general population) with a strong effect on disease risk in additional genes or in genes previously associated with LOAD. Examples are provided by rare variants detected in TREM2 [159,160] or ABCA7 [161,162]. Also, in these cases, the main pathways involved are the immune response (TREM2, ABCA7) and lipid metabolism (ABCA7) [157]. As

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for APOE ε4 (discussed in section 2.3), a few rare variants in the risk genes (e.g., TREM2 [163], ABCA7 [162], EPHA1 [164]) have been shown segregating in families, suggesting a possible effect on disease penetrance, although further studies are needed to strengthen this hypothesis. The role of rare variants in EOAD has been less investigated nonetheless a few examples are available like the rare protective variant detected in APP [51] and the variant in CCL11 (eotaxin-1) [74]. This last example is of particular relevance, as it demonstrated an involvement of the immune pathway in modulating the disease onset in presence of a highly penetrant mutation in PSEN1. The molecular mechanism through which eotaxin-1 modulates AD onset needs further investigation but, if validated, may represent a first step to develop therapies able to delay the onset of AD. This finding also supports the hypothesis that rare variants can modulate the disease onset and most likely also the disease progression. Taken together, the findings suggest that Ab processing in LOAD could have a lesser central role, whereas the clearance of the aggregates might be involved in the disease pathogenesis [165]. On the one hand, the identification in EOAD of AAO disease modifiers (APOE: lipid metabolism and CCL11: immune pathway) in genes belonging to two of the biological pathways detected by GWAS in LOAD, is indicative of multiple (common) pathways modulating the clinical expression of AD. On the other hand, the shared clinical features as well as the pathologic findings in brain tissue of both EOAD and LOAD suggest that discoveries obtained in EOAD will likely translate to LOAD.

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progresses have been made in the last years in disclosing the pathologic mechanisms of AD. Novel regulatory mechanism and biological pathways have been disclosed, but prevention of the disease is still a challenge ahead. In this review, we have summarized the main aspects of the research in EOAD and how these have influenced the current knowledge of the disease mechanisms. We suggest that continuation of the investigations of families segregating known mutations and the elucidation of the missing genetic etiology in unexplained EOAD patients still has a vast potential to deliver novel crucial pieces that will lead to a better understanding of the complex puzzle of AD at large. Acknowledgments The research in the authors’ group is funded in part by a MetLife Foundation Award for Medical Research (USA), a U.S. Army Medical Research and Material Command (USAMRMC) Research Award, a Janssen Pharmaceutica Stellar Research Project, the Belgian Science Policy Office Interuniversity Attraction Poles program, the Alzheimer Research Foundation (SAO-FRA), the Flemish Government initiated Flanders Impulse Program on Networks for Dementia Research (VIND), the Flemish Government initiated Methusalem Excellence Program, the Research Foundation Flanders (FWO), and the University of Antwerp Research Fund, Belgium.

RESEARCH IN CONTEXT 9. Conclusions The renewed interest in EOAD in the NGS era has the potential to implement the knowledge of the molecular and cellular mechanisms which ultimately lead to AD. The indepth genetic characterization, by systematic screening of the dementia causal genes, will allow patient stratification in more homogenous groups, leading to the selection of unexplained EOAD, both familiar and sporadic patients to include in further research. This can be gene hunting projects or trials for biomarkers selection or compound testing, paving the way to personalized medicine. In parallel to the deep genetic profiling of patients cohorts, the longitudinal follow-up of families with known mutations will provide additional insights in the known disease process that could have implications for diagnostics, for the development of predictive tests, and for clinical trial design. In a future prospective, the integration of NGS with data coming from other–omics analysis (i.e., epigenomics, proteomics, transcriptomics, and metabolomics), might lead to the identification of molecular disease signatures, identifying pattern(s) of altered protein expression that are better able to discriminate early disease stages and facilitate the identification of novel causal proteins and pathways or to prioritize genes and proteins as “druggable” molecular targets for novel therapeutic approaches. In conclusion, tremendous

1. Systematic review: PubMed search, meeting abstracts and presentations, were used to collect information concerning Alzheimer disease (AD). The search focus was on the genetics of early-onset AD (EOAD), on the application of high-throughput sequencing technologies and on ongoing translational studies on EOAD. 2. Interpretation: We provide a comprehensive review on EOAD genetics. Despite the tremendous advance in the field, a large part of the EOAD patients is still unexplained. Thus, possible molecular mechanisms that might underlie the missing genetic etiology of EOAD are explored. 3. Future directions: The basis of the current knowledge of AD is derived from genetic studies on large EOAD pedigrees in the early 90s. Here, we suggest that the use of high-throughput sequencing technologies applied to EOAD patients has a vast potential to deliver crucial information to help in better understanding the molecular mechanisms of AD in general.

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