Abstract

   Alzheimer’s disease (AD) is the most common type of neurodegenerative
   dementia, but the cause of AD remained poorly understood. Many
   mutations in the amyloid precursor protein (APP) and presenilin 1 and 2
   (PSEN1 and PSEN2) have been reported as the pathogenic causes of
   early-onset AD (EOAD), which accounts for up to 5% of all AD cases. In
   this study, we screened familiar/de novo EOAD (n = 67) samples by
   next-generation sequencing (NGS) of a 50-gene panel, which included
   causative and possible pathogenic variants linked to neurodegenerative
   disorders. Remarkably, three missense mutations in PSEN1 (T119I, G209A,
   and G417A) and one known variant in PSEN2 (H169N) were discovered in 6%
   of the cases. Additionally, 67 missense mutations in susceptibility
   genes for late-onset AD were identified, which may be involved in
   cholesterol transport, inflammatory response, and β-amyloid modulation.
   We identified 70 additional novel and missense variants in other genes,
   such as MAPT, GRN, CSF1R, and PRNP, related to neurodegenerative
   diseases, which may represent overlapping clinical and
   neuropathological features with AD. Extensive genetic screening of
   Korean patients with EOAD identified multiple rare variants with
   potential roles in AD pathogenesis. This study suggests that
   individuals diagnosed with AD should be screened for other
   neurodegenerative disease-associated genes. Our findings expand the
   classic set of genes involved in neurodegenerative pathogenesis, which
   should be screened for in clinical trials. Main limitation of this
   study was the absence of functional assessment for possibly and
   probably pathogenic variants. Additional issues were that we could not
   perform studies on copy number variants, and we could not verify the
   segregation of mutations.

   Subject terms: Next-generation sequencing, Developmental neurogenesis

Introduction

   Alzheimer disease (AD) is a devastating neurodegenerative disease
   accounting for 50–75% of all forms of dementia. Approximately 44
   million people worldwide were estimated to be diagnosed with AD or a
   related dementia in 2015^[34]1. Approximately 4.6 million new cases of
   dementia are reported annually, and the number of AD patients is
   expected to nearly double by 2030^[35]1. Genetic factors may explain
   many of the variations affecting AD risk, particularly familial AD and
   early-onset AD (EOAD), in which most genetic variants are related to
   amyloid-β (Aβ) processing^[36]2–[37]4. EOAD is a subtype of AD in which
   disease onset occurs before the age of 65 years, but several patients
   develop AD in their 30 s or 40s^[38]5–[39]8. Three genes have been
   identified as causative factors for EOAD: amyloid precursor protein
   (APP)^[40]5,[41]9, presenilin 1 (PSEN1)^[42]6–[43]8,[44]10, and
   presenilin 2 (PSEN2)^[45]11,[46]12. To date, >270 highly penetrant
   mutations (http://www.molgen.ua.ac.be/admutations/;
   [47]http://www.alzforum.org/mutations) in these genes were reported to
   cause familial AD, and emerging studies continue to report additional
   novel mutations^[48]13–[49]15. However, few cases of high-penetrant
   mutations in APP, PSEN1, and PSEN2 can explain the causality in EOAD
   families (5–10%), with a large group of autosomal dominant pedigrees
   unexplained genetically^[50]16–[51]18. Patients with late-onset AD
   (LOAD) are more common in people at the age of 65 years or
   older^[52]15,[53]19. Large-scale genome-wide association studies (GWAS)
   identified >20 genetic loci, including APOE, associated with increased
   susceptibility to LOAD, which could be involved in the pathway of Aβ
   production and clearance^[54]20,[55]21. The heritability of LOAD was
   estimated to be approximately 80%^[56]15,[57]22. Thus, a better
   understanding of genetic susceptibilities would be essential for early
   detection and treatment^[58]23–[59]27. Studies of the differences in
   heritability and age of onset between EOAD and LOAD may explain why
   EOAD patients have a more penetrant genetic etiology, thus, providing
   insight for discovering the genetic causes of AD.

   Clinically, many types of dementia may have neuropathological or
   clinical crossover. For example, Parkinson’s disease (PD) patients both
   with or without dementia exhibit alpha-synuclein deposits in the brain,
   and patients with dementia with Lewy bodies can show similar clinical
   presentations^[60]14. Many similarities in genetic variants were
   reported between different neurodegenerative disorders, such as rare
   TREM2 variants in AD^[61]28, while TREM2 p.R47H was reported as a risk
   variant for PD, frontotemporal dementia (FTD), and amyotrophic lateral
   sclerosis (ALS)^[62]29,[63]30. Remarkably, LOAD is genetically far more
   complex than EOAD, and some genetic loci were shown to be associated
   with multiple types of dementias; the most common example is APOE ε4,
   which was suggested to be associated with AD, PD, and other
   neurodegenerative diseases^[64]21. More interestingly, a recent study
   demonstrated that PARK2 (p.T240M, p.Q34fs delAG) variants in
   early-onset PD and MAPT (p.A469T) are also associated with de novo
   EOAD^[65]31. Sporadic neurodegenerative diseases, such as PD, AD,
   multiple system atrophy, Creutzfeldt-Jakob disease (CJD), ALS, and
   corticobasal degeneration disease, can present similar clinical
   phenotypes as progressive supranuclear palsy, which may result in a
   false diagnosis^[66]32,[67]33. These findings suggest the need for a
   detailed screening of patients with EOAD to improve differential
   diagnosis, as may harbor potential causative variants of PD and FTD.

