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Mapping the genetic landscape of early-onset Alzheimer’s disease in a cohort of 36 families

Alzheimer's Research & Therapy volume 14, Article number: 77 (2022) Cite this article

Abstract

Background

Many families with clinical early-onset Alzheimer’s disease (EOAD) remain genetically unexplained. A combination of genetic factors is not standardly investigated. In addition to monogenic causes, we evaluated the possible polygenic architecture in a large series of families, to assess if genetic testing of familial EOAD could be expanded.

Methods

Thirty-six pedigrees (77 patients) were ascertained from a larger cohort of patients, with relationships determined by genetic data (exome sequencing data and/or SNP arrays). All families included at least one AD patient with symptom onset <70 years. We evaluated segregating rare variants in known dementia-related genes, and other genes or variants if shared by multiple families. APOE was genotyped and duplications in APP were assessed by targeted test or using SNP array data. We computed polygenic risk scores (PRS) compared with a reference population-based dataset, by imputing SNP arrays or exome sequencing data.

Results

In eight families, we identified a pathogenic variant, including the genes APP, PSEN1, SORL1, and an unexpected GRN frameshift variant. APOE-ε4 homozygosity was present in eighteen families, showing full segregation with disease in seven families. Eight families harbored a variant of uncertain significance (VUS), of which six included APOE-ε4 homozygous carriers. PRS was not higher in the families combined compared with the population mean (beta 0.05, P = 0.21), with a maximum increase of 0.61 (OR = 1.84) in the GRN family. Subgroup analyses indicated lower PRS in six APP/PSEN1 families compared with the rest (beta −0.22 vs. 0.10; P = 0.009) and lower APOE burden in all eight families with monogenic cause (beta 0.29 vs. 1.15, P = 0.010). Nine families remained without a genetic cause or risk factor identified.

Conclusion

Besides monogenic causes, we suspect a polygenic disease architecture in multiple families based on APOE and rare VUS. The risk conveyed by PRS is modest across the studied families. Families without any identified risk factor render suitable candidates for further in-depth genetic evaluation.

Introduction

Alzheimer’s disease (AD) is the leading cause of dementia worldwide [1]. The typical clinical presentation includes progressive memory loss and deficits in other cognitive domains such as orientation, language, and problem solving [2]. In most patients, the first symptoms occur after the age of 65, whereas in ~5% the disease manifests earlier in life. Genes play an important role in the etiology and so far >70 different loci have been associated with AD, implicated in a variety of functional pathways [3, 4]. The heritability of non-Mendelian, late-onset AD (LOAD) is estimated around 60–80% [5] with the ε4 polymorphism in the APOE gene as the most common risk factor. A threefold increased AD risk is observed when carrying one APOE-ɛ4 allele, and 8-to-12-fold in homozygous carriers [6]. The heritability estimates of early-onset AD (EOAD) are substantially higher (92–100%). There are three known Mendelian AD genes: APP, PSEN1, and PSEN2, which account for 5–10% of EOAD cases [7].

Advances in genetic research techniques and larger sample sizes have enabled the discovery of other variants and susceptibility loci [7, 8]. Genome-wide association studies (GWAS) typically expose common variants with considerate population frequency, but relatively low disease penetrance. Large GWAS within the AD field have uncovered both risk (e.g., BIN1, CR1) and protective associations (e.g., CLU, PICALM) [4]. In contrast, next-generation sequencing usually reveals rare variants with higher penetrance such as SORL1 [9] and TREM2 [10]. Some genes, including SORL1 and ABCA7, are found to harbor both common and rare variants associated with AD risk [11, 12]. All these studies gradually expose the genetic architecture of AD. Still, since detecting rare variants with moderate to large effects is challenging, a large part of the genetic risk remains unexplained.

Previous research demonstrated that studying families with a high burden of disease provides the opportunity to identify novel variants [13,14,15]. In addition, the search for risk genes currently includes polygenic risk score (PRS) approaches, a calculation based on the number of risk alleles carried by an individual and the corresponding effect sizes as defined by GWAS. The PRS in AD was previously found to be most elevated in sporadic early-onset cases [16]. In family-based studies, PRS has not been applied to a large extent. Here, we reconstructed 36 small pedigrees consisting of EOAD patients. We have analyzed exome sequencing data of these families to search for rare segregating variants in known and novel genes that might cause disease or act as risk modifiers. In addition, we determined APOE genotypes and computed a PRS to investigate the possibility of polygenic etiology underlying AD in these families.

Methods

Data collection

A schematic overview of our analysis setup is presented in Fig. 1. Pedigrees were determined based on genetic data of patients ascertained from three different cohorts: (1) 4640 patients from the Amsterdam Dementia Cohort (ADC) [17] with a variety of clinical diagnoses within the dementia spectrum (including AD, mild cognitive impairment [MCI], frontotemporal dementia [FTD], and dementia with Lewy bodies [DLB]); (2) 137 clinical AD patients from Erasmus Medical Center (Erasmus MC); and (3) 53 patients with positive family history and pathologically confirmed AD selected from the Netherlands Brain Bank (NBB). Families consisting of ≥2 patients were selected, including at least one with probable AD [2] and onset <70 years of age. As opposed to the standardly used threshold of 65, we slightly released this age restriction to enable evaluation of patients and families on the border of EOAD and LOAD, hypothetically of interest regarding oligogenic or polygenic nature of disease. Family members aged ≥75 without cognitive complaints were included, if available, to enable segregation analysis of genetic variants. These relatives were confirmed cognitively healthy by MMSE. We reviewed the clinical data of all patients, including the results of previous clinical genetic testing of the patient and/or relatives.

Fig. 1
figure 1

Flowchart of the different analyses performed in this study. Patients with AD were ascertained from two clinical centers (Amsterdam Dementia Cohort and Erasmus MC) and from the Netherlands Brain Bank. Following the reconstruction of 36 pedigrees, various types of genetic data were assessed. Copy number variation in APP was tested using SNP array data or by targeted TaqMan assay. Prioritized candidate variants in novel genes were further investigated by replication in a larger cohort of AD patients. APOE genotypes and polygenic risk scores were computed for all individuals based on either (imputed) exome sequences or SNP arrays. Finally, all genetic data were reviewed to evaluate the genetic etiology of the families. Abbreviations: APOE apolipoprotein E, APP amyloid precursor protein, SNP single nucleotide polymorphism, VUS variant of uncertain significance

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Genetic data generation and processing

For the majority of patients, single nucleotide polymorphism (SNP) arrays were available as part of the European Alzheimer’s and Dementia Biobank (EADB). Details on data generation and processing have been described previously [3]. Exome sequencing data were generated for all selected patients and eight unaffected relatives. Genomic DNA was extracted from whole blood or frozen post-mortem brain tissue using standard laboratory procedures. DNA samples were paired-end sequenced using Illumina sequencers, after capturing using either Nimblegen SeqCap EZ v3 (ADC and NBB) or v2 (Erasmus MC) capture kits. Raw sequencing data from all sites were collected on a single site and processed using a uniform pipeline as reported recently [18]. In brief, sequenced reads were processed using the Burrows-Wheeler Aligner (BWA) Tool, Picard, and Samtools, and GATK was used for variant calling and quality control according to best practices [19, 20]. All samples were jointly genotyped into a single dataset VCF file (variant call format). Subsequently, a family-VCF was generated for each family. Population database frequencies and functional and impact scores were annotated to variants using ANNOVAR [21].

