Dataset Information

Late-onset Alzheimer disease genetic variants in posterior cortical atrophy and posterior AD.

ABSTRACT: To investigate association of genetic risk factors for late-onset Alzheimer disease (LOAD) with risk of posterior cortical atrophy (PCA), a syndrome of visual impairment with predominant Alzheimer disease (AD) pathology in posterior cortical regions, and with risk of "posterior AD" neuropathology.We assessed 81 participants with PCA diagnosed clinically and 54 with neuropathologic diagnosis of posterior AD vs 2,523 controls for association with 11 significant single nucleotide polymorphisms (SNPs) from published LOAD risk genome-wide association studies.There was highly significant association with APOE ?4 and increased risk of PCA (p = 0.0003, odds ratio [OR] = 3.17) and posterior AD (p = 1.11 × 10(-17), OR = 6.43). No other locus was significant after corrections for multiple testing, although rs11136000 near CLU (p = 0.019, OR = 0.60) and rs744373 near BIN1 (p = 0.025, OR = 1. 63) associated nominally significantly with posterior AD, and rs3851179 at the PICALM locus had significant association with PCA (p = 0.0003, OR = 2.84). ABCA7 locus SNP rs3764650, which was also tested under the recessive model because of Hardy-Weinberg disequilibrium, also had nominally significant association with PCA risk. The direction of association at APOE, CLU, and BIN1 loci was the same for participants with PCA and posterior AD. The effects for all SNPs, except rs3851179, were consistent with those for LOAD risk.We identified a significant effect for APOE and nominate CLU, BIN1, and ABCA7 as additional risk loci for PCA and posterior AD. Our findings suggest that at least some of the genetic risk factors for LOAD are shared with these atypical conditions and provide effect-size estimates for their future genetic studies.

SUBMITTER: Carrasquillo MM

PROVIDER: S-EPMC4001196 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature

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Late-onset Alzheimer disease genetic variants in posterior cortical atrophy and posterior AD.

Carrasquillo Minerva M MM Khan Qurat ul Ain Qu Murray Melissa E ME Krishnan Siddharth S Aakre Jeremiah J Pankratz V Shane VS Nguyen Thuy T Ma Li L Bisceglio Gina G Petersen Ronald C RC Younkin Steven G SG Dickson Dennis W DW Boeve Bradley F BF Graff-Radford Neill R NR Ertekin-Taner Nilüfer N

Neurology 20140326 16

<h4>Objective</h4>To investigate association of genetic risk factors for late-onset Alzheimer disease (LOAD) with risk of posterior cortical atrophy (PCA), a syndrome of visual impairment with predominant Alzheimer disease (AD) pathology in posterior cortical regions, and with risk of "posterior AD" neuropathology.<h4>Methods</h4>We assessed 81 participants with PCA diagnosed clinically and 54 with neuropathologic diagnosis of posterior AD vs 2,523 controls for association with 11 significant si ...[more]

