Late-Onset Alzheimer's Disease Family Research Articles

The National Institute on Aging Late-Onset Alzheimer's Disease Family Based Study: A resource for genetic discovery.

The National Institute on Aging Late-Onset Alzheimer's Disease Family Based Study (NIA-LOAD FBS) was established to study the genetic etiology of Alzheimer's disease (AD). Recruitment focused on families with two living affected siblings and a third first-degree relative similar in age with or without dementia. Uniform assessments were completed, DNA was obtained, as was neuropathology, when possible. Apolipoprotein E (APOE) genotypes, genome-wide single nucleotide polymorphism (SNP) arrays, and sequencing was completed in most families. APOE genotype modified the age-at-onset in many large families. Novel variants and known variants associated with early- and late-onset AD and frontotemporal dementia were identified supporting an international effort to solve AD genetics. The NIA-LOAD FBS is the largest collection of familial AD worldwide, and data or samples have been included in 123 publications addressing the genetic etiology of AD. Genetic heterogeneity and variability in the age-at-onset provides opportunities to investigate the complexity of familial AD.

Fast Algorithms for Conducting Large-Scale GWAS of Age-at-Onset Traits Using Cox Mixed-Effects Models.

Age-at-onset is one of the critical traits in cohort studies of age-related diseases. Large-scale genome-wide association studies (GWAS) of age-at-onset traits can provide more insights into genetic effects on disease progression and transitions between stages. Moreover, proportional hazards (or Cox) regression models can achieve higher statistical power in a cohort study than a case-control trait using logistic regression. Although mixed-effects models are widely used in GWAS to correct for sample dependence, application of Cox mixed-effects models (CMEMs) to large-scale GWAS is so far hindered by intractable computational cost. In this work, we propose COXMEG, an efficient R package for conducting GWAS of age-at-onset traits using CMEMs. COXMEG introduces fast estimation algorithms for general sparse relatedness matrices including, but not limited to, block-diagonal pedigree-based matrices. COXMEG also introduces a fast and powerful score test for dense relatedness matrices, accounting for both population stratification and family structure. In addition, COXMEG generalizes existing algorithms to support positive semidefinite relatedness matrices, which are common in twin and family studies. Our simulation studies suggest that COXMEG, depending on the structure of the relatedness matrix, is orders of magnitude computationally more efficient than coxme and coxph with frailty for GWAS. We found that using sparse approximation of relatedness matrices yielded highly comparable results in controlling false-positive rate and retaining statistical power for an ethnically homogeneous family-based sample. By applying COXMEG to a study of Alzheimer's disease (AD) with a Late-Onset Alzheimer's Disease Family Study from the National Institute on Aging sample comprising 3456 non-Hispanic whites and 287 African Americans, we identified the APOE ε4 variant with strong statistical power (P = 1e-101), far more significant than that reported in a previous study using a transformed variable and a marginal Cox model. Furthermore, we identified novel SNP rs36051450 (P = 2e-9) near GRAMD1B, the minor allele of which significantly reduced the hazards of AD in both genders. These results demonstrated that COXMEG greatly facilitates the application of CMEMs in GWAS of age-at-onset traits.

Identification of Alzheimer's disease-associated rare coding variants in the ECE2 gene.

Accumulation of amyloid β protein (Aβ) due to increased generation and/or impaired degradation plays an important role in Alzheimer's disease (AD) pathogenesis. In this report