Determine pedigrees by genetic data

We assessed family relationships using two independent methods. First, available SNP arrays were pooled for an identical-by-descent (IBD) segment-based method to classify degrees of relatedness using PLINK 1.9 [22]. Second, using the exome sequencing data of all three cohorts, we calculated a kinship coefficient between all pairs of samples using the dataset-VCF file (VCFtools --relatedness2) with common variants only (allele frequency >5%), based on the method of Manichaikul et al. [23]. Previous studies demonstrated that the algorithm reliably infers up to third-degree relationships. Both methods were compared for possible discrepancies and were fully concordant. All identified kinships were checked and confirmed with previously clinically determined pedigrees, when available.

APOE genotyping and APP copy number analysis

APOE genotypes were determined by either (1) clinical testing, (2) sequencing data, or (3) SNP arrays. Additionally, copy number variation (CNV) of the APP gene was assessed in at least one member of the included families, either using a made-to-order TaqMan assay (Hs01547105_cn, Applied Biosystems) or by CNV analysis of SNP arrays using the PennCNV algorithm [24].

Exome sequencing variant filtering

Family-level VCFs were created per identified family, consisting of at least two affected individuals. All variants were filtered based on the following criteria: (1) QD score (quality by depth) of ≥ 5; (2) affecting coding (missense, nonsense, frameshift) or splicing regions (up to 2 bp of exon-intron junctions); (3) minor allele frequency (MAF) < 0.1% in the Genome Aggregation Database (GnomAD) [25]; and (4) heterozygous in all affected individuals and homozygous reference in all unaffected individuals, when available.

Dementia-related genes

Following the initial filtering steps, all variants in genes included in a comprehensive list of reported AD and other dementia-related genes/loci (Additional file 1: Table S1) were manually evaluated. In case of other neurodegenerative disorders reported in the family history, related genes were additionally examined. This included assessment of variant type and location, in silico prediction scores, presence in online genetic databases (Alzforum, HEX [healthy exomes], HGMD, LOVD, and ClinVar), and existing literature on the variant or a different variant in the same position. Subsequently, the variants were classified according to guidelines by ACMG (American College of Medical Genetics and Genomics) [26]. Variants predicted to be tolerable by two out of three prediction tools (SIFT, PolyPhen2, MutationTaster) and/or a CADD score <10 were classified as likely benign. Definite pathogenic variants according to genetic databases were confirmed by Sanger sequencing (Applied Biosystems, CA, USA). These families were excluded from subsequent analyses for novel genes.

Novel genes

For other genes (not dementia related), we prioritized identical variants occurring in ≥2 families OR different variants in the same gene in ≥3 families, excluding the families with confirmed monogenic cause. Different variants in the same gene shared by only two families were filtered by (1) CADD score >15 in both families and (2) a possible link to dementia/neurodegeneration based on the literature, as per consensus by two researchers. Finally, variants in regions known to give rise to false positives (e.g., low-quality regions, repeats, homologous regions) were manually reviewed using the Integrative Genomics Viewer (IGV) and excluded by expert bioinformaticians when appropriate (Additional file 1: Table S2) [27].

Replication of candidate variants

To replicate the association of our candidate variants with AD, we used exome sequencing data available from Dutch studies contributing to the Alzheimer Disease European Sequencing (ADES) consortium (ADC, Erasmus MC, NBB, Rotterdam Study, 100-plus study) and from the Amsterdam Human Genetics department (parents of trios and non-dementia cases) consisting of 833 EOAD patients, 521 LOAD patients, and 6949 healthy controls. The set of samples was pruned such that no family relations (up to third degree) remained. Only variants with a MAF < 0.1% were selected and results were categorized based on CADD score (CADD 15, 20, 25, and 30). Population structure was corrected for with 10 PCA components. Only genes with ≥10 carriers were considered for subsequent analysis. Quality control and burden tests were performed using ordinal logistic regression with an EOAD > LOAD > controls labeling as recently described [18]. This method exploits the assumption that the genetic risk will be enriched towards EOAD patients, as can be expected for the candidate variants and genes in this study. Variant-specific analyses were performed with the same approach (minimum of 5 carriers). P-values were adjusted for multiple testing using the FDR approach (Benjamini-Hochberg procedure), with FDR < 0.05 considered as suggestively associated with AD.

Polygenic risk scores

We calculated weighted polygenic risk scores (PRS) for all patients based on genetic variants (Additional file 1: Table S3) showing genome-wide significant association with AD in a recent meta-GWAS by de Rojas et al. [28]. Effect sizes of the variants were obtained from previous GWAS [29,30,31]. PRS were generated by multiplying the genotype dosage of each risk allele by its respective weight and then summing across all variants. For 56/77 patients included in this study, SNP arrays were available as part of EADB and used to directly genotype variants or impute with high quality (imputation score R2 > 0.6). For the other 21 patients, we imputed exome sequencing data using the HRC reference panel, including the samples with SNP array data to improve imputation quality. Four out of the 39 variants could not be imputed with sufficient quality (R2 < 0.3), leaving 35 variants for PRS calculation (median R2 = 0.73) based on WES data. To enable comparison of all patients, PRS based on SNP arrays were computed using the same 35 variants. Both PRS sets showed a linear correlation (R2 = 0.61). We scaled the PRS of all individuals by subtracting the mean PRS of an in-house population-based control dataset (n = 980); thus, all given effect sizes are relative to the population mean. PRS for each family was obtained by averaging the effect sizes of the affected individuals. We compared PRS between groups of families using an unpaired t-test.

APOE burden

To separate the effects of APOE from other genetic variants, we excluded the APOE SNPs from the PRS calculation. However, we estimated the risk based on APOE for all individuals using the SNP effect sizes from the same meta-GWAS (i.e., −1.20 for rs429358-T-C and 0.47 for rs7412-C-T) [28], scaled to the mean of the same population-based control dataset, to obtain risk estimates on the same scale as PRS. To compare the APOE burden across subsets of families, we performed a Mann-Whitney U test with continuity on the family-averaged risk. Correlation between APOE-risk and PRS was assessed by Spearman’s rank-sum test.

Genetic findings categorized based on clinical actionability

We evaluated the impact of the different genetic components for each family. The cumulative genetic evidence and potential relationships between variant carrier status (pathogenic variant or a VUS), APOE burden, and PRS were assessed, leading to categorization of the families as follows: (1) genetic cause identified, sufficient to be reported back to patients (i.e., passing actionable risk threshold); (2) one or more genetic risk factors identified suggesting potential polygenic etiology (i.e., passing genetic risk threshold, but insufficient for clinical action as the complete cause remains uncertain); (3) no genetic risk factors identified after evaluation of variants in dementia-related genes, APOE, and PRS, suggesting the presence of additional, yet undiscovered genetic risk factors.

Results

Clinical demographics of included families

With the pooled genetic data of 4840 patients, we constructed 36 families (at least second-degree relation) comprising at least two patients (n = 77). Seven pedigrees were previously clinically established and used to validate our two methods for determining relatedness. Both approaches were able to define first- and second-degree relationships and were fully concordant.