   Thus, although many risk genes have been reported for AD, the
   identification of additional disease-associated genes remained
   challenging. Recently, next-generation sequencing (NGS) technologies
   have been used to examine patients with EOAD patients with unknown
   etiology^[68]34, NGS may provide fast and cost-effective sequencing
   strategies and sequence an entire genome in >1 day. In this study, an
   NGS panel of 50 genes described previously^[69]19 was used to evaluated
   67 Korean patients with EOAD.

Results

PSEN1 and PSEN2 variants

   In PSEN1, three mutations, p.Thr119Ile (c.356 C > T), p.Gly209Ala
   (c.626 G > C), and, p.Gly417Ala (c.1250 G > C), were identified
   (Table [70]1, Fig. [71]1). PSEN1 p.Thr119Ile was identified in an EOAD
   patient in whom disease onset occurred at 64 years. The first symptoms
   appeared in 2012 and included memory impairment and language problems.
   Mini-Mental State Examination (MMSE)and Clinical Dementia Rating (CDR)
   scores were 28 and 0.5, respectively, and the patient was definitely
   diagnosed with AD. Analysis by 18 F-fludeoxyglucose and positron
   emission tomography (FDG-PET) revealed decreased metabolism in the
   bilateral, parietal, and temporal cortices. PSEN1 Thr119Ile is located
   in a conservative HL-I, and a similar pathogenic mutation, Thr116Ile,
   was found in the same loop. Thus, Thr119Ile may present similar effects
   as the Thr116Ile mutation^[72]7. To validate the NGS data, a positive
   sample was used for screening^[73]35, in which a novel missense
   mutation was reported, p.Gly209Ala (c.626 G > C). The G-to-A
   substitution at nucleotide 209 (Gly209Ala) in PSEN1 gene was observed
   in 54-year-old right-handed female patient who presented a 12-year
   history of progressive memory decline. Interestingly, PSEN1 Gly417Ala
   was found in a 37-year-old male patient diagnosed with symptoms that
   overlap between AD and Parkinsonism, followed by progressive language
   disturbance and behavioral changes with Parkinsonism. PSEN1 Gly417Ala
   is in the transmembrane domain-VIII and may result in increased stress
   inside the transmembrane helix (TM), as alanine is more hydrophobic
   than glycine. Additionally, this mutation may impair splicing of the
   PSEN1 transcriptome, as it is adjacent to the splice site^[74]6. In
   PSEN2, a previously reported^[75]12,[76]36 mutation, p.His169Asn
   (c.505 C > A), was found in a 63-years-old-female patient with EOAD who
   developed cognitive decline, memory problems, and language impairment
   beginning in 2009. Korean Mini Mental State Examination (K-MMSE) score
   was 0 and CDR score was 3, revealing that the patient had severe
   dementia. Initially, PSEN2 His169Asn was identified in two unrelated
   Chinese individuals, one affected by familial LOAD (age at onset of 62
   years) and the other by apparently sporadic FTD (age at onset of 68
   years).

Table 1.

   Genetic, clinical and pathological characteristics of definite AD cases
   carrying pathogenic mutations or risk variants of PSEN1 and PSEN2.
   Genes DNA change Protein change AOO Gender ApoE ExAC frequency
   PolyPhen2
   HumDiv Sift score Provean Family history Clinical features
   PSEN1

   c.356 C > T

   (Exon 5)
   p.Thr119Ile 64 F ε33 Novel

   0.9

   (D)

   0.06

   (T)

   −2.37

   (N)
   Unknown EOAD

   c.626 G > C

   (Exon 7)
   p.Gly209Ala 54 F ε33 Novel

   1

   (D)

   0

   (D)

   −5.67

   (D)
   Probable positive AD, depression

   c.1250 G > C

   (Exon 12)
   p.Gly417Ala 37 M ε33 Novel

   0.99

   (D)

   0

   (D)

   −5.33

   (D)
   Unknown AD with Parkinsonism
   PSEN2

   c.505 C > A

   (Exon 6)
   p.His169Asn 59 F ε33 0.0001648

   0.925

   (D)

   0.04

   (D)

   −6.33

   (D)
   Unknown Left dominant AD
   [77]Open in a new tab

   Abbreviations: AD, Alzheimer’s disease; PSEN1, presenilin 1; PSEN2,
   presenilin 2; AOO, age of onset; ApoE, Apolipoprotein E; ExAC, the
   Exome Aggregation Consortium; EOAD, early-onset Alzheimer disease; F,
   female; M, male; D, damaging; T, tolerant; N, neutral.

Figure 1.

