Interpretable machine-learning prediction of PSEN1 missense variant pathogenicity based on multi-omics enrichment in six core Alzheimer's disease genes.

BackgroundAn estimated ∼2‐3% of early‐onset Alzheimer’s disease (AD) patients carry a predicted rare damaging variant in the SORL1 gene, which encodes the SORLA receptor protein. While protein‐truncating variants (loss‐of‐function, LOF) occur almost exclusively in AD patients, the large majority of damaging variants are missense variants. However, the effect of individual missense variants on AD risk varies widely, warranting a variant pathogenicity screen.MethodsWe prioritized genetic variants in SORL1 by investigating to what extent they lead to familial disease when they occur in protein homologs. We used a sample of 18,959 non‐related AD cases from the ADES/ADSP studies to investigate the age‐at‐onset in carriers of a prioritized SORL1 variant relative to WT SORL1 in context of APOE genotype. Second, since functional SORLA is cleaved at the neuronal membrane into soluble SORLA (sSORLA) we used Western Blot and proteomics to test whether SORL1 variants affected the level of sSORLA in CSF.ResultsCompared to SORL1 wild type AD patients, carriers of prioritized SORL1 variants had an expedited age at onset similar to carriers of LoF variants: of those homozygous for APOE‐ε4 who carried a prioritized SORL1 variant, 80% had AD by age 61 (95%CI 60‐NA, n = 6) compared to 73 for WT‐SORL1 carriers (95%CI 72‐73, n = 1091). Of those heterozygous for APOE‐ε4 who carried a prioritized SORL1 variant, 80% had AD by age 71 (95%CI 68‐76, n = 62) compared to age 78 WT‐SORL1 carriers (95%CI 78‐78, n = 4816). Of those negative for the APOE‐ε4 allele with a SORL1 variant, 80% had AD by 83 years (95%CI 78‐90, n = 54) compared to 85 years for WT‐SORL1 carriers (95%CI 85‐85, n = 5773). These effects were absent for non‐prioritized missense variants. Second, using WB we found that sSORLA levels in carriers of pathogenic missense variants were reduced to ∼50% relative to carriers of benign SORL1 variants, while levels of truncating SORL1 variants were reduced to ∼65%.DiscussionPrioritizing SORL1 missense variants according to variant‐effects in protein homologs leads to variants that expedite the age‐at‐onset independent of APOE background. Carriers of pathogenic missense or truncating variants have low CSF‐sSORLA levels, which may serve as a biomarker for impaired SORL1.

Intrinsic Connectivity Identifies the Hippocampus as a Main Crossroad between Alzheimer’s and Semantic Dementia-Targeted Networks

Accumulating evidence suggests that genetic variants in the SORL1 gene are associated with Alzheimer disease (AD), but a strategy to identify which variants are pathogenic is lacking. In a discovery sample of 115 SORL1 variants detected in 1908 Dutch AD cases and controls, we identified the variant characteristics associated with SORL1 variant pathogenicity. Findings were replicated in an independent sample of 103 SORL1 variants detected in 3193 AD cases and controls. In a combined sample of the discovery and replication samples, comprising 181 unique SORL1 variants, we developed a strategy to classify SORL1 variants into five subtypes ranging from pathogenic to benign. We tested this pathogenicity screen in SORL1 variants reported in two independent published studies. SORL1 variant pathogenicity is defined by the Combined Annotation Dependent Depletion (CADD) score and the minor allele frequency (MAF) reported by the Exome Aggregation Consortium (ExAC) database. Variants predicted strongly damaging (CADD score >30), which are extremely rare (ExAC-MAF <1 × 10−5) increased AD risk by 12-fold (95% CI 4.2–34.3; P=5 × 10−9). Protein-truncating SORL1 mutations were all unknown to ExAC and occurred exclusively in AD cases. More common SORL1 variants (ExAC-MAF≥1 × 10−5) were not associated with increased AD risk, even when predicted strongly damaging. Findings were independent of gender and the APOE-ɛ4 allele. High-risk SORL1 variants were observed in a substantial proportion of the AD cases analyzed (2%). Based on their effect size, we propose to consider high-risk SORL1 variants next to variants in APOE, PSEN1, PSEN2 and APP for personalized risk assessments in clinical practice.