For each family, clinical and genetic findings are summarized in Table 1. All included families consisted of at least one patient with (probable) AD before the age of 70. The average age at onset of all affected individuals was 63 years. A lumbar puncture was performed in 32/36 probands, confirming decreased concentrations of β-amyloid in cerebrospinal fluid (CSF). Occasionally, relatives had been diagnosed with other phenotypes on the dementia spectrum (MCI, FTD). Four families included an unaffected relative aged >75. Detailed information on the individual level is provided in Additional file 1 (Table S4).

Table 1 Summary of the clinical characteristics and genetic findings of the 36 families evaluated in this study
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Pathogenic variants in dementia-related genes

In eight families, we detected six different pathogenic variants in known dementia genes (Table 2). Six families harbored known causal pathogenic variants in PSEN1 and APP, with pathological confirmation of AD in two patients. The identified frameshift deletion in SORL1 (p.P961fs), absent in GnomAD, has not yet been reported in variant databases. We classified this variant as pathogenic, as previous studies show that truncating SORL1 variants are highly penetrant [32]. This is consistent with the high burden of EOAD in this family.

Table 2 Pathogenic variants and rare VUS identified in dementia-related genes
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In one family, we identified a pathogenic variant in the gene GRN, known to cause FTD. The frameshift variant (p.Q130SfsX125) was among the first GRN variants reported in FTD families and confirmed pathogenic in later studies [33,34,35]. The family in the current study consisted of two siblings with remarkably early onset (<50 years) of typical AD, without symptoms suggestive of FTD. The diagnosis of AD was supported by CSF biomarkers in both siblings (Aβ 674/477, t-tau 649/573, p-tau 78/78). Plasma GRN levels were not available for these patients. MRI showed generalized atrophy with relative sparing of the hippocampi. Family history was positive for EOAD in first- and second-degree relatives.

APOE burden

The majority of the families included APOE-ε4 heterozygous patients (24/36 families; 67%), and at least one homozygous carrier was found in 18 families (18/36; 50%). Of these, seven families showed complete segregation of APOE-ε4 homozygosity. Only one patient carried an APOE-ε2 allele. Of the families with a pathogenic variant, only the family with the SORL1 frameshift also included an APOE-ε4 homozygous carrier.

We evaluated the APOE burden among the families using previously computed effect sizes for each genotype, scaled to the mean of our population-based control dataset (n = 980). As such, a risk of 0 indicates the population mean (OR = 1), whereas an effect size of 1, for instance, implies an increased risk with odds ratio 2.7. We observed a significantly smaller risk conveyed by APOE in the eight families with pathogenic variants as compared with the rest (0.29 vs. 1.15, P = 0.010) (Table 3). To evaluate the presence of additional genetic causes or risk factors (e.g., rare variants or polygenic risk), we kept the families with high APOE burden — and without monogenic cause — in subsequent analyses.

Table 3 Subgroup analyses of AD risk in the families imposed by APOE and polygenic risk score
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Variants of uncertain significance in dementia-related genes

In a broad panel of dementia-related genes, we identified 10 rare segregating variants. Two missense variants in CR1 and GRN were classified as likely benign based on in silico predictions, leaving eight variants of uncertain significance (VUS), including missense variants in the AD risk genes SORL1, ABCA7, and FERMT2 (Table 2). In all eight families, at least one patient was APOE-ε4 heterozygous, and six families included APOE-ε4 homozygous carriers. The difference in APOE-risk between families with and without a VUS was not statistically significant (1.44 vs. 0.82; P = 0.063).

We identified a nonsense variant in SQSTM1 (p.Y140X), a gene associated with FTD and amyotrophic lateral sclerosis (ALS) [36]. The variant is absent from all genetic databases and leads to a premature stop codon removing the last 301 amino acids, resulting in a strong deleteriousness prediction (CADD 35), although SQSTM1 seems relatively tolerant to loss of function (pLI 0.001) [25]. The two patients carrying this variant both had CSF profiles consistent with AD. Both were APOE-ε4 heterozygous. The family history indicated early death of the father, but five out of 11 of his siblings had suffered from early-onset dementia. Their clinical presentations did not include symptoms suggestive of FTD/ALS. Unfortunately, no additional relatives were currently available for further segregation analysis.

We also found a variant in the gene PRNP, which is associated with familial forms of prion disease. The clinical picture of this family did not encompass atypical features reminiscent of inherited prion disease such as Creutzfeldt-Jakob disease.

Other neurodegenerative disease genes

Besides dementia, some patients reported other neurodegenerative disorders in the family history. In two families, relatives had been diagnosed with Parkinson’s disease (PD). We screened the sequencing data of these families for PD-related genes [37], but did not detect any candidate variants. Similarly, we screened four families with FTD and/or ALS phenotypes for genetic variants in ALS-related genes [38], which did not yield any result. A repeat expansion in C9orf72 was excluded in three out of four families. For the remaining family, the CSF profile was consistent with the diagnosis of AD, lowering the possibility of FTD-C9orf72.

Variants in novel genes

A total of 28 families without definite pathogenic variant (including those with a VUS and high APOE-ε4 burden) were included for further analyses, to identify variants in novel genes possibly associated with AD risk. On average, 76 variants were remaining in each of these families after initial filtering steps (coding, segregating variants with MAF < 0.1%). Genes were prioritized based on two different approaches. First, we ascertained shared variants by ≥2 families and different variants in one gene in ≥3 families, summing up to 42 unique genes. After excluding likely false positive variants (Additional file 1: Table S2), 28 genes remained for replication analysis. In parallel, we evaluated different variants in genes shared by two families with CADD > 15, further prioritized by possible association with neurodegeneration based on literature. This resulted in the selection of nine additional genes (further details in Additional file 1: Table S5). Following the two parallel strategies, we included 37 genes (78 variants) in the replication analysis. A complete list of all variants is provided in Additional file 1 (Table S6).

Replication analysis

For our prioritized set of genes, we performed a genetic association test on both gene and variant levels, using exome sequencing data from Dutch studies contributing to the ADES consortium. None of the genes or variants tested was significantly associated with EOAD nor LOAD in this dataset as compared with controls (Tables S7-8), although 40/78 very rare variants could not be tested due to limited power. The most significant association was found for LRP1B in patients carrying variants with CADD>15 (OR = 1.13, unadjusted P = 0.08). The largest effect was found for variants with CADD>25 in the gene RBFOX1 (OR = 2.14, unadjusted P = 0.15). In the per-variant analysis, a missense variant in FNDC1 was present more often in cases compared with controls with nominal significance (OR = 3.12, unadjusted P = 0.03).

Polygenic risk score

Similar to APOE, PRS of the families were scaled to the mean of our population-based control dataset. Figure 2 depicts the increased risks based on PRS and APOE for each family, highlighting families with pathogenic variants and a VUS. An overview of subgroup comparisons with statistical output is provided in Table 3. On average, the polygenic risk of the AD families was 0.05 higher (OR = 1.05) than the population mean (SD 0.28, P = 0.206) and did not correlate with age at onset (R2 < 0.01). PRS of the eight families with a pathogenic variant did not deviate from the rest (−0.03 vs 0.07; P = 0.379). However, the six families with APP or PSEN1 variants showed significantly lower PRS (−0.22 vs. 0.10; P = 0.009), whereas those with a pathogenic variant in SORL1 and GRN had higher scores (0.47 and 0.61, respectively).