   [78]Figure 1
   [79]Open in a new tab

   Detection of PSEN1 and PSEN2 genes mutations in early-onset Alzheimer’s
   disease. (A) DNA sequencing data of the four mutations found in this
   study. (B) Location of PSEN1 and PSEN2 mutations in the genomic DNAs
   and protein. (C) Possible protein structure changes, associated with
   the PSEN1 and PSEN2 mutations, where the differences of normal and
   mutant these proteins have been highlighted in the black circle, and
   normal was labeled with yellow, while mutant was labeled with pink. In
   the protein structure, red means the alpha helices or TM regions, and
   grey is for the hydrophilic loops (HL) while green means the kinks in
   the helices or loops.

Mutations in genes-associated with LOAD

   Given that three EOAD-related genes (APP, PSEN1, and PSEN2) contained
   no mutations, we identified 67 non-synonymous variants in the LOAD risk
   factor genes. Possible pathogenic mutations or risk variants were found
   in S100A9, CR1, BIN1, CLU, CTNNA3, DNMBP, SORL1, BACE1, GAB2, LPR6,
   ADAM10, ABCA7, and CD33 (Table [80]S2). No missense mutations were
   identified in either TREM2 or in PICALM among the cohort. Of these, we
   analyzed the 32 rare coding variants (minor allele frequency <1%).
   Remarkably, recent studies confirmed the role of rare variants in SORL1
   and ABCA7 in both EOAD and LOAD risk^[81]37–[82]41 which have been
   shown to increase Aβ40 and/or Aβ42 secretion when expressed in
   vitro^[83]37. In this study, 10 missense variants were identified in
   SORL1. Remarkably, approximately 33% (22/66) of patients in the cohort
   carried a common variant (rs2298813-p.Ala528Thr), which was
   demonstrated to increase Aβ42 secretion in AD cases by altering Aβ
   levels and interfering with APP trafficking^[84]37,[85]38.
   Additionally, we identified a total of 24 missense variants in ABCA7 in
   the Korean AD cohort referred for medical genetic testing. Based on in
   silico functional analysis, many variants were probably predicted as
   damaging. Interestingly, a stop-gain mutation in ABCA7 (p.Trp1214Ter
   (rs201060968) was suggested as evidence of loss-of-function mutations
   that may be related to the pathogenetic mechanism of AD^[86]42, which
   also was found in this study from a patient with EOAD. Additionally,
   one frameshift mutation of 7 base pairs (p.Asp540fs), leading to a
   premature stop codon, was highly significant and should be further
   examined. Notably, because clusterin (CLU) was suggested as the second
   highest genetic risk factor for AD, the novel variant in CLU Arg127His
   was identified in an EOAD patient. These findings may support the
   important roles of CLU mutations in the AD pathogenesis. In summary, we
   found several probable significant novel mutations, and these variants
   commonly associated with LOAD susceptibility genes may have a
   biological function related to AD. These findings suggested that these
   LOAD gene defects played important roles in the pathogenesis of the AD
   cohort, which may explain the missing genetic etiology of EOAD.

Mutations in neurodegeneration-associated genes

   The overlapping clinical and neuropathologic features between AD and
   other neurodegenerative dementias (FTD, corticobasal degeneration
   [CBD], progressive supranuclear palsy [PSP], and CJD) may result
   misdiagnosis in 17–30% of AD cases^[87]43. Additionally, because rare
   high-penetrant mutations in APP, PSEN1, and PSEN2 explained only a
   small fraction of EOAD families, leaving a large group of autosomal
   dominant pedigrees genetically unexplained. To fill this gap in
   knowledge, we explored the frequencies and spectrum of mutations in
   genes previously implicated in EOAD by NGS of a cohort of patients with
   pathologically confirmed EOAD. We identified 70 non-synonymous variants
   in the patients in 21 dementia-related genes, namely SIGMAR1, FUS, GRN,
   MAPT, ALS2, TAF15, FIG. 4, OPTN, DAO, HNRNPA1, PINK1, PARK7, SNCA,
   ATP13A2, PARK2, LRRK2, SPAST, SPG11, CSF1R, NOTCH3, and PRNP. In
   contrast, no missense mutation were found in TARDBP, CHMP2B, VCP,
   UBQLN2, SOD1, ANG, VAPB, SQSTM1, GBA, or CYP7B1 (Table [88]S3). Based
   on these results, the underlying genetics of AD may be more complex and
   heterogeneous than previously thought, as many potential variants are
   present in genes associated with other forms of neurodegeneration as
   PD, FTD, ALS, and also Prion diseases. This may support previous
   findings demonstrating the existence of pathogenic mutations in a wide
   spectrum of clinical neurological disorders including AD, FTD, ALS, and
   PSP in AD and all types of dementia^[89]44. Remarkably, two known
   heterozygosity mutations, Gly219Lys and p.Met129Val in PRNP, were
   detected in these EOAD cases at a high frequency in this study.
   Approximately 3.3% (1/67) of AD patients showed heterozygosity in codon
   129 (Met/Val alleles), whereas codon 219 (Glu/Lys alleles) was also
   detected in 10.9% (7/67) of cases, suggesting whether these common
   variants are benign polymorphisms (Table [90]S4). A possible risk
   factor for CJD, PRNP p.Met232Arg, was also identified in an EOAD case,
   suggesting that PRNP mutations were present in Korean patients with AD.
   Although rare cases of co-occurrence of cerebral autosomal dominant
   arteriopathy with subcortical infarcts and leukoencephalopathy
   (CADASIL) and AD have been described^[91]45–[92]47, identifying the
   proteins involved in the pathologic process of both diseases would
   validate the results of our cohort study. Interestingly, 8 genetic
   variants were found in NOTCH3, but none were found to alter cysteine
   residues in the protein. In contrast, significant pathological overlap
   was found between FTD and AD for two GRN mutations, Ser40Asn and
   Arg564Cys in exon 1 and exon 12, respectively. Arg564Cys was reported
   as a mutation with unclear function, but was suggested to be associated
   with AD^[93]48. Our findings suggested that several mutations in GRN
   may be risk factors for AD, and pathological and genetic overlap may be
   possible between these disorders^[94]49. We cannot exclude the
   possibility that some of these variants of unknown significance will be
   reclassified in the future based on putative functional and/or genetic
   arguments or may confer an increased risk for developing AD. These data
   will help advance our understanding of dementia-related genes in Korean
   and East Asian populations diagnosed with AD.

