The Alzheimer's Disease Sequencing Project (ADSP) aims to identify novel genes influencing Alzheimer's disease (AD). Variants within genes known to cause dementias other than AD have previously been ...associated with AD risk. We describe evidence of co-segregation and associations between variants in dementia genes and clinically diagnosed AD within the ADSP.
We summarize the properties of known pathogenic variants within dementia genes, describe the co-segregation of variants annotated as "pathogenic" in ClinVar and new candidates observed in ADSP families, and test for associations between rare variants in dementia genes in the ADSP case-control study. The participants were clinically evaluated for AD, and they represent European, Caribbean Hispanic, and isolate Dutch populations.
Pathogenic variants in dementia genes were predominantly rare and conserved coding changes. Pathogenic variants within ARSA, CSF1R, and GRN were observed, and candidate variants in GRN and CHMP2B were nominated in ADSP families. An independent case-control study provided evidence of an association between variants in TREM2, APOE, ARSA, CSF1R, PSEN1, and MAPT and risk of AD. Variants in genes which cause dementing disorders may influence the clinical diagnosis of AD in a small proportion of cases within the ADSP.
IMPORTANCE: 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. OBJECTIVE: To search for rare variants contributing to the risk for EOAD. DESIGN, SETTING, AND PARTICIPANTS: 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. MAIN OUTCOMES AND MEASURES: 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. RESULTS: 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). CONCLUSIONS AND RELEVANCE: 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.
Introduction
Progranulin (GRN) mutations occur in frontotemporal lobar degeneration (FTLD) and in Alzheimer's disease (AD), often with TDP‐43 pathology.
Methods
We determined the frequency of rs5848 ...and rare, pathogenic GRN mutations in two autopsy and one family cohort. We compared Braak stage, β‐amyloid load, hyperphosphorylated tau (PHFtau) tangle density and TDP‐43 pathology in GRN carriers and non‐carriers.
Results
Pathogenic GRN mutations were more frequent in all cohorts compared to the Genome Aggregation Database (gnomAD), but there was no evidence for association with AD. Pathogenic GRN carriers had significantly higher PHFtau tangle density adjusting for age, sex and APOE ε4 genotype. AD patients with rs5848 had higher frequencies of hippocampal sclerosis and TDP‐43 deposits. Twenty‐two rare, pathogenic GRN variants were observed in the family cohort.
Discussion
GRN mutations in clinical and neuropathological AD increase the burden of tau‐related brain pathology but show no specific association with β‐amyloid load or AD.
Imputation has become a standard approach in genome-wide association studies (GWAS) to infer
untyped markers. Although feasibility for common variants imputation is well established, we aimed to ...assess rare and ultra-rare variants' imputation in an admixed Caribbean Hispanic population (CH).
We evaluated imputation accuracy in CH (
= 1,000), focusing on rare (0.1% ≤ minor allele frequency (MAF) ≤ 1%) and ultra-rare (MAF < 0.1%) variants. We used two reference panels, the Haplotype Reference Consortium (HRC;
= 27,165) and 1000 Genome Project (1000G phase 3;
= 2,504) and multiple phasing (SHAPEIT, Eagle2) and imputation algorithms (IMPUTE2, MACH-Admix). To assess imputation quality, we reported: (a) high-quality variant counts according to imputation tools' internal indexes (e.g., IMPUTE2 "Info" ≥ 80%). (b) Wilcoxon Signed-Rank Test comparing imputation quality for genotyped variants that were masked and imputed; (c) Cohen's kappa coefficient to test agreement between imputed and whole-exome sequencing (WES) variants; (d) imputation of G206A mutation in the
(ultra-rare in the general population an more frequent in CH) followed by confirmation genotyping. We also tested ancestry proportion (European, African and Native American) against WES-imputation mismatches in a Poisson regression fashion.
