Investigating the genetics of sporadic early-onset Alzheimer’s disease using a customised genotyping chip

Alzheimer’s disease (AD) is the commonest form of dementia and is characterised with neuropathological hallmarks such as aggregated amyloid plaques and hyper-phosphorylated tau protein. One type of AD is autosomal dominant AD (ADAD) which is caused by highly penetrant variants in one of three genes (APP, PSEN1 and PSEN2), other cases of AD are described as sporadic and can have a late onset of disease symptoms (> 65 years of age) or early onset (≤ 65 years or age). Late-onset Alzheimer’s disease (LOAD) is estimated to be 70% heritable and is common. Conversely sporadic early-onset Alzheimer’s disease (sEOAD) is estimated to 90% heritable but is relatively rare. The difference in prevalence between the two types of AD has resulted in genome wide association studies focusing on LOAD with sEOAD receiving little attention. Here we use an Illumina human exome genotyping chip customised with neurodegenerative markers (NeuroX) to genotype the coding region of sEOAD samples in a hope to elucidate the genetic aetiology of sEOAD. Sanger sequencing exons 16 and 17 of APP was conducted in a sEOAD cohort (n=451) to screen for variants known to cause ADAD; 9% (n=4) of the cohort were heterozygous for known causative variants and where subsequently removed from the sEOAD NeuroX genotyping data before analyses. Screening also highlighted an intronic 6bp deletion downstream of exon 17 in APP with a non-significant increased minor allele frequency (MAF) in sEOAD, however it did not appear to influence splicing of exon 17. Screening the sEOAD cohort for other variants known to cause neurodegenerative disease was conducted using the NeuroX genotyping data (n=408) which identified two samples with variants in PARK2, these variants are thought to contribute susceptibility to Parkinson’s disease (PD) suggesting these variants might elicit risk for multiple diseases. A further study with increased power would ascertain if the 6bp deletion and PARK2 variants are associated with sEOAD. Statistical analyses of the sEOAD NeuroX genotypes highlighted many variants, genes and pathways that could be contributing to susceptibility to disease; however no tests reached significance after adjusting for multiple testing. The genes most associated (PDZK1, DCLK3, SLC33A1 and BLOC1S2) appear to be biologically relevant and would be ideal candidates for further study. Additionally, just under half of the variants that are significant associated with LOAD were genotyped on the NeuroX and two of these were significantly associated with sEOAD after correcting for multiple testing (rs3851179 and rs3764650). The genotypes of all the variants highlighted would need to be verified before their functionalities were investigated further

Investigating splicing variants uncovered by next-generation sequencing the Alzheimer’s disease candidate genes, CLU, PICALM, CR1, ABCA7, BIN1, the MS4A locus, CD2AP, EPHA1 and CD33

Late onset Alzheimer’s disease (LOAD), the most common cause of late onset dementia, has a strong genetic component. To date, 21 disease-risk loci have been identified through genome wide association studies (GWAS). However, the causative functional variant(s) within these loci are yet to be discovered. This study aimed to identify potential functional splicing mutations in the nine original GWAS-risk genes: CLU, PICALM, CR1, ABCA7, BIN1, the MS4A locus, CD2AP, EPHA1 and CD33. Target enriched next generation sequencing (NGS) was used to resequence the entire genetic region for each of these GWAS-risk loci in 96 LOAD patients and in silico databases were used to annotate the variants for functionality. Predicted splicing variants were further functionally characterised using splicing prediction software and minigene splicing assays. Following in silico annotation, 21 variants were predicted to influence splicing and, upon further annotation, four of these were examined utilising the in vitro minigene assay. Two variants, rs881768 A>G in ABCA7 and a novel variant 11: 60179827 T>G in MS4A6A were shown, in these cell assays, to affect the splicing of these genes. The method employed in the paper successfully identified potential splicing variants in GWAS-risk genes. Further investigation will be needed to understand the full effect of these variants on LOAD risk. However, these results suggest a possible pipeline in order to identify putative functional variants as a result of NGS in disease-associated loci although improvements are needed within the current prediction programme in order to reduce the number of false positives

Evaluating the role of pathogenic dementia variants in posterior cortical atrophy