Twenty Years of the Alzheimer’s Disease Amyloid Hypothesis: A Genetic Perspective

DLB and PDD: a role for mutations in dementia and Parkinson disease genes?

Associations of risk genes with onset age and plasma biomarkers of Alzheimer's disease: a large case-control study in mainland China.

ACMG/AMP interpretation of BRCA1 missense variants: Structure-informed scores add evidence strength granularity to the PP3/BP4 computational evidence

To realize precision medicine, it is important to realize the detection of the individual atrophy of Alzheimer's disease (AD) patients. Our objective is to find individual brain regions of interest (ROIs) in AD patients via an unsupervised deep learning network.This study used structural Magnetic Resonance Imaging (sMRI) scans with the 732 healthy control (HC) subjects and 202 AD patients from the Alzheimer's disease Neuroimaging Initiative (ADNI), and the 105 HC subjects were collected at the Xuanwu Hospital. An unsupervised deep learning network based on Adversarial Autoencoders (AAE) was proposed to delineate the individual atrophy of AD patients. In the proposed model, Variational Autoencoders (VAE) and Generative Adversarial Networks (GAN) were combined to learn the potential distribution and train a generator. In this step, the 530 HCs from ADNI were applied as the training dataset and the 105 HCs from Xuanwu Hospital were applied as an external validation dataset. The structural similarity (SSIM) was used to judge the robustness of the proposed model. Then, ROIs of the 202 AD patients were detected. In order to verify the clinical performance of these ROIs, other 202 HCs were selected from ADNI and a multilayer perceptron (MLP) was used to classify AD versus HC by 5 folder cross-validation. In the comparative experiments, we compared our model with three other previous models.The SSIM reached 0.86 in both training and external validation datasets. Eventually, the classification accuracy of our model achieved 0.94±0.02. In the meanwhile, the classification accuracies were 0.89±0.01, 0.85±0.04 and 0.91±0.03 for the three previous methods.Our deep learning model could detect individual atrophy in AD patients. It may be a useful tool for AD diagnosis in clinics.

The overlap between Alzheimer's disease and epilepsy uncovered by transcriptome sequencing

BackgroundThe sortilin‐related receptor 1 protein, SORL1, interacts with retromer to regulate trafficking of cargo out of the early endosome. Genetic variants in SORL1 that lead to a premature protein truncation (PTVs) are observed almost exclusively in Alzheimer’s disease (AD) patients, suggesting SORL1’s haploinsufficiency may be causal for AD. However, the large majority of SORL1 variants are rare missense variants which affect diverse structural domains, some of which may be causative for disease or (strongly) risk‐increasing, while others are (likely) benign.MethodsWe prioritized functionally relevant SORL1 protein‐residues by domain‐mapping of disease mutations, DMDM, (Figure A). We evaluated the effect of prioritized genetic SORL1‐variants identified in 18,959 AD cases and 21,893 non‐demented controls, on age at onset and on disease risk. Second, since functional SORLA is cleaved at the neuronal membrane into soluble SORLA (sSORLA) we used Western Blot and proteomics to test whether SORL1 variants affected the level of sSORLA in CSF.ResultsPrioritized missense variants (PMVs) associated with a 10.5‐fold increased risk of early onset AD (p = 3.0 × 10‐29) and a 4.5‐fold increased risk of late onset AD (p = 4.9 × 10‐11). The distribution of the ages at onset of PMV‐ and PTV‐carriers overlapped (Figure B), which was on average &gt;8‐years earlier than carriers of wild‐type SORL1, regardless of APOE genotype. Variants affecting the ‘YWTD‐motif’ or ‘calcium cage’ residues (Figure A) were observed only in AD cases. Carriers of such variants had an earlier age at onset compared to carriers of PTVs, indicative of a dominant negative effect, while other variants had a milder effect than losing a copy (Figure C). Using WB, we found that sSORLA levels in carriers of specific PMVs were reduced to ∼50% relative to carriers of benign SORL1 variants, while levels of PTVs were reduced to ∼65%.DiscussionSpecific SORL1 variants occur only in AD cases and have an early age at onset, and some may have a dominant negative effect, independent of APOE background. Carriers of these variants might be identified by a very low level of CSF sSORLA. Such variants might be grouped with pathogenic variants in the three autosomal dominant AD genes PSEN1/2 and APP.