SHAPEIT2 retrieved higher percentage of imputed high-quality variants than Eagle2 (rare: 51.02% vs. 48.60%; ultra-rare 0.66% vs. 0.65%, Wilcoxon
-value < 0.001). SHAPEIT-IMPUTE2 employing HRC outperformed 1000G (64.50% vs. 59.17%; 1.69% vs. 0.75% for high-quality rare and ultra-rare variants, respectively, Wilcoxon
-value < 0.001). SHAPEIT-IMPUTE2 outperformed MaCH-Admix. Compared to 1000G, HRC-imputation retrieved a higher number of high-quality rare and ultra-rare variants, despite showing lower agreement between imputed and WES variants (e.g., rare: 98.86% for HRC vs. 99.02% for 1000G). High Kappa (
= 0.99) was observed for both reference panels. Twelve G206A mutation carriers were imputed and all validated by confirmation genotyping. African ancestry was associated with higher imputation errors for uncommon and rare variants (
-value < 1e-05).
Reference panels with larger numbers of haplotypes can improve imputation quality for rare and ultra-rare variants in admixed populations such as CH. Ethnic composition is an important predictor of imputation accuracy, with higher African ancestry associated with poorer imputation accuracy.
Haptoglobin (HP) is an antioxidant of apolipoprotein E (APOE), and previous reports have shown HP binds with APOE and amyloid beta (Aβ) to aid its clearance. A common structural variant of the HP ...gene distinguishes it into two alleles: HP1 and HP2.
HP genotypes were imputed in 29 cohorts from the Alzheimer's Disease Genetics Consortium (N = 20,512). Associations between the HP polymorphism and Alzheimer's disease (AD) risk and age of onset through APOE interactions were investigated using regression models.
The HP polymorphism significantly impacts AD risk in European-descent individuals (and in meta-analysis with African-descent individuals) by modifying both the protective effect of APOE ε2 and the detrimental effect of APOE ε4. The effect is particularly significant among APOE ε4 carriers.
The effect modification of APOE by HP suggests adjustment and/or stratification by HP genotype is warranted when APOE risk is considered. Our findings also provided directions for further investigations on potential mechanisms behind this association.
To analyze pedigrees with quantitative trait (QT) and sequence data, we developed a rare variant (RV) quantitative nonparametric linkage (QNPL) method, which evaluates sharing of minor alleles. ...RV-QNPL has greater power than the traditional QNPL that tests for excess sharing of minor and major alleles. RV-QNPL is robust to population substructure and admixture, locus heterogeneity, and inclusion of nonpathogenic variants and can be readily applied outside of coding regions. When QNPL was used to analyze common variants, it often led to loci mapping to large intervals, e.g., >40 Mb. In contrast, when RVs are analyzed, regions are well defined, e.g., a gene. Using simulation studies, we demonstrate that RV-QNPL is substantially more powerful than applying traditional QNPL methods to analyze RVs. RV-QNPL was also applied to analyze age-at-onset (AAO) data for 107 late-onset Alzheimer's disease (LOAD) pedigrees of Caribbean Hispanic and European ancestry with whole-genome sequence data. When AAO of AD was analyzed regardless of APOE ε4 status, suggestive linkage (LOD = 2.4) was observed with RVs in KNDC1 and nominally significant linkage (p < 0.05) was observed with RVs in LOAD genes ABCA7 and IQCK. When AAO of AD was analyzed for APOE ε4 positive family members, nominally significant linkage was observed with RVs in APOE, while when AAO of AD was analyzed for APOE ε4 negative family members, nominal significance was observed for IQCK and ADAMTS1. RV-QNPL provides a powerful resource to analyze QTs in families to elucidate their genetic etiology.