Fig. 2
figure 2

The AD risk based on APOE and polygenic risk score (PRS) in the 36 families. Each node represents one family, with PRS plotted on the x-axis and APOE-risk on the y-axis. The effect sizes of PRS and APOE were scaled to the mean of an in-house population-based control dataset (n = 980). For interpretation of the effect sizes: 0, OR = 1; 1, OR = 2.7; and 2, OR = 7.4. The horizontal lines for APOE-risk indicate families with risk >1 (i.e., at least one APOE-ε44 carrier) and risk >2 (i.e., fully segregating with APOE-ε44). Dark colored nodes depict families with pathogenic variant (n = 8), whereas light colored nodes represent families with a variant of uncertain significance (n = 8). Families with a pathogenic variant are clustered in the lower left corner, with a minor impact of APOE and PRS. Other families in the same region, yet without monogenic defect, would be suitable candidates for further genetic evaluation. Families with a relatively high APOE burden are located in the upper part of the plot, which also includes most families with a VUS. The overall contribution of PRS seems modest (average beta = 0.05, OR = 1.05), with the highest risk observed in the family with a GRN variant (beta = 0.61, OR = 1.84)

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When selecting 11 families without monogenic cause and low APOE-risk (<1), PRS did not differ from the other 25 families (−0.001 vs. 0.07, P = 0.515), nor was a significant difference observed when comparing families with and without a VUS (0.18 vs. 0.01; P = 0.129). When excluding the families with pathogenic variant, a positive correlation was observed between PRS and APOE-risk (Rs = 0.48, P = 0.003) in the remaining 28 families.

Families categorized according to genetic findings

Following the assessment of dementia-related genes, APOE genotypes, and PRS, we classified the families into three distinct categories as schematically visualized in Fig. 3. The first represents eight families with a monogenic cause identified, meriting disclosure to patients and relatives. The second group comprises 19 families in whom we found one or more genetic risk factors, suggesting polygenic or multifactorial etiology. As these factors do not sufficiently explain the disease occurrence, clinical reporting or further action is currently not indicated in these families. The last group contains nine families without any identified genetic cause or risk factor, despite the early onset of AD and the positive family history, in which additional genetic factors may be uncovered.

Fig. 3
figure 3

Schematic summary of the genetic etiology of the families, categorized into three groups. We classified the families into three distinct groups based on the clinical actionability of the genetic findings, as illustrated systematically in the upper panel and for the 36 families in the lower panel. The first group represents families with a pathogenic variant identified as the cause of disease, passing a clinically actionable threshold where results could be returned to the patients and relatives. The second category includes families with (possible) genetic risk factors (APOE, VUS, and PRS). These are not clinically actionable (i.e., these families are clearly burdened by genetic risk factors, but the complete cause is uncertain). Possibly, additional components of the genetic risk remain unidentified. The last group comprises families without any genetic risk factor currently identified, despite early onset and positive family history. The lower panel shows that we can genetically explain eight families, suspect a partially identified polygenic/multifactorial etiology in 19 families, whereas nine remain completely unresolved

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Discussion

In this genetic study of AD, we analyzed exome sequencing data of 36 small families with a high burden of early-onset disease. In addition to known monogenic causes, we evaluated rare novel variants as well as the increased genetic risk based on APOE and polygenic risk profiling.

The genetic landscape of EOAD includes FTD-related genes

Besides pathogenic variants in APP, PSEN1, and SORL1, we discovered a deleterious GRN frameshift variant (p.Q130SfsX125) in one family. The variant is known to cause FTD [33, 39] and has not been associated with clinical AD, although one family was described with profound AD neuropathology (reported as UBC11, Braak stage VI) [40]. Although FTD-GRN patients may show coexistent AD pathology [41], certain GRN variants have been suggested as the direct cause of AD [42] through several proposed mechanisms (i.e., Aβ clearance, tau phosphorylation, neuroinflammation), indicating shared pathways between AD and progranulin [43]. As such, we propose that the identified variant — possibly in concert with an AD polygenic risk profile (OR = 1.84) — plays a causal role in this family and should be reported in genetic counseling. It would be of interest to evaluate PRS in a larger cohort of GRN carriers presenting with an AD phenotype. In a clinical setting, we recommend to examine an extended panel of dementia genes in familial EOAD, when APP, PSEN1, and PSEN2 are tested negative, as was suggested before [44]. Following a family history of PD and ALS, we also screened genes related to these disorders in a few families. As this did not yield any candidate variants, it remains unclear whether such genes should be standardly examined in AD.

The second non-traditional AD finding is a rare stop-gained variant in SQSTM1. This gene encodes the scaffold protein p62, involved in diverse signaling pathways associated with neurodegeneration such as autophagy, inflammation, and tau degradation [45]. Several studies have shown that reduced SQSTM1 gene expression and lack of cytoplasmic p62 provoke the aggregation of pathological tau and neurofibrillary tangles [46, 47], implying an important role of p62 in the pathogenesis of AD. Although no risk or causal SQSTM1 variants have been identified in AD patients so far [48], the wide clinical spectrum of SQSTM1 variants is exemplified by case reports of familial hippocampal amnestic syndrome closely resembling AD [49] and atypical FTD with prominent memory decline [50]. Replication of SQSTM1 variants in larger AD cohorts is needed before it can be included in genetic risk prediction.

Regarding the gene PRNP, several variants have been suggested to play a role in other neurodegenerative disorders besides prion disease [51]. For instance, variants in the same N-terminal domain as the here identified variant were previously detected in FTD and AD patients [52, 53]. As such, we cannot exclude a causal role, although currently its classification remains a VUS.

Evaluating the risk imposed by APOE and rare VUS

We identified a substantial burden of APOE, with heterozygous ε4-allele carriers in 67% of the families and homozygosity in 50%. Although the risk conferred by two APOE-ε4 alleles can cause semi-dominant inheritance with AD lifetime risk estimates of 30% by 75 years of age [54], we cannot be sure if APOE might be the sole genetic factor in these families, or whether some carry additional factors — genetic and/or environmental — to trigger disease initiation.

Following the relatively low burden of APOE and PRS in families with a known pathogenic variant, we anticipated a similar low burden in families with a VUS, if these are pathogenic. However, six out of eight families with a VUS showed a high occurrence of APOE-ε4 homozygosity. Moreover, we did not observe a difference in PRS across families with and without a VUS. It suggests that the identified VUS are insufficient by themselves to cause disease and that the genetic burden for AD in these families is multifactorial, although individual families might have a high burden of APOE or PRS by chance, on top of a monogenic cause.

A possible additive risk of VUS and APOE is supported by their observed frequent co-occurrence. In addition, interactions between genetic factors influencing the same biological pathways might point in the direction of a cumulative effect, for example between APOE and the identified VUS in SORL1 and ABCA7, which are both involved in lipid metabolic pathways [12, 32]. Previous work has indeed shown that the combination of pathogenic variants in SORL1 with APOE-ε4 increases penetrance [55]. Additional risk genes in which we detected a VUS are also associated with lipid metabolism (e.g., FERMT2, BIN1, CR1) [56, 57], reinforcing the possibility that convergence of these genetic factors with APOE on this particular pathway bestows an increased AD risk.