In silico gene/protein functional interaction network

   The understanding of the molecular mechanisms underlying AD remains
   incomplete. Because AD is considered as a complex disease thought to
   result from the interactions of many genes and environmental factors,
   each gene may have only a small effect on the disease. Most previous
   studies focused on candidate genes and studied each gene individually,
   either evaluating one marker at a time or forming haplotypes over
   multiple neighboring loci in and around one gene. In addition, the
   effects of genes on phenotype result from physical interactions in gene
   regulatory networks and biochemical pathways in an individual. The
   multifactorial nature of this complex disease suggests that gene–gene
   interactions within biological networks may be evaluated by Gene
   Ontology^[95]50. Given that many mutations have been identified in the
   gene panel from the EOAD cohort, the roles of these genes and their
   interactions in AD remain unclear. Thus, we conducted interaction
   network analysis to assess 35 gene–gene (Tables [96]S2 and [97]S3)
   interactions which may be associated with AD using ClueGo. As a result,
   the major biological themes linked to these genes were revealed by
   function and biochemical pathway enrichment analysis, and relationships
   between pathways were also observed, suggesting involvement of the Aβ
   metabolic process (Fig. [98]2). ClueGo analysis revealed a complex
   network of genes that may play a role in diverse mechanisms. PSEN1 and
   PSEN2 may be involved in amyloid mechanisms, such as amyloid beta
   formation and APP metabolism/catabolism. Other genes potentially also
   play a direct or indirect role in amyloid metabolism, including SORL1,
   ABCA7, CLU, SNCA, and LRRK2. Several genes may be involved in other
   mechanisms, such as by regulating cellular responses to oxidative
   stress or synaptic transmission (PARK7, SPAST, MAPT, ALS2, DAO, PARK2,
   PARK7, and PRNP). However, many of these interactions require further
   investigation. Further studies of these pathways may increase the
   understanding of the patho-mechanisms related to the mutations. These
   results may lead to improved therapeutics targeting with Aβ
   accumulation as the dominant factor.

Figure 2.

   [99]Figure 2
   [100]Open in a new tab

   Modular partitions of the risk genes were obtained by network
   decomposition with the ClueGo algorithm. Gene nodes were sized by
   connectivity and partitioned to Modules 1–10. Potential pathways which
   could play a role in amyloid mechanism, such as amyloid beta formation,
   APP metabolism/catabolism are displayed.

Discussion

   Recent studies demonstrated that mutations in AD-related Mendelian
   genes, including APP, PSEN1, and PSEN2, cause, contribute to, and
   modify the risk of AD^[101]51,[102]52. Due to the clinical and
   pathologic overlap between AD and other neurodegenerative diseases, a
   expanding genetic contribution toward AD risk by genes involved in the
   diseases would be considered for some time now. However, to demonstrate
   that the variants contribute to disease at a genome-wide level requires
   statistical analysis of large cohorts and well-characterized
   populations. The frequency of rare mutations in non-Mendelian genes
   should also be determined, as these variants may contribute to disease
   through different mechanisms, such as through the impairment in amyloid
   metabolism, inflammatory pathways, or lipid metabolism. Because of the
   phenotypic similarity of patients, extensive genetic screening should
   be performed for those diagnosed with EOAD. AD and FTD may show similar
   symptoms and pathology, and several mutations in GRN and MAPT have been
   observed among patients with AD. Additionally, mutations in PD-causing
   genes have frequently been detected in EOAD, suggesting that these
   variants contribute to AD onset^[103]53.