BackgroundRare pathogenic SORL1 gene variants have been identified as strong risk‐increasing factors in Alzheimer’s disease (AD). Recent analysis indicated that SORL1 loss‐of‐function (LoF) variants associated with a ∼40‐fold increased risk for early‐onset AD, while missense variants that were prioritized using in silico variant pathogenicity‐prediction algorithms associated only with a 2‐fold risk increase. However, current risk‐prediction strategies do not take SORL1‐specific features into account. We aimed to design a variant classification strategy that adds in‐depth knowledge of SORLA structural folding and function to the variant risk prediction.MethodWe identified coding SORL1 variants in a discovery/replication sample of whole exome sequencing data from the ADES/ADSP studies comprising 18,959 cases and 21,893 controls after quality control. We selected only the extremely rare variants (MAF&lt;0.05%) and further divided these into LoF(n = 77) and missense variants(n = 539). With an in‐depth evaluation of protein functional domains (informed by pathogenic variants in homologous genes) combined with their predicted pathogenicity (REVEL algorithm) we prioritized missense variants into ‘high priority’, ‘moderate priority’ and ‘low priority’. We compared the age‐dependent penetrance of missense variants for each priority‐category and LoF variants, in context of APOE genotype.ResultCarriers of prioritized missense variants had a similar age‐dependent penetrance as LoF variants, with median age at AD‐onset of ∼65 years which is significantly lower compared to non‐carriers (∼72 years). In addition, carriers of prioritized SORL1 missense variants have an expedited age at onset by ∼7 years independent of their APOE genotype. If we look at a 80% penetrance level, SORL1 carriers with homozygous ε4 background had AD by age 61 (95%CI 60‐NA, n = 6) compared to age 73 (95%CI 72‐73, n = 1091) for SORL1 WT carriers. Carriers with a heterozygous background had AD by age 71 (95%CI 68‐76, n = 62) compared to 78 (95%CI 78‐78, n = 4816) for WT. For a non‐ε4 background the age at onset for carriers was 83 years (95%CI 78‐90, n = 54) compared to 85 years (95%CI 85‐85, n = 5773) for WT. The moderate and low priority group showed no significant difference compared to WT.ConclusionOur prioritization model can successfully identify likely pathogenic missense SORL1 variants, allowing for better identification of patients with possible SORL1‐associated‐AD.

Background: Recent research indicates that lipid metabolism and autophagy play crucial roles in the development of Alzheimer's disease (AD). Investigating the relationship between AD diagnosis and gene expression related to lipid metabolism, autophagy, and lipophagy may improve early diagnosis and the identification of therapeutic targets. Methods: Transcription datasets from AD patients were obtained from the Gene Expression Omnibus (GEO). Genes associated with lipid metabolism, autophagy, and lipophagy were sourced from the Gene Set Enrichment Analysis (GSEA) database and the Human Autophagy Database (HADb). Lipophagy-related hub genes were identified using a combination of Limma analysis, weighted gene co-expression network analysis (WGCNA), and machine learning techniques. Based on these hub genes, we developed an AD risk prediction nomogram and validated its diagnostic accuracy using three external validation datasets. Additionally, the expression levels of the hub genes were assessed through quantitative reverse transcription polymerase chain reaction (qRT-PCR). Results: Our analysis identified three hub genes-ACBD5, GABARAPL1, and HSPA8-as being associated with AD progression. The nomogram constructed from these hub genes achieved an area under the curve (AUC) value of 0.894 for AD risk prediction, with all validation sets yielding AUC values greater than 0.8, indicating excellent diagnostic efficacy. qRT-PCR results further corroborated the associations between these hub genes and AD development. Conclusions: This study identified and validated three lipophagy-related hub genes and developed a reliable diagnostic model, offering insights into the pathology of AD and facilitating the diagnosis of AD patients.