Modest contribution of PRS in familial EOAD

The overall impact of PRS in the current families was modest, similar to earlier findings in autosomal dominant AD [16]. It should be pointed out that the PRS was constructed based on GWAS data of mostly LOAD patients, but was replicated in EOAD with similar effect estimates [28]. Although an oversimplification of polygenic inheritance, we calculated a family-averaged PRS to represent the shared polygenic burden. This allowed us to compare the contribution of PRS across families, also in conjunction with other genetic factors.

The four families with the highest PRS showed, on average, a risk score of 0.56 (OR = 1.75) as compared with the general population, which is not evidently higher than previously reported effects in LOAD [16, 28], and appears small when set against the impact of APOE with maximum risk of 2.04 (OR = 7.34) (Fig. 2). Families with causal variants in APP and PSEN1 showed especially low PRS — similar to the low burden of APOE — consistent with the perception that these variants are on their own sufficient to cause AD. This raises the notion whether families with similarly low PRS and APOE burden (i.e., nine families as shown in Figs. 2 and 3) may harbor monogenic causes (pathogenic variants) that currently remain undetected. As our analyses investigating rare variants shared by multiple families did not yield potential candidates, we would prioritize these families for genetic evaluation using other methods, as well as attempting to expand specifically these pedigrees to optimize segregation analysis. Investigating multiple affected relatives using whole genome sequencing could aid in the identification of novel genetic defects, including intronic and structural variation.

Similar to APOE and rare VUS, a cumulative effect might apply to the risk conveyed by APOE and PRS, as we observed a positive correlation between the two. Future work is needed to clarify if a combined signal might be based on biological interactions, endorsed by a recent study demonstrating a disproportionally large effect of PRS (without APOE) among APOE-ε4 carriers with earlier disease onset [58].

Genetic testing of APOE and PRS in clinical practice

APOE genotyping is not incorporated in current risk predictions, because it is neither diagnostic nor sufficiently predictive, and at present has no clinical consequences [59]. These guidelines will likely not change until effective treatments become available. Nevertheless, given the fact that half of the families studied here were affected by APOE-ε4 homozygosity, in some instances accompanied by a potentially relevant VUS, a pertinent question is at which point the risk is sufficiently high to include these factors in genetic risk modeling and counseling. Importantly, APOE genotyping is currently used in clinical trials and is available via direct-to-consumer genetic testing [60]. Previous studies suggest that genetic risk disclosure of APOE can be safely performed in a clinical setting, provided that appropriate and standardized counseling protocols are applied [61, 62]. On the other hand, the risk of misunderstanding or overinterpretation is an important issue, which must be carefully weighed against the possible personal utility of disclosure for the individual patient [63]. At any rate, we urge APOE genotyping in a research setting to further clarify its impact and interplay with other (genetic) factors.

Although it might exert a contributory effect in some families, at present, we do not recommend PRS as a diagnostic tool in familial EOAD as studied here, following its relatively small impact in all families. Although a few families showed higher risk, this currently seems insufficient to justify clinical action — similar to APOE — until disease-modifying therapies or cost-effective preventive measures become available. As a result, although the PRS will likely improve when more variants are added to it, we foresee it will not change this view on its clinical utility in familial EOAD in the near future.

Currently, PRS seems most applicable to estimate risk in late-onset forms of AD [28, 64] and has proven useful to stratify patients for clinical trials to reduce variability [65, 66], and for genes that show reduced penetrance (e.g., PSEN2), PRS might influence clinical expression, as has been shown for other diseases such as hereditary breast cancer [67]. Additional work is required to support these potential applications of PRS in a clinical setting.

Future directions

By evaluating various genetic factors in parallel, we investigated components of multifactorial genetic risk associated with AD. To validate whether individual genetic factors (pathogenic variants, VUS, APOE, PRS) interact and are thus multiplicative or additive must be determined by population studies with sufficient carriers to populate such models. For instance, the variability in disease penetrance and expression of APOE might be explained by interactions with other genes involved in the same pathways, as supported by prior studies investigating biological interactions and correlations with clinical measures [68,69,70,71]. Large-scale genetic studies could thereby also investigate how the combined genetic signals translate into expected penetrance at a certain age at onset. Our findings in non-traditional AD genes, of which the clinical impact might be modulated by other factors such as PRS, need to be replicated in extended cohorts. We anticipate that this will contribute to the development of more inclusive risk algorithms. Finally, we propose that the commonly used threshold of 65 years for EOAD could be slightly released when evaluating oligogenic/polygenic inheritance, although the most appropriate age remains a topic of investigation.

Limitations

There are some limitations to this study. First, we prioritized variants shared by two families by employing several strategies (i.e., allele frequency, CADD score, literature search), possibly discarding variants that could be relevant. Second, we tested potential candidates in a sizeable cohort, but the power was limited for the assessment of very rare variants and this dataset was not specifically enriched for cases with a positive family history. Replication of these variants is required in additional case-control samples. Finally, for a subset of patients, we computed PRS from imputed sequencing data with lower imputation quality than SNP arrays. This could have impacted results, despite the high correlation (R2 = 0.6) between the two datasets.

Conclusion

Our comprehensive study of familial AD, with each family having at least one patient with age at onset <70, demonstrates its broad genetic framework. Besides monogenic causes, we suspect a polygenic/multifactorial etiology in around half of the families based on APOE and rare variants in dementia-related genes. Although PRS may contribute to disease risk, the extent of its effect is small and warrants further investigation before it can be incorporated in clinical risk prediction. Importantly, we showed that assessing APOE burden and PRS can help distinguish those patients with a higher a priori probability for a monogenic cause, thus, rendering them suitable candidates for further in-depth genetic evaluation.

Availability of data and materials

The genetic variants analyzed in this study and summary statistics are included in the Supplementary data files. The full datasets generated and/or analyzed during the current study are not publicly available due to privacy restrictions applicable to genetic data from human subjects.

References

  1. Alzheimer's A. 2019 Alzheimer’s disease facts and figures. Alzheimers Dement. 2019;15(3):321–87.

    Article  Google Scholar 

  2. Gm M, Ds K, Chertkow H, Bt H, Cr JJ, Kawas C, et al. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute On Aging-Alzheimer’s Association Workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011;7(3):263–9.

    Article  Google Scholar 

  3. Bellenguez C, Küçükali F, Jansen IE, et al. New insights into the genetic etiology of Alzheimer's disease and related dementias. Nat Genet. 2022;54(4):412-36. https://0-doi-org.brum.beds.ac.uk/10.1038/s41588-022-01024-z.

  4. Sims R, Hill M, Williams J. The multiplex model of the genetics of Alzheimer’s disease. Nat Neurosci. 2020;23(3):311–22.

    Article  CAS  PubMed  Google Scholar 

  5. Gatz M, Ca R, Fratiglioni L, Johansson B, Ja M, Berg S, et al. Role of genes and environments for explaining Alzheimer disease. Arch Gen Psychiatry. 2006;63(2):168–74.

    Article  PubMed  Google Scholar 

  6. Corder E, Am S, Wj S, De S, Pc G, Small G, et al. Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer’s disease in late onset families. Science. 1993;261(5123):921–3.