Abstract INTRODUCTION Despite a two‐fold risk, individuals of African ancestry have been underrepresented in Alzheimer's disease (AD) genomics efforts. METHODS Genome‐wide association studies (GWAS) ...of 2,903 AD cases and 6,265 controls of African ancestry. Within‐dataset results were meta‐analyzed, followed by functional genomics analyses. RESULTS A novel AD‐risk locus was identified in MPDZ on chromosome (chr) 9p23 (rs141610415, MAF = 0.002, P = 3.68×10 −9 ). Two additional novel common and nine rare loci were identified with suggestive associations ( P < 9×10 −7 ). Comparison of association and linkage disequilibrium (LD) patterns between datasets with higher and lower degrees of African ancestry showed differential association patterns at chr12q23.2 ( ASCL1 ), suggesting that this association is modulated by regional origin of local African ancestry. DISCUSSION These analyses identified novel AD‐associated loci in individuals of African ancestry and suggest that degree of African ancestry modulates some associations. Increased sample sets covering as much African genetic diversity as possible will be critical to identify additional loci and deconvolute local genetic ancestry effects. Highlights Genetic ancestry significantly impacts risk of Alzheimer's Disease (AD). Although individuals of African ancestry are twice as likely to develop AD, they are vastly underrepresented in AD genomics studies. The Alzheimer's Disease Genetics Consortium has previously identified 16 common and rare genetic loci associated with AD in African American individuals. The current analyses significantly expand this effort by increasing the sample size and extending ancestral diversity by including populations from continental Africa. Single variant meta‐analysis identified a novel genome‐wide significant AD‐risk locus in individuals of African ancestry at the MPDZ gene, and 11 additional novel loci with suggestive genome‐wide significance at P < 9×10−7. Comparison of African American datasets with samples of higher degree of African ancestry demonstrated differing patterns of association and linkage disequilibrium at one of these loci, suggesting that degree and/or geographic origin of African ancestry modulates the effect at this locus. These findings illustrate the importance of increasing number and ancestral diversity of African ancestry samples in AD genomics studies to fully disentangle the genetic architecture underlying AD, and yield more effective ancestry‐informed genetic screening tools and therapeutic interventions.
Background
Despite large genome‐wide association studies, only ∼30% of the heritability of Alzheimer’s disease is explained. The Alzheimer’s Disease Sequencing Project Whole Exome Sequencing (ADSP ...WES) has identified millions of genetic variants, over 97% of which are rare (MAF<1%), with 23% appearing in only one person. These rare variants could provide valuable information about new and previously identified risk loci for AD. However, current analysis strategies do not have power to detect associations for such rare variants.
Method
We have developed a protein structure‐based approach that evaluates rare missense variants based on their spatial distribution in a known protein structure rather than on their allele frequency. We hypothesize that AD cases exhibit clustering of rare variants within a protein structure relative to cognitive normal controls. We applied our approach to the ADSP WES Discovery Dataset with 5,522 AD cases and 4,919 controls on 5,969 genes with known structures from the Protein Data Bank (PDB) and 17,450 genes with Alpha Fold2 predicted structures. Only rare variants(MAF<0.05) were included in the analysis. We validated the identified genes within an independent dataset with multi‐ancestry individuals (ADSP WGS Replication) and a European‐ancestry dataset with 15,078 individuals (ADSP validation dataset).
Result
We identified three significant genes (TREM2, SORL1, and EXOC3L4) and one suggestive gene (CSF1R) with AD‐associated spatial clusterings from the ADSP WES data. For TREM2(PDB:6XDS; p‐value = 3.592E‐07) and SORL1(PDB:3WSY;p‐value = 6.701E‐05), two known AD genes, the spatial clusters are significant after excluding known AD risk variants, indicating the presence of additional low‐frequency risk variants within these genes. EXOC3L4(AlphaFold2:Q17RC7;p‐value = 2.504E‐05) is a novel AD risk gene that has a cluster of variants primarily shared by AD cases around the C terminal end of the Sec6 domain. This cluster replicated with significant associations in the ADSP WGS Replication and ADSP validation dataset.
Conclusion
Our result suggests multiple rare missense variants in TREM2, SORL1, EXOC3L4, and likely CSF1R are associated with AD risk by spatial clustering within the protein structure. These spatial clusterings have been replicated in two additional datasets. These spatial patterns may indicate potential functional regions in the protein structure associated with AD risk and are prime targets for further experimental validation.