Posterior cortical atrophy (PCA) is an understudied visual impairment syndrome most often due to “posterior Alzheimer's disease (AD)” pathology. Case studies detected mutations in PSEN1, PSEN2, GRN, MAPT, and PRNP in subjects with clinical PCA. To detect the frequency and spectrum of mutations in known dementia genes in PCA, we screened 124 European-American subjects with clinical PCA (n = 67) or posterior AD neuropathology (n = 57) for variants in genes implicated in AD, frontotemporal dementia, and prion disease using NeuroX, a customized exome array. Frequencies in PCA of the variants annotated as pathogenic or potentially pathogenic were compared against ∼4300 European-American population controls from the NHLBI Exome Sequencing Project. We identified 2 rare variants not previously reported in PCA, TREM2 Arg47His, and PSEN2 Ser130Leu. No other pathogenic or potentially pathogenic variants were detected in the screened dementia genes. In this first systematic variant screen of a PCA cohort, we report 2 rare mutations in TREM2 and PSEN2, validate our previously reported APOE ε4 association, and demonstrate the utility of NeuroX

Identification of a possible proteomic Biomarker in Parkinson’s Disease: Discovery and Replication in Blood, brain and CSF

Biomarkers to aid diagnosis and delineate the progression of Parkinson’s disease are vital for targeting treatment in the early phases of the disease. Here, we aim to discover a multi-protein panel representative of Parkinson’s and make mechanistic inferences from protein expression profiles within the broader objective of finding novel biomarkers. We used aptamer-based technology (SomaLogic®) to measure proteins in 1599 serum samples, 85 cerebrospinal fluid samples and 37 brain tissue samples collected from two observational longitudinal cohorts (the Oxford Parkinson’s Disease Centre and Tracking Parkinson’s) and the Parkinson’s Disease Brain Bank, respectively. Random forest machine learning was performed to discover new proteins related to disease status and generate multi-protein expression signatures with potential novel biomarkers. Differential regulation analysis and pathway analysis were performed to identify functional and mechanistic disease associations. The most consistent diagnostic classifier signature was tested across modalities [cerebrospinal fluid (area under curve) = 0.74, P = 0.0009; brain area under curve = 0.75, P = 0.006; serum area under curve = 0.66, P = 0.0002]. Focusing on serum samples and using only those with severe disease compared with controls increased the area under curve to 0.72 (P = 1.0 × 10(−4)). In the validation data set, we showed that the same classifiers were significantly related to disease status (P < 0.001). Differential expression analysis and weighted gene correlation network analysis highlighted key proteins and pathways with known relationships to Parkinson’s. Proteins from the complement and coagulation cascades suggest a disease relationship to immune response. The combined analytical approaches in a relatively large number of samples, across tissue types, with replication and validation, provide mechanistic insights into the disease as well as nominate a protein signature classifier that deserves further biomarker evaluation

Investigating the genetic architecture of dementia with Lewy bodies: a two-stage genome-wide association study

Background Dementia with Lewy bodies is the second most common form of dementia in elderly people but has been overshadowed in the research field, partly because of similarities between dementia with Lewy bodies, Parkinson’s disease, and Alzheimer’s disease. So far, to our knowledge, no large-scale genetic study of dementia with Lewy bodies has been done. To better understand the genetic basis of dementia with Lewy bodies, we have done a genome-wide association study with the aim of identifying genetic risk factors for this disorder. Methods In this two-stage genome-wide association study, we collected samples from white participants of European ancestry who had been diagnosed with dementia with Lewy bodies according to established clinical or pathological criteria. In the discovery stage (with the case cohort recruited from 22 centres in ten countries and the controls derived from two publicly available database of Genotypes and Phenotypes studies [phs000404.v1.p1 and phs000982.v1.p1] in the USA), we performed genotyping and exploited the recently established Haplotype Reference Consortium panel as the basis for imputation. Pathological samples were ascertained following autopsy in each individual brain bank, whereas clinical samples were collected by clinical teams after clinical examination. There was no specific timeframe for collection of samples. We did association analyses in all participants with dementia with Lewy bodies, and also in only participants with pathological diagnosis. In the replication stage, we performed genotyping of significant and suggestive results from the discovery stage. Lastly, we did a meta-analysis of both stages under a fixed-effects model and used logistic regression to test for association in each stage. Findings This study included 1743 patients with dementia with Lewy bodies (1324 with pathological diagnosis) and 4454 controls (1216 patients with dementia with Lewy bodies vs 3791 controls in the discovery stage; 527 vs 663 in the replication stage). Results confirm previously reported associations: APOE (rs429358; odds ratio [OR] 2·40, 95% CI 2·14–2·70; p=1·05 × 10–⁴⁸), SNCA (rs7681440; OR 0·73, 0·66–0·81; p=6·39 × 10–¹⁰), and GBA (rs35749011; OR 2·55, 1·88–3·46; p=1·78 × 10–⁹). They also provide some evidence for a novel candidate locus, namely CNTN1 (rs7314908; OR 1·51, 1·27–1·79; p=2·21 × 10–⁶); further replication will be important. Additionally, we estimate the heritable component of dementia with Lewy bodies to be about 36%. Interpretation Despite the small sample size for a genome-wide association study, and acknowledging the potential biases from ascertaining samples from multiple locations, we present the most comprehensive and well powered genetic study in dementia with Lewy bodies so far. These data show that common genetic variability has a role in the disease