    Article  CAS  PubMed  Google Scholar 

  7. Cuyvers E, Sleegers K. Genetic variations underlying Alzheimer’s disease: evidence from genome-wide association studies and beyond. Lancet Neurol. 2016;15(8):857–68.

    Article  CAS  PubMed  Google Scholar 

  8. Lambert JC, Ibrahim-Verbaas CA, Harold D, Naj AC, Sims R, Bellenguez C, et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat Genet. 2013;45(12):1452–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Pottier C, Hannequin D, Coutant S, Rovelet-Lecrux A, Wallon D, Rousseau S, et al. High frequency of potentially pathogenic SORL1 mutations in autosomal dominant early-onset Alzheimer disease. Mol Psychiatry. 2012;17(9):875–9.

    Article  CAS  PubMed  Google Scholar 

  10. Korvatska O, Jb L, Jayadev S, Mcmillan P, Kurtz I, Guo X, et al. R47h variant of Trem2 associated with Alzheimer disease in a large late-onset family: clinical, genetic, and neuropathological study. JAMA Neurol. 2015;72(8):920–7.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Bellenguez C, Charbonnier C, Grenier-Boley B, Quenez O, Le Guennec K, Nicolas G, et al. Contribution to Alzheimer’s disease risk of rare variants in Trem2, Sorl1, And Abca7 in 1779 cases and 1273 controls. Neurobiol Aging. 2017;59(220):E1–9.

    Google Scholar 

  12. De Roeck A, Van Broeckhoven C, Sleegers K. The role of Abca7 in Alzheimer’s disease: evidence from genomics, transcriptomics and methylomics. Acta Neuropathol. 2019;138(2):201–20.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Cruchaga C, Cm K, Sc J, Ba B, Cai Y, Guerreiro R, et al. Rare coding variants in the phospholipase D3 gene confer risk for Alzheimer’s disease. Nature. 2014;505(7484):550–4.

    Article  CAS  PubMed  Google Scholar 

  14. Kohli MA, Cukier HN, Hamilton-Nelson KL, Rolati S, Kunkle BW, Whitehead PL, et al. Segregation of a rare TTC3 variant in an extended family with late-onset Alzheimer disease. Neurol Genet. 2016;2(1):E41.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Cukier HN, Kunkle BK, Hamilton KL, et al. Exome Sequencing of Extended Families with Alzheimer's Disease Identifies Novel Genes Implicated in Cell Immunity and Neuronal Function. J Alzheimers Dis Parkinsonism. 2017;7(4):355.https://0-doi-org.brum.beds.ac.uk/10.4172/2161-0460.1000355.

  16. Cruchaga C, Del-Aguila JL, Saef B, Black K, Mv F, Budde J, et al. Polygenic risk score of sporadic late-onset Alzheimer’s disease reveals a shared architecture with the familial and early-onset forms. Alzheimers Dement. 2018;14(2):205–14.

    Article  PubMed  Google Scholar 

  17. van der Flier WM, Pijnenburg YA, Prins N, Lemstra AW, Bouwman FH, Teunissen CE, et al. Optimizing patient care and research: the Amsterdam dementia cohort. J Alzheimers Dis. 2014;41(1):313–27.

    Article  PubMed  Google Scholar 

  18. Holstege H, Hulsman M, Charbonnier C, Grenier-Boley B, Quenez O, Grozeva D, et al. Exome sequencing identifies rare damaging variants in ATP8B4 and ABCA1 as novel risk factors for Alzheimer’s Disease. medRxiv. 2022:2020.07.22.20159251. https://0-doi-org.brum.beds.ac.uk/10.1101/2020.07.22.20159251.

  19. Li H, Durbin R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics. 2009;25(14):1754–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Mckenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20(9):1297–303.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Wang K, Li M, Hakonarson H. Annovar: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010;38(16):E164-E.

    Article  CAS  Google Scholar 

  22. Purcell S, Neale B, Todd-Brown K, Thomas L, Ma F, Bender D, et al. Plink: A tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81(3):559–75.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Manichaikul A, Mychaleckyj JC, Rich SS, Daly K, Sale M, Wm C. Robust relationship inference in genome-wide association studies. Bioinformatics. 2010;26(22):2867–73.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Wang K, Li M, Hadley D, Liu R, Glessner J, Sf G, et al. Penncnv: an integrated hidden Markov model designed for high-resolution copy number variation detection in whole-genome SNP genotyping data. Genome Res. 2007;17(11):1665–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alföldi J, Wang Q, et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature. 2020;581(7809):434–43.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Nykamp K, Anderson M, Powers M, Garcia J, Herrera B, Yy H, et al. Sherloc: a comprehensive refinement of the ACMG-AMP variant classification criteria. Genet Med. 2017;19(10):1105–17.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Robinson JT, Thorvaldsdóttir H, Wenger AM, Zehir A, Mesirov JP. Variant review with the Integrative Genomics Viewer. Cancer Res. 2017;77(21):E31–E4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. De Rojas I, Moreno-Grau S, Tesi N, Grenier-Boley B, Andrade V, Jansen IE, et al. Common variants in Alzheimer’s disease and risk stratification by polygenic risk scores. Nat Commun. 2021;12(1):3417.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Kunkle BW, Grenier-Boley B, Sims R, Bis JC, Damotte V, Naj AC, et al. Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates aβ, tau, immunity and lipid processing. Nat Genet. 2019;51(3):414–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Sims R, van der Lee SJ, Naj AC, Bellenguez C, Badarinarayan N, Jakobsdottir J, et al. Rare coding variants in Plcg2, Abi3, And Trem2 implicate microglial-mediated innate immunity in Alzheimer’s disease. Nat Genet. 2017;49(9):1373–84.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Jun G, Ibrahim-Verbaas CA, Vronskaya M, Lambert JC, Chung J, Naj AC, et al. A novel Alzheimer disease locus located near the gene encoding tau protein. Mol Psychiatry. 2016;21(1):108–17.

    Article  CAS  PubMed  Google Scholar 

  32. Holstege H, van der Lee SJ, Hulsman M, Wong TH, van Rooij JG, Weiss M, et al. Characterization of pathogenic SORL1 genetic variants for association with Alzheimer’s disease: a clinical interpretation strategy. Eur J Hum Genet. 2017;25(8):973–81.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Baker M, Mackenzie IR, Pickering-Brown SM, Gass J, Rademakers R, Lindholm C, et al. Mutations in progranulin cause tau-negative frontotemporal dementia linked to chromosome 17. Nature. 2006;442(7105):916–9.

    Article  CAS  PubMed  Google Scholar 

  34. Gass J, Cannon A, Ir M, Boeve B, Baker M, Adamson J, et al. Mutations in progranulin are a major cause of ubiquitin-positive frontotemporal lobar degeneration. Hum Mol Genet. 2006;15(20):2988–3001.