   In this study, we examined whether the significant phenotypic overlap
   between family/sporadic EOAD and other neurodegenerative diseases (PD,
   FTD, ALS, and CJD) could be explained by the common genetic background
   of patients. Recently, 210 familial AD-associated mutations
   (www.molgen.ua.ac.be/ADMutations) were linked to PSEN1, the catalytic
   subunit of the γ-secretase complex^[104]54, most of which lead to an
   increased proportion of Aβ42^[105]55. These mutations may be associated
   with gain-of function or loss-of function effects. Previously, the
   estimated mutation frequencies in an EOAD patient cohort for the three
   genes were <1% for APP, 6% for PSEN1, and 1% for PSEN2^[106]18. Thus,
   we developed a panel of 50 genes to evaluate whether the rare coding
   variability of these genes is responsible for a high proportion of EOAD
   risk^[107]19. In our EOAD cohort, we identified three rare variants of
   PSEN1 (p.Thr119Ile, p.Gly209Ala, and p.Gly417Ala) and a rare known
   variant in PSEN2 (p. His169Asn). A novel mutation in exon 5 of
   presenilin-1 (Thr119Ile) in a Korean patient with EOAD who showed
   memory decline at 64 years of age followed by memory impairment,
   language problems, and personality changes. This mutation was not found
   in any public databases, indicating that the polymorphism is rare.
   Because isoleucine may disrupt helix structures, substitution of
   threonine for isoleucine at codon 119 may have significantly disturbed
   PSEN1 protein functions. This hypothesis is supported by the results of
   in silico analyses which predicted that the T119I mutation results in
   major helix torsion. Several threonine > isoleucine substitutions,
   identified in the conservative hydrophilic loop-I of PSEN1, were
   associated with aggressive AD phenotype. Additionally, PSEN1
   p.Gly209Ala is in the transmembrane–IV region of the PS1 protein, where
   several other pathogenic variants were reported at the same residue,
   including Gly209Arg, Gly209Glu, and Gly209Val
   (http://www.molgen.ua.ac.be/ADMutations/). This may be a pathogenic
   variant linked to a related phenotype in EOAD and was suggested as a
   possible novel pathogenic variant in PSEN1 during structural prediction
   using the PSEN1 mutation. Remarkably, a known pathogenic mutation in
   exon 6 of PSEN2 was identified in a Korean patient with EOAD who showed
   memory decline at 59 years of age, followed by memory impairment,
   language problems, and personality changes. Previously, this mutation
   was reported in a patient with familial late-onset AD with progressive
   memory loss beginning at the age of 68 years and in 1 sporadic
   early-onset patient with FTD with gradual onset of symptoms starting at
   62 years of age^[108]36. This residue is in exon 6, transmembrane
   domain III, which is conserved in PSEN1 His169, where variants
   (His163Pro, His163Tyr, and His163Arg) have been described as pathogenic
   mutations ([109]http://www.alzforum.org)^[110]12. In total, less than
   6% of patients with EOAD in this study carried these mutations, which
   explained only 5–10% of patients with EOAD as previously
   reported^[111]15,[112]17,[113]18.

   Based on this findings, majority of early onset AD cases in the cohort
   remain genetically unexplained, which were also demonstrated the
   missing genetic etiology of EOAD (90–95%)^[114]16. The large number of
   patients with EOAD of unknown genetic background and a lack of
   statistical power reinforces the fact that additional causal genes need
   to be identified. Recent meta-analysis of GWASs identified at least 22
   genes implicated in LOAD as described above, which may play a role in
   AD pathogenesis, although their functional role and significance remain
   unclear^[115]15. In this NGS study, several novel and known AD-related
   mutations were found in the cohort of 67 patients with AD, which may be
   associated with neurodegeneration. We also identified several mutations
   in other genes that cause neurodegenerative disease (such as FTD, PD,
   prion, or ALS). Although numerous studies have identified different
   genetic risk factors, including the ε4 allele of APOE, genetic variants
   have not been integrated with genetic epidemiology to quantify the age
   of AD onset. More recently, a study developed a polygenic hazard score
   for quantifying individual differences in the age-specific genetic risk
   for AD and found that the polygenic architecture plays an important
   role in modifying the AD risk beyond APOE^[116]15. In this study, most
   patients carried several validated variants within the panel of 50
   genes. Our results revealed important additional variants associated
   with other genes; APP, PSEN1, PSEN2, and APOE genetic variants were not
   shown to contribute to the disease etiology. Therefore, it may be
   difficult to identify genetic variants associated with the disease
   mechanism of AD and were found in more than two patients. Given the
   flexibility of our genetic findings, our results are useful for
   investigating whether a combination of common and rare genetic variants
   along with clinical, cognitive, and imaging biomarkers are useful for
   diagnosing AD onset.