PMID: 24670887

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Project description:Defining a signature of cortical regions of interest preferentially affected by Alzheimer disease (AD) pathology may offer improved sensitivity to early AD compared to hippocampal volume or mesial temporal lobe alone. Since late-onset Alzheimer disease (LOAD) participants tend to have age-related comorbidities, the younger-onset age in autosomal dominant AD (ADAD) may provide a more idealized model of cortical thinning in AD. To test this, the goals of this study were to compare the degree of overlap between the ADAD and LOAD cortical thinning maps and to evaluate the ability of the ADAD cortical signature regions to predict early pathological changes in cognitively normal individuals. We defined and analyzed the LOAD cortical maps of cortical thickness in 588 participants from the Knight Alzheimer Disease Research Center (Knight ADRC) and the ADAD cortical maps in 269 participants from the Dominantly Inherited Alzheimer Network (DIAN) observational study. Both cohorts were divided into three groups: cognitively normal controls (nADRC = 381; nDIAN = 145), preclinical (nADRC = 153; nDIAN = 76), and cognitively impaired (nADRC = 54; nDIAN = 48). Both cohorts underwent clinical assessments, 3T MRI, and amyloid PET imaging with either 11C-Pittsburgh compound B or 18F-florbetapir. To generate cortical signature maps of cortical thickness, we performed a vertex-wise analysis between the cognitively normal controls and impaired groups within each cohort using six increasingly conservative statistical thresholds to determine significance. The optimal cortical map among the six statistical thresholds was determined from a receiver operating characteristic analysis testing the performance of each map in discriminating between the cognitively normal controls and preclinical groups. We then performed within-cohort and cross-cohort (e.g. ADAD maps evaluated in the Knight ADRC cohort) analyses to examine the sensitivity of the optimal cortical signature maps to the amyloid levels using only the cognitively normal individuals (cognitively normal controls and preclinical groups) in comparison to hippocampal volume. We found the optimal cortical signature maps were sensitive to early increases in amyloid for the asymptomatic individuals within their respective cohorts and were significant beyond the inclusion of hippocampus volume, but the cortical signature maps performed poorly when analyzing across cohorts. These results suggest the cortical signature maps are a useful MRI biomarker of early AD-related neurodegeneration in preclinical individuals and the pattern of decline differs between LOAD and ADAD.

Project description:ImportanceIdentifying the youngest age when Alzheimer disease (AD) influences cognition and the earliest affected cognitive domains will improve understanding of the natural history of AD and approaches to early diagnosis.ObjectiveTo evaluate the age at which cognitive differences between individuals with higher compared with lower genetic risk of AD are first apparent and which cognitive assessments show the earliest difference.Design, setting, and participantsThis cross-sectional study used data from UK Biobank participants of European genetic ancestry, aged 40 years or older, who contributed genotypic and cognitive test data from January 1, 2006, to December 31, 2015. Data analysis was performed from March 10, 2020, to January 4, 2022.ExposureThe AD genetic risk score (GRS), which is a weighted sum of 23 single-nucleotide variations.Main outcomes and measuresSeven cognitive tests were administered via touchscreen at in-person visits or online. Cognitive domains assessed included fluid intelligence, episodic memory, processing speed, executive functioning, and prospective memory. Multiple cognitive measures were derived from some tests, yielding 32 separate measures. Interactions between age and AD-GRS for each of the 32 cognitive measures were tested with linear regression using a Bonferroni-corrected P value threshold. For cognitive measures with significant evidence of age by AD-GRS interaction, the youngest age of interaction was assessed with new regression models, with nonlinear specification of age terms. Models with youngest age of interaction from 40 to 70 years, in 1-year increments, were compared, and the best-fitting model for each cognitive measure was chosen. Results across cognitive measures were compared to determine which cognitive indicators showed earliest AD-related change.ResultsA total of 405 050 participants (mean [SD] age, 57.1 [7.9] years; 54.1% female) were included. Sample sizes differed across cognitive tests (from 12 455 to 404 682 participants). The AD-GRS significantly modified the association with age on 13 measures derived from the pairs matching (range in difference in mean cognition per decade increase in age for 1-SD higher AD-GRS, 2.5%-11.5%), symbol digit substitution (range in difference in mean cognition per decade increase in age for 1-SD higher AD-GRS, 2.0%-5.8%), and numeric memory tests (difference in mean cognition per decade increase in age for 1-SD higher AD-GRS, 8.8%) (P = 1.56 × 10-3). Best-fitting models suggested that cognitive scores of individuals with a high vs low AD-GRS began to diverge by 56 years of age for all 13 measures and by 47 years of age for 9 measures.Conclusions and relevanceIn this cross-sectional study, by early midlife, subtle differences in memory and attention were detectable among individuals with higher genetic risk of AD.