    Article  CAS  PubMed  Google Scholar 

  35. Skoglund L, Matsui T, Freeman S, Wallin A, Es B, Mp F, et al. Novel progranulin mutation detected in 2 patients with FTLD. Alzheimer Dis Assoc Disord. 2011;25(2):173–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Rubino E, Rainero I, Chiò A, Rogaeva E, Galimberti D, Fenoglio P, et al. Sqstm1 mutations in frontotemporal lobar degeneration and amyotrophic lateral sclerosis. Neurology. 2012;79(15):1556–62.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Blauwendraat C, Nalls MA, Singleton AB. The genetic architecture of parkinson’s disease. Lancet Neurol. 2020;19(2):170–8.

    Article  CAS  PubMed  Google Scholar 

  38. Nguyen HP, Van Broeckhoven C, van der Zee J. ALS genes in the genomic era and their implications for FTD. Trends Genet. 2018;34(6):404–23.

    Article  CAS  PubMed  Google Scholar 

  39. Snowden JS, Pickering-Brown SM, Mackenzie IR, Richardson AM, Varma A, Neary D, et al. Progranulin gene mutations associated with frontotemporal dementia and progressive non-fluent aphasia. Brain. 2006;129(Pt 11):3091–102.

    Article  CAS  PubMed  Google Scholar 

  40. Mackenzie IR, Baker M, Pickering-Brown S, Hsiung GY, Lindholm C, Dwosh E, et al. The neuropathology of frontotemporal lobar degeneration caused by mutations in the progranulin gene. Brain. 2006;129(Pt 11):3081–90.

    Article  PubMed  Google Scholar 

  41. Gefen T, Ahmadian SS, Mao Q, Kim G, Seckin M, Bonakdarpour B, et al. Combined pathologies in Ftld-Tdp types A and C. J Neuropathol Exp Neurol. 2018;77(5):405–12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Redaelli V, Rossi G, Maderna E, Gg K, Piccoli E, Caroppo P, et al. Alzheimer neuropathology without frontotemporal lobar degeneration hallmarks (TAR DNA-binding protein 43 inclusions) in missense progranulin mutation Cys139arg. Brain Pathol. 2018;28(1):72–6.

    Article  CAS  PubMed  Google Scholar 

  43. Jing H, Tan MS, Yu JT, Tan L. The role of Pgrn in Alzheimer’s disease. Mol Neurobiol. 2016;53(6):4189–96.

    Article  CAS  PubMed  Google Scholar 

  44. Goldman JS, Van Deerlin VM. Alzheimer’s disease and frontotemporal dementia: the current state of genetics and genetic testing since the advent of next-generation sequencing. Mol Diagn Ther. 2018;22(5):505–13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Salminen A, Kaarniranta K, Haapasalo A, Hiltunen M, Soininen H, Alafuzoff I. Emerging role Of P62/sequestosome-1 in the pathogenesis of Alzheimer’s disease. Prog Neurobiol. 2012;96(1):87–95.

    Article  CAS  PubMed  Google Scholar 

  46. Ramesh Babu J, Lamar Seibenhener M, Peng J, Strom AL, Kemppainen R, Cox N, et al. Genetic inactivation of P62 leads to accumulation of hyperphosphorylated tau and neurodegeneration. J Neurochem. 2008;106(1):107–20.

    Article  CAS  PubMed  Google Scholar 

  47. Du Y, Wooten MC, Gearing M, Wooten MW. Age-associated oxidative damage to the P62 promoter: implications for Alzheimer disease. Free Radic Biol Med. 2009;46(4):492–501.

    Article  CAS  PubMed  Google Scholar 

  48. Cuyvers E, Van Der Zee J, Bettens K, Engelborghs S, Vandenbulcke M, Robberecht C, et al. Genetic variability in Sqstm1 and risk of early-onset Alzheimer dementia: a European Early-Onset Dementia Consortium Study. Neurobiol Aging. 2015;36(5):2005 E15–22.

    Article  CAS  Google Scholar 

  49. Carandini T, Sacchi L, Ghezzi L, Pietroboni AM, Fenoglio C, Arighi A, et al. Detection of the Sqstm1 mutation in a patient with early-onset hippocampal amnestic syndrome. J Alzheimers Dis. 2021;79(2):477–81.

    Article  PubMed  Google Scholar 

  50. Sun L, Rong Z, Li W, Zheng H, Xiao S, Li X. Identification of a novel hemizygous Sqstm1 nonsense mutation in atypical behavioral variant frontotemporal dementia. Front Aging Neurosci. 2018;10:26.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Bagyinszky E, Giau VV, Youn YC, An SSA, Kim S. Characterization of mutations in Prnp (prion) gene and their possible roles in neurodegenerative diseases. Neuropsychiatr Dis Treat. 2018;14:2067–85.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Oldoni E, Fumagalli GG, Serpente M, Fenoglio C, Scarioni M, Arighi A, et al. Prnp P39l variant is a rare cause of frontotemporal dementia in Italian population. J Alzheimers Dis. 2016;50(2):353–7.

    Article  CAS  PubMed  Google Scholar 

  53. Zhang W, Jiao B, Xiao T, Pan C, Liu X, Zhou L, et al. Mutational analysis of Prnp in Alzheimer’s disease and frontotemporal dementia in China. Sci Rep. 2016;6:38435.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Genin E, Hannequin D, Wallon D, Sleegers K, Hiltunen M, Combarros O, et al. Apoe and Alzheimer disease: a major gene with semi-dominant inheritance. Mol Psychiatry. 2011;16(9):903–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Louwersheimer E, Cohn-Hokke PE, Pijnenburg YAL, Weiss MM, Sistermans EA, Rozemuller AJ, et al. Rare genetic variant in Sorl1 may increase penetrance of Alzheimer’s disease in a family with several generations of Apoe-Ɛ4 homozygosity. J Alzheimers Dis. 2017;56(1):63–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Jones L, Harold D, Williams J. Genetic evidence for the involvement of lipid metabolism in Alzheimer’s disease. Biochim Biophys Acta. 2010;1801(8):754–61.

    Article  CAS  PubMed  Google Scholar 

  57. Liu Y, Thalamuthu A, Mather KA, Crawford J, Ulanova M, Wong MWK, et al. Plasma lipidome is dysregulated in Alzheimer’s disease and is associated with disease risk genes. Transl. Psychiatry. 2021;11(1):344.

    Google Scholar 

  58. Fulton-Howard B, Goate AM, Adelson RP, Koppel J, Gordon ML, Barzilai N, et al. Greater effect of polygenic risk score for Alzheimer’s disease among younger cases who are apolipoprotein E-Ε4 carriers. Neurobiology Of. Aging. 2021;99:101.E1–9.

    CAS  Google Scholar 

  59. Goldman JS, Hahn SE, Catania JW, Larusse-Eckert S, Butson MB, Rumbaugh M, et al. Genetic counseling and testing for Alzheimer disease: joint practice guidelines of the American College Of Medical Genetics And The National Society Of Genetic Counselors. Genetics In Medicine : Official Journal Of The American College Of Medical. Genetics. 2011;13(6):597–605.

    Google Scholar 

  60. Food and Drug Administration. Evaluation of automatic class III designation for the 23 and Me Personal Genome Service (PGS) genetic health risk test for hereditary thrombophilia, alpha‐1 antitrypsin deficiency, Alzheimer's disease, Parkinson's disease, Gaucher disease type 1, factor XI deficiency, celiac disease, G6PD deficiency, hereditary hemochromatosis and early‐onset primary dystonia. Decision summary. 2017. https://www.accessdata.fda.gov/cdrh_docs/reviews/den160026.pdf.