   In contrast, ClueGo pathway analysis revealed complex interactions
   between genes. In addition to PSEN1 and PSEN2, several additional
   AD-risk genes may play a role in amyloid peptide formation and
   metabolism, such as SORL1, ADAM10, ABCA7, and CLU^[117]56.
   Additionally, the ClueGo interaction also suggested that other
   neurodegenerative disease-causing genes impact amyloid-associated
   pathways, such as LRRK2, PARK7, or SNCA. LRRK2 mutants were associated
   with reduced Aβ levels in patients with CSF of PD^[118]57. LRRK2 was
   suggested as potential risk factor for AD^[119]58. Mutated LRRK2
   protein may enhance the phosphorylation of the intracellular domain of
   APP, resulting in neurotoxicity^[120]59. SNCA may also impact AD onset,
   as the interaction of Aβ peptides and α-synuclein interaction may
   result in neurotoxicity through enhanced oxidative stress, impaired
   calcium metabolism, and abnormal mitochondrial pathways^[121]60. PARK7
   or DJ1 protein may impact AD onset, as their expression levels were
   elevated in cases of oxidative stress^[122]61. Additional gene
   interactions were observed in the response to oxidative stress.
   Oxidative stress has been suggested to play a significant role in
   neurodegeneration and the onset of different neurodegenerative
   diseases, including in AD.

   There were some limitations to this study. For example, we could not
   screen our patients for copy number variants (CNVs) or repetitive
   element expansions, such as duplication in APP or PRNP insertions and
   deletions^[123]62. We also could not screen for the G4C2 repeat
   expansion of the c9orf72 promoter region, which was established as
   causative factor for FTD and ALS. However, abnormal c9orf72 repeat
   expansions may be rare in Asian (Korean, Japanese) populations^[124]13.
   Additionally, we could not perform in vitro cell studies to verify the
   importance of novel mutations in AD risk genes^[125]63. Further, family
   members of patients refused genetic testing, and segregation of rare
   mutations could not be screened.

   Our study confirmed that mutations in APP, PSEN1, and PSEN2 are
   relatively rare among patients with EOAD^[126]14,[127]53. Rare missense
   mutations were also found in additional causative genes, such as GRN,
   MAPT, LRRK2, NOTCH3, or PRNP, which may contribute to
   neurodegeneration^[128]64. We also found several mutations, including
   novel mutations in AD risk factor genes, such as SORL1, ABCA7, or CD33.
   Other genes may play a role in AD onset but were not included in our
   gene panel. We plan to perform whole exome sequencing (WES) of patients
   in whom no mutations were found in APP, PSEN1, or PSEN2. Recently,
   several possible candidate genes have been identified and suggested to
   be involved in neurodegeneration. Whole exome sequencing studies may be
   helpful for additional genetic profiling of patients with AD and
   dementia and to improve diagnosis as well as broaden the knowledge of
   the possible genes and pathways associated with
   neurodegeneration^[129]62.

   Renewed interest in EOAD in the NGS era may improve the knowledge of
   the molecular and cellular mechanisms that ultimately lead to AD.
   In-depth genetic characterization conducted by systematic screening of
   dementia-causing genes, will allow for patient stratification in more
   homogenous groups, leading to the selection of unexplained EOAD in both
   familial and sporadic patients that can be further analyzed. This may
   be accomplished through gene identification studies or trials for
   biomarker selection or compound testing, providing a foundation for
   personalized medicine. We cannot exclude the possibility that some of
   these variants of unknown significance will be reclassified in the
   future based on putative functional and/or genetic arguments or may
   confer an increased risk for developing AD. Additional studies will
   advance our understanding of dementia-related genes in Korean and East
   Asian subjects diagnosed with AD.

Materials and Methods

Case selection

   The average age of EOAD diagnosis was 58.1 years (n = 67). All patients
   were diagnosed as either definite or probable AD according to National
   Institute of Neurological and Communicative Disorders and Stroke and
   the Alzheimer’s disease and Related Disorders Association
   (NINCDS-ADRDA) and Consortium to Establish a Registry for Alzheimer’s
   Disease (CERAD) guidelines^[130]65. The family history of patients was
   also checked. A positive family history means that the patient had at
   least one family member, affected with dementia or some type of
   neurodegenerative disease. However, we could not verify the family
   history because all family members and relatives declined genetic
   testing.

   DNA was extracted from blood samples using the GeneAll blood kit
   (Seoul, Republic of Korea) following the manufacturer’s protocol. DNA
   quality and quantity were assessed by gel electrophoresis and with a
   NanoDrop 3300 spectrometer, respectively. All samples were checked for
   the APOE genotype using the EzWay™ Direct PCR method (Komabiotech,
   Korea) to detect the APOE ɛ2, ɛ3, and ɛ4 alleles (Fig. [131]S1 and
   Table [132]S1).

   As normal controls, we used the whole genome sequencing dataset of 622
   unaffected individuals from the Korean Reference Genome Database
   (KRGDB; [133]http://coda.nih.go.kr/coda/KRGDB/index.jsp). We also
   checked all variants against larger reference databases, including the
   Exome Aggregation Consortium (ExAC;
   [134]http://exac.broadinstitute.org/) and 1000Genomes database
   (http://www.internationalgenome.org/). As an internal positive control,
   one previously reported sample with a heterozygous missense mutation
   (PSEN1 G209A) was included^[135]35.