Project description:ObjectiveTo determine whether atrophy relates to phenotypical variants of posterior cortical atrophy (PCA) recently proposed in clinical criteria (i.e., dorsal, ventral, dominant-parietal, and caudal) we assessed associations between latent atrophy factors and cognition.MethodsWe employed a data-driven Bayesian modeling framework based on latent Dirichlet allocation to identify latent atrophy factors in a multicenter cohort of 119 individuals with PCA (age 64 ± 7 years, 38% male, Mini-Mental State Examination 21 ± 5, 71% β-amyloid positive, 29% β-amyloid status unknown). The model uses standardized gray matter density images as input (adjusted for age, sex, intracranial volume, MRI scanner field strength, and whole-brain gray matter volume) and provides voxelwise probabilistic maps for a predetermined number of atrophy factors, allowing every individual to express each factor to a degree without a priori classification. Individual factor expressions were correlated to 4 PCA-specific cognitive domains (object perception, space perception, nonvisual/parietal functions, and primary visual processing) using general linear models.ResultsThe model revealed 4 distinct yet partially overlapping atrophy factors: right-dorsal, right-ventral, left-ventral, and limbic. We found that object perception and primary visual processing were associated with atrophy that predominantly reflects the right-ventral factor. Furthermore, space perception was associated with atrophy that predominantly represents the right-dorsal and right-ventral factors. However, individual participant profiles revealed that the large majority expressed multiple atrophy factors and had mixed clinical profiles with impairments across multiple domains, rather than displaying a discrete clinical-radiologic phenotype.ConclusionOur results indicate that specific brain behavior networks are vulnerable in PCA, but most individuals display a constellation of affected brain regions and symptoms, indicating that classification into 4 mutually exclusive variants is unlikely to be clinically useful.

Project description:ImportanceGenetic causes of late-onset Alzheimer disease (LOAD) are not completely explained by known genetic loci. Whole-exome and whole-genome sequencing can improve the understanding of the causes of LOAD and provide initial steps required to identify potential therapeutic targets.ObjectiveTo identify the genetic loci for LOAD across different ethnic groups.Design, setting, and participantsThis multicenter cohort study was designed to analyze whole-exome sequencing data from a multiethnic cohort using a transethnic gene-kernel association test meta-analysis, adjusted for sex, age, and principal components, to identify genetic variants associated with LOAD. A meta-analysis was conducted on the results of 2 independent studies of whole-exome and whole-genome sequence data from individuals of European ancestry. This group of European American, African American, and Caribbean Hispanic individuals participating in an urban population-based study were the discovery cohort; the additional cohorts included affected individuals and control participants from 2 publicly available data sets. Replication was achieved using independent data sets from Caribbean Hispanic families with multiple family members affected by LOAD and the International Genetics of Alzheimer Project.Main outcomes and measuresLate-onset Alzheimer disease.ResultsThe discovery cohort included 3595 affected individuals, while the additional cohorts included 5931 individuals with LOAD and 5504 control participants. Of 3916 individuals in the discovery cohort, we included 3595 individuals (1397 with LOAD and 2198 cognitively healthy controls; 2451 [68.2%] women; mean [SD] age, 80.3 [6.83] years). Another 321 individuals (8.2%) were excluded because of non-LOAD diagnosis, age younger than 60 years, missing covariates, duplicate data, or genetic outlier status. Gene-based tests that compared affected individuals (n = 7328) and control participants (n = 7702) and included only rare and uncommon variants annotated as having moderate-high functional effect supported PINX1 (8p23.1) as a locus with gene-wide significance (P = 2.81 × 10-6) after meta-analysis across the 3 studies. The PINX1 finding was replicated using data from the family-based study and the International Genetics of Alzheimer Project. Full meta-analysis of discovery and replication cohorts reached a P value of 6.16 × 10-7 for PINX1 (in 7620 affected individuals vs 7768 control participants). We also identified TREM2 in an annotation model that prioritized highly deleterious variants with a combined annotation dependent depletion greater than 20 (P= 1.0 × 10-7).Conclusions and relevanceThis gene-based, transethnic approach identified PINX1, a gene involved in telomere integrity, and TREM2, a gene with a product of an immune receptor found in microglia, as associated with LOAD. Both genes have well-established roles in aging and neurodegeneration.

Dataset Information

Late-onset Alzheimer disease genetic variants in posterior cortical atrophy and posterior AD.

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Late-onset Alzheimer disease genetic variants in posterior cortical atrophy and posterior AD.

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