New cerebrospinal fluid biomarkers in Alzheimer’s disease

Mutations in APP, PSEN1, and PSEN2 lead to early-onset Alzheimer disease (EOAD) but account for only approximately 11% of EOAD overall, leaving most of the genetic risk for the most severe form of Alzheimer disease unexplained. This extreme phenotype likely harbors highly penetrant risk variants, making it primed for discovery of novel risk genes and pathways for AD. To search for rare variants contributing to the risk for EOAD. In this case-control study, whole-exome sequencing (WES) was performed in 51 non-Hispanic white (NHW) patients with EOAD (age at onset <65 years) and 19 Caribbean Hispanic families previously screened as negative for established APP, PSEN1, and PSEN2 causal variants. Participants were recruited from John P. Hussman Institute for Human Genomics, Case Western Reserve University, and Columbia University. Rare, deleterious, nonsynonymous, or loss-of-function variants were filtered to identify variants in known and suspected AD genes, variants in multiple unrelated NHW patients, variants present in 19 Hispanic EOAD WES families, and genes with variants in multiple unrelated NHW patients. These variants/genes were tested for association in an independent cohort of 1524 patients with EOAD, 7046 patients with late-onset AD (LOAD), and 7001 cognitively intact controls (age at examination, >65 years) from the Alzheimer's Disease Genetics Consortium. The study was conducted from January 21, 2013, to October 13, 2016. Alzheimer disease diagnosed according to standard National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer Disease and Related Disorders Association criteria. Association between Alzheimer disease and genetic variants and genes was measured using logistic regression and sequence kernel association test-optimal gene tests, respectively. Of the 1524 NHW patients with EOAD, 765 (50.2%) were women and mean (SD) age was 60.0 (4.9) years; of the 7046 NHW patients with LOAD, 4171 (59.2%) were women and mean (SD) age was 77.4 (8.6) years; and of the 7001 NHW controls, 4215 (60.2%) were women and mean (SD) age was 77.4 (8.6) years. The gene PSD2, for which multiple unrelated NHW cases had rare missense variants, was significantly associated with EOAD (P = 2.05 × 10-6; Bonferroni-corrected P value [BP] = 1.3 × 10-3) and LOAD (P = 6.22 × 10-6; BP = 4.1 × 10-3). A missense variant in TCIRG1, present in a NHW patient and segregating in 3 cases of a Hispanic family, was more frequent in EOAD cases (odds ratio [OR], 2.13; 95% CI, 0.99-4.55; P = .06; BP = 0.413), and significantly associated with LOAD (OR, 2.23; 95% CI, 1.37-3.62; P = 7.2 × 10-4; BP = 5.0 × 10-3). A missense variant in the LOAD risk gene RIN3 showed suggestive evidence of association with EOAD after Bonferroni correction (OR, 4.56; 95% CI, 1.26-16.48; P = .02, BP = 0.091). In addition, a missense variant in RUFY1 identified in 2 NHW EOAD cases showed suggestive evidence of an association with EOAD as well (OR, 18.63; 95% CI, 1.62-213.45; P = .003; BP = 0.129). The genes PSD2, TCIRG1, RIN3, and RUFY1 all may be involved in endolysosomal transport-a process known to be important to development of AD. Furthermore, this study identified shared risk genes between EOAD and LOAD similar to previously reported genes, such as SORL1, PSEN2, and TREM2.

High Activities of BACE1 in Brains with Mild Cognitive Impairment