Panel design

   Fifty genes (Table [136]2) comprising 796 exons were ultimately
   selected for the targeted sequencing panel as previously
   reported^[137]19. The following candidate genes were evaluated: (1)
   previously reported as disease-causing mutations in patients with AD
   and (2) other risk factor genes of neurodegenerative diseases. A total
   of 876 pairs of primers were designed for the 796 exons of 50 genes and
   their surrounding regions. Briefly, 67 patients with EOAD were analyzed
   by NGS, following the schematic diagram shown in Fig. [138]3.

Table 2.

   List of 50 genes where causative or probably causative variants were
   reported to cause early-onset dementia.
   Disease categories No. of genes Candidate genes selection
   Alzheimer’s disease 19 APP, PSEN1, PSEN2, S100A9, CR1, BIN1, TREM2,
   CLU, CTNNA3, DNMBP, SORL1, BACE1, PICALM, GAB2, LPR6, ADAM10, ABCA7,
   CD33, TOMM40.

   Amyotrophic Lateral Sclerosis (ALS)

   & Frontotemporal dementia (FTD)
   18 TDP43, CHMP2B, SIGMAR1, VCP, FUS, GRN, MAPT, UBQLN2, ALS2, TAF15,
   FIG. 4, OPTN, DAO, HNRNPA1, SOD1, ANG, VAPB, SQSTM1.
   Dementia with Lewy Bodies 7 PINK1, PARK7, PARK9, GBA, SNCA, PARK2,
   LRRK2.
   Other neurodegenerative diseases 6 SPAST, CYP7B1, SPG11, CSF1R, NOTCH3,
   PRNP.
   [139]Open in a new tab

   Abbreviations: APP, amyloid precursor protein; PSEN1, presenilin 1;
   PSEN2, presenilin 2; S100A9, S100 calcium binding protein A9; CR1,
   complement receptor 1; BIN1, Bridging integrator 1; TREM2, triggering
   receptor expressed on myeloid cells 2; CLU, clusterin; CTNNA3, catenin
   alpha 3; DNMBP, dynamin-binding protein; SORL1, sortilin-related
   receptor; BACE1, Beta-secretase 1; PICALM, Phosphatidylinositolbinding
   clathrin assembly protein; GAB2, GRB2-associated binding protein 2;
   LPR6, Low-density lipoprotein receptor-related protein 6; ADAM10, A
   disintegrin and metalloprotease 10; ABCA7, ATP-binding cassette
   transporter A7; CD33, cluster of differentiation 33; TOMM40,
   translocase of outer mitochondrial membrane 40; TDP43, transactive
   response DNA binding protein 43 kDa; CHMP2B, charged multivesicular
   body protein 2B; SIGMAR1, sigma non-opioid intracellular receptor 1;
   VCP, valosin-containing protein; FUS, fused in sarcoma; GRN,
   progranulin; MAPT, microtubule associated protein tau; UBQLN2,
   ubiquilin 2; ALS2, amyotrophic lateral sclerosis 2; TAF15, TATA-box
   binding protein associated factor 15; FIG. 4, FIG. 4 phosphoinositide
   5-phosphatase; OPTN, optineurin; DAO, D-amino acid oxidase; HNRNPA1,
   heterogeneous nuclear ribonucleoprotein A1; SOD1, Superoxide dismutase
   1; ANG, Angiogenin precursor; VAPB, Vesicle-associated membrane
   protein-associated protein B; SQSTM1, Domain-specific mutations in
   sequestosome 1; PINK1, PTEN-induced kinase 1; PARK7, Parkinsonism
   associated deglycase 7; PARK9, Parkinson disease (autosomal recessive)
   9; GBA, glucocerebrosidase; SNCA, Alpha-synuclein; PARK2, Parkinson
   disease associated gene 2; LRRK2, Leucine-rich repeat kinase 2; SPAST,
   spastin; CYP7B1, cytochrome P450 7B1; SPG11, spastic paraplegia 11;
   CSF1R, colony stimulating factor 1 receptor; NOTCH3, Neurogenic locus
   notch homolog protein 3; PRNP, prion protein.

Figure 3.

   [140]Figure 3
   [141]Open in a new tab

   The NGS strategy for identifying variants in AD patients. The flow
   chart illustrated the main steps in the working procedure from the
   analysis of the patient sample to the identification of the mutations.

Ion Torrent PGM sequencing and data processing

   NGS was performed in an Ion Torrent PGM system by the Theragen Etex Bio
   Institute (Seoul, Republic of Korea,
   [142]http://www.theragenetex.com/). Fragment libraries were constructed
   by DNA fragmentation, and 20 ng of DNA were used for multiplex PCR of a
   panel covering 50 genes, where causative or probably causative variants
   were reported as shown in Table [143]2 (Ion AmpliSeq Customized Panel,
   Life Technologies, Carlsbad, CA, USA). Barcode and adaptor ligation and
   library amplification were performed using the Ion DNA Barcoding kit
   (Life Technologies, Grand Island, NY, USA) according to the
   manufacturer’s instructions. The size distribution of the DNA fragments
   was analyzed on the Agilent Bioanalyzer using the High Sensitivity Kit
   (Agilent Technologies, Santa Clara, CA, USA). Template preparation,
   emulsion PCR, and Ion Sphere particle (ISP) enrichment were performed
   using the Ion Xpress Template kit (Life Technologies, Grand Island, NY,
   USA) according to the manufacturer’s instructions. The ion sphere
   particles were loaded onto a P1 chip and sequenced with an Ion P1
   sequencing 200 kit (Life Technologies, Grand Island, NY, USA).