Background
This study used admixture mapping to prioritize the genetic regions associated with Alzheimer's disease (AD) in African American (AA) individuals, followed by ancestry‐aware regression ...analysis to fine‐map the prioritized regions.
Methods
We analyzed 10,271 individuals from 17 different AA datasets. We performed admixture mapping and meta‐analyzed the results. We then used regression analysis, adjusting for local ancestry main effects and interactions with genotype, to refine the regions identified from admixture mapping. Finally, we leveraged in silico annotation and differential gene expression data to prioritize AD‐related variants and genes.
Results
Admixture mapping identified two genome‐wide significant loci on chromosomes 17p13.2 (p = 2.2 × 10−5) and 18q21.33 (p = 1.2 × 10−5). Our fine mapping of the chromosome 17p13.2 and 18q21.33 regions revealed several interesting genes such as the MINK1, KIF1C, and BCL2.
Discussion
Our ancestry‐aware regression approach showed that AA individuals have a lower risk of AD if they inherited African ancestry admixture block at the 17p13.2 locus.
Highlights
We identified two genome‐wide significant admixture mapping signals: on chromosomes 17p13.2 and 18q21.33, which are novel in African American (AA) populations.
Our ancestry‐aware regression approach showed that AA individuals have a lower risk of Alzheimer's disease (AD) if they inherited African ancestry admixture block at the 17p13.2 locus.
We found that the overall proportion of African ancestry does not differ between the cases and controls that suggest African genetic ancestry alone is not likely to explain the AD prevalence difference between AA and non‐Hispanic White populations.
Background
Polygenic risk scores (PRS) provide an overall estimate of the individual’s genetic propensity to a trait by combining sparse information scattered across multiple genetic loci which often ...display small effect sizes. Most genetic studies are of European ancestry, ultimately limiting the use of PRS in other ethnicities. Here we constructed and validated a PRS for late‐onset Alzheimer’s Disease (LOAD) in Caribbean Hispanics (CH).
Method
We employed genome‐wide association summary statistics from 4,312 CH to construct an ancestry‐specific PRS (“CH‐PRS”) in an independent validation CH cohort (N=1,850). CH‐PRS performance was evaluated using the area under the receiver operator characteristic (ROC) curves and logistic regression to evaluate strength of the association with LOAD and statistical significance. We sought to further replicate the CH‐PRS in an independent CH dataset (Alzheimer’s Disease Research Center “ADC‐CH”, N=200) and in a brain autopsy cohort (N=33). We also studied the CH‐PRS performance in predicting conversion to LOAD in a subset of non‐demented individuals at baseline with longitudinal data (N=600), employing a Cox regression model. Finally, we tested the effect of ethnicity on PRS performances by employing European (EUR) and African American (AA) ancestry studies as discovery datasets to construct alternative PRSs (“EUR‐PRS”, “AA‐PRS”) in our validation cohort.
Result
In the full model (LOAD ∼ CH‐PRS + sex + age + APOE‐ɛ4), the AUC reached 74.02% (OR=1.51 95%CI=1.36‐1.68), raising to >75% in APOE‐ɛ4 non‐carriers. In the autopsy cohort, higher CH‐PRS was significantly associated with pathological AD diagnosis (AD ∼ CH‐PRS; AUC=72%; OR=2.35, 95%CI=1.0‐5.52), and AUC=83% in the full model. In ADC‐CH, the PRS showed significant association with LOAD (OR=1.61, 95%CI=1.19‐2.17). CH‐PRS significantly predicted conversion to LOAD over time (HR=1.96, CI=1.61‐2.39). EUR‐PRS and AA‐PRS reached lower prediction accuracy (AUC=58% and 53%, respectively).
Conclusion
PRS is an effective strategy to delineate individual risk profiles as shown by our cross‐sectional and longitudinal analyses as well as associations with well‐established AD‐hallmarks. Enriching diversity in genetic studies is indeed critical to provide a PRS tool that is effective in predicting LOAD risk across ethnic populations.
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