  61. Green RC, Roberts JS, Cupples LA, Relkin NR, Whitehouse PJ, Brown T, et al. Disclosure of Apoe genotype for risk of Alzheimer’s disease. N Engl J Med. 2009;361(3):245–54.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Christensen KD, Karlawish J, Roberts JS, Uhlmann WR, Harkins K, Wood EM, et al. Disclosing genetic risk for Alzheimer’s dementia to individuals with mild cognitive impairment. Alzheimers Dementia (New York, NY). 2020;6(1):E12002-E.

    Google Scholar 

  63. Bunnik EM, Richard E, Milne R, Schermer MHN. On the personal utility of Alzheimer’s disease-related biomarker testing in the research context. J Med Ethics. 2018;44(12):830–4.

    Article  PubMed  Google Scholar 

  64. van der Lee SJ, Wolters FJ, Ikram MK, Hofman A, Ikram MA, Amin N, et al. The effect of Apoe and other common genetic variants on the onset of Alzheimer’s disease and dementia: a community-based cohort study. Lancet Neurol. 2018;17(5):434–44.

    Article  PubMed  Google Scholar 

  65. Dickson SP, Hendrix SB, Brown BL, Ridge PG, Nicodemus-Johnson J, Hardy ML, et al. Genorisk: a polygenic risk score for Alzheimer’s disease. Alzheimers Dement (N Y). 2021;7(1):E12211.

    Google Scholar 

  66. Lambert SA, Abraham G, Inouye M. Towards clinical utility of polygenic risk scores. Hum Mol Genet. 2019;28(R2):R133–R42.

    Article  CAS  PubMed  Google Scholar 

  67. Fahed AC, Wang M, Homburger JR, Patel AP, Bick AG, Neben CL, et al. Polygenic background modifies penetrance of monogenic variants for tier 1 genomic conditions. Nat Commun. 2020;11(1):3635.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Taira K, Bujo H, Hirayama S, Yamazaki H, Kanaki T, Takahashi K, et al. Lr11, A mosaic Ldl receptor family member, mediates the uptake of Apoe-rich lipoproteins in vitro. Arterioscler Thromb Vasc Biol. 2001;21(9):1501–6.

    Article  CAS  PubMed  Google Scholar 

  69. Yano K, Hirayama S, Misawa N, Furuta A, Ueno T, Motoi Y, et al. Soluble Lr11 competes with amyloid Β in binding to cerebrospinal fluid-high-density lipoprotein. Clin Chim Acta. 2019;489:29–34.

    Article  CAS  PubMed  Google Scholar 

  70. Chang YT, Hsu SW, Huang SH, Huang CW, Chang WN, Lien CY, et al. Abca7 polymorphisms correlate with memory impairment and default mode network in patients with Apoeε4-associated Alzheimer’s disease. Alzheimers Res Ther. 2019;11(1):103.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Engelman CD, Koscik RL, Jonaitis EM, Okonkwo OC, Hermann BP, La Rue A, et al. Interaction between two cholesterol metabolism genes influences memory: findings from the Wisconsin Registry For Alzheimer’s Prevention. J Alzheimers Dis. 2013;36(4):749–57.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We wish to express our gratitude to all patients who participated in this study and to all NBB donors that provided the material to perform this research. Several authors of this publication are members of the European Reference Network for Rare Neurological Diseases - Project ID No 739510.

Funding

This research was funded by Alzheimer Nederland (WE.09-2018-07). SvdL received funding from ZonMW (#733050512). SvdL and HH are recipients of ABOARD, which is a public-private partnership receiving funding from ZonMW (#73305095007) and Health~Holland, Topsector Life Sciences & Health (PPP-allowance; #LSHM20106). More than 30 partners participate in ABOARD (www.aboard-project.nl). ABOARD also receives funding from de Hersenstichting, Edwin Bouw Fonds, and Gieskes-Strijbisfonds. MH and HH received compute hours from the Dutch Research Council on the Cartesius supercomputer (2016, 2017, 2018, 2019), which is embedded in the Dutch national e-infrastructure with the support of SURF Cooperative (project name: ‘100plus’; project numbers 15318 and 17232).

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Authors and Affiliations

  1. Alzheimer Center Erasmus MC, Department of Neurology, Erasmus University Medical Center, Rotterdam, The Netherlands

    Merel O. Mol, Harro Seelaar, John C. van Swieten, Laura Donker Kaat & Jeroen G. J. van Rooij

  2. Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands

    Sven J. van der Lee, Marc Hulsman, Yolande A. L. Pijnenburg, Phillip Scheltens & Henne Holstege

  3. Genomics of Neurodegenerative Diseases and Aging, Human Genetics, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands

    Sven J. van der Lee, Marc Hulsman & Henne Holstege

  4. Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands

    Sven J. van der Lee, Marc Hulsman, Yolande A. L. Pijnenburg, Phillip Scheltens & Henne Holstege

  5. Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands

    Marc Hulsman & Henne Holstege

  6. Department of Clinical Genetics, Erasmus Medical Center, Rotterdam, The Netherlands

    Laura Donker Kaat

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Contributions

MM: conceptualization, methodology, formal analyses, and writing original draft; SvdL/MH/HH: conceptualization, resources genetic data, methodology, formal analyses, and manuscript review and editing; YP/PS/HS/LDK: resources clinical data and manuscript review and editing; NBB: resources neuropathological data and manuscript review and editing; JvR/JvS: conceptualization, methodology, manuscript review and editing, supervision, and funding acquisition. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Merel O. Mol.

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Ethics approval and consent to participate

The study was approved by the Medical Ethical Committee of the Erasmus Medical Center Rotterdam. Brain autopsy was conducted by the Netherlands Brain Bank (NBB) at the designated premises of the VU Medical Center in Amsterdam (the Netherlands). Ethical approval for the NBB procedures and forms was given by the Medical Ethics Committee of the VU Medical Center. Written informed consent for the use of tissues, clinical, and neuropathological data was obtained from all participants or their legal representatives, according to the Code of conduct for Brain Banking and Declaration of Helsinki.

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Supplementary Information

Additional file 1: Table S1

. List of dementia related genes and loci that were analyzed in the exome sequencing data for each family separately. Table S2. List of variants and genes that were excluded following manual evaluation using the Integrative Genomics Viewer (IGV). Table S3. List of variants (n = 35) included to compute the Polygenic Risk Score (PRS). Table S4. Clinical and genetic findings of all affected individuals (n = 77) and unaffected relatives (n = 4). Table S5. Selection of genes shared by two families with possible connection to dementia based on literature search. Table S6. Total list of candidate genes (n = 37) and variants (n = 78) occurring in ≥2 families selected for replication analysis. Table S7. Replication analysis of candidate genes. Table S8. Replication analysis of candidate variants.

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Mol, M.O., van der Lee, S.J., Hulsman, M. et al. Mapping the genetic landscape of early-onset Alzheimer’s disease in a cohort of 36 families. Alz Res Therapy 14, 77 (2022). https://0-doi-org.brum.beds.ac.uk/10.1186/s13195-022-01018-3

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Alzheimer's Research & Therapy

ISSN: 1758-9193