   Data analyses, including alignment to the hg19 human reference genome
   and variant calling, were performed using Torrent Suite Software
   v.4.4.3 (Life Technologies). Variants were filtered by Bam-Utils
   v1.0.2. Filtered variants were annotated using SnpEff v4.2.
   Additionally, Integrative Genome Viewer (IGV) software
   ([144]http://software.broadinstitute.org/igv/) was used for mutation
   analysis. The joint variant calling file (VCF) was annotated with
   refGene gene regions, single-nucleotide polymorphism (SNP) effects, and
   functional effect prediction tools, as well as Exome Variant Server
   (EVS) and 1000 Genomes minor allele frequencies (MAFs) using Annovar
   ([145]http://www.openbioinformatics.org/annovar/). For all mutations,
   the variants were interpreted using the Human Gene Mutation Database
   (HGMD, [146]www.hgmd.cf.ac.uk), AD&FTD
   ([147]www.molgen.ua.ac.be/admutations/), and AlzForum
   (alzforum.org/mutations) databases and by literature searches.

Verification of mutations by Sanger sequencing

   To confirm the presence of mutations, automated Sanger sequencing
   reactions were performed by BioNeer, Inc. (Daejeon, Korea,
   [148]http://eng.bioneer.com/home.aspx) using previously reported primer
   sets^[149]19. Prior to sequencing, PCR products were purified with the
   GeneAll PCR protocol kit (Seoul, Korea) following the manufacturer’s
   protocol. Big Dye Terminator Cyclic sequencing was performed on an ABI
   3730XL DNA Analyzer. Sequencing text data were aligned by NCBI Blast
   ([150]http://blast.ncbi.nlm.nih.gov/Blast.cgi), and chromatograms were
   screened with DNA BASER ([151]http://www.dnabaser.com) software.
   Mutations and sequence variants were identified by the NCBI Gene
   ([152]http://www.ncbi.nlm.nih.gov/gene) and UniProt
   ([153]http://www.uniprot.org) databases.

Gene functional interaction network and in-silico protein modeling

   In this study, 50 genes and their ontology were further analyzed to
   evaluate their functional influences on AD metabolic pathways by ClueGO
   v2.0.5. This tool visualized non-redundant biological networks of large
   clusters of genes, which were grouped into functional networks by
   statistical evaluation with respect to existing annotations in Gene
   Ontology^[154]50.

   These protein change predictions were determined using SIFT
   ([155]http://sift.jcvi.org/) and PolyPhen-2
   ([156]http://genetics.bwh.harvard.edu/pph2). PolyPhen2 uses a
   clustering algorithm and performs multiple alignment to select
   sequences. The algorithm uses eight sequence-based and three
   structure-based features for prediction, which are based on comparison
   of wild-type and mutant proteins. Two types of datasets can be used,
   HumDiv and HumVar data. HumDiv data is used to determine the role of
   rare alleles in complex disease phenotypes and natural selection.
   Alleles that may be less damaging should also be treated as possibly
   pathogenic. HumVar data is used to diagnose Mendelian disorders and
   requires the differentiation of highly damaging mutations from less
   damaging variants. SIFT uses different protein databases, such as
   SWISS-PROT, SWISS-PROT/TrEMBL, or protein databases of NCBI to
   calculate the possibility of the pathogenic nature of mutations by
   comparing mutant and normal alleles. This program scores amino acid
   substitutions; when the values are greater or less than 0.05, mutations
   can be defined as deleterious or tolerated, respectively. Additionally,
   PROVEAN ([157]http://provean.jcvi.org/index.php) was used to determine
   the nature of mutations as benign or possible damaging.

   Furthermore, some significant damaging missense mutations were further
   probed to determine the structures of normal and mutant protein changes
   by the RaptorX web server ([158]http://raptorx.uchicago.edu/), a
   protein structure prediction server, using amino acid sequences.
   RaptorX uses a few measures including P-value, score, un-normalized
   global distance test (uGDT), and global distance test (GDT), uSeqID,
   and SeqID to evaluate the quality of a predicted 3D structure model.
   Next, Discovery Studio 3.5 Visualizer from Accelrys was used to display
   superimposed images.

Ethical approval

   The current project received ethics approval from the Seoul National
   University College of Medicine in Seoul National Bundang Hospital
   (SNUH) and written informed consent was obtained from all participants
   according to the requirements of the Seoul National Bundang Human
   Research Committee. All procedures involving human participants were
   conducted in accordance with the ethical standards of the institutional
   and/or national research committee and 1964 Helsinki Declaration and
   its later amendments.

Supplementary information

   [159]Supplementary information^ (626.6KB, pdf)

Acknowledgements