Analysis of C9orf72 repeat expansions in a large international cohort of dementia with Lewy bodies

C9orf72 repeat expansions are a common cause of amyotrophic lateral sclerosis and frontotemporal dementia. To date, no large-scale study of dementia with Lewy bodies (DLB) has been undertaken to assess the role of C9orf72 repeat expansions in the disease. Here, we investigated the prevalence of C9orf72 repeat expansions in a large cohort of DLB cases and identified no pathogenic repeat expansions in neuropathologically or clinically defined cases, showing that C9orf72 repeat expansions are not causally associated with DLB. (C) 2016 Elsevier Inc. All rights reserved.Peer reviewe

A comprehensive screening of copy number variability in dementia with Lewy bodies

The role of genetic variability in dementia with Lewy bodies (DLB) is now indisputable; however, data regarding copy number variation (CNV) in this disease has been lacking. Here, we used whole-genome genotyping of 1454 DLB cases and 1525 controls to assess copy number variability. We used 2 algorithms to confidently detect CNVs, performed a case-control association analysis, screened for candidate CNVs previously associated with DLB-related diseases, and performed a candidate gene approach to fully explore the data. We identified 5 CNV regions with a significant genome-wide association to DLB; 2 of these were only present in cases and absent from publicly available databases: one of the regions overlapped LAPTM4B, a known lysosomal protein, whereas the other overlapped the NME1 locus and SPAG9. We also identified DLB cases presenting rare CNVs in genes previously associated with DLB or related neurodegenerative diseases, such as SNCA, APP, and MAPT. To our knowledge, this is the first study reporting genome-wide CNVs in a large DLB cohort. These results provide preliminary evidence for the contribution of CNVs in DLB risk. (C) 2019 Elsevier Inc. All rights reserved.Peer reviewe

Mutation analysis of sporadic early-onset Alzheimer's disease using the NeuroX array

We have screened sporadic early-onset Alzheimer's disease (sEOAD, n = 408) samples using the NeuroX array for known causative and predicted pathogenic variants in 16 genes linked to familial forms of neurodegeneration. We found 2 sEOAD individuals harboring a known causative variant in PARK2 known to cause early-onset Parkinson's disease; p.T240M (n = 1) and p.Q34fs delAG (n = 1). In addition, we identified 3 sEOAD individuals harboring a predicted pathogenic variant in MAPT (p.A469T), which has previously been associated with AD. It is currently unknown if these variants affect susceptibility to sEOAD, further studies would be needed to establish this. This work highlights the need to screen sEOAD individuals for variants that are more classically attributed to other forms of neurodegeneration

Polygenic risk score in postmortem diagnosed sporadic early-onset Alzheimer’s disease

Sporadic early onset Alzheimer’s disease (sEOAD) exhibits the symptoms of late onset Alzheimer’s disease (LOAD) but lacks the familial aspect of the early onset familial form. The genetics of Alzheimer’s disease (AD) identifies APOEε4 to be the greatest risk factor; however, it is a complex disease involving both environmental risk factors and multiple genetic loci. Polygenic risk scores (PRS) accumulate the total risk of a phenotype in an individual based on variants present in their genome. We determined whether sEOAD cases had a higher PRS compared to controls. A cohort of sEOAD cases were genotyped on the NeuroX array and PRS were generated using PRSice. The target dataset consisted of 408 sEOAD cases and 436 controls. The base dataset was collated by the IGAP consortium, with association data from 17,008 LOAD cases and 37,154 controls, which can be used for identifying sEOAD cases due to having shared phenotype. PRS were generated using all common SNPs between the base and target dataset, PRS were also generated using only SNPs within a 500kb region surrounding the APOE gene. Sex and number of APOE ε2 or ε4 alleles were used as variables for logistic regression and combined with PRS. The results show that PRS is higher on average in sEOAD cases than controls, although there is still overlap amongst the whole cohort. Predictive ability of identifying cases and controls using PRSice was calculated with 72.9% accuracy, greater than the APOE locus alone (65.2%). Predictive ability was further improved with logistic regression, identifying cases and controls with 75.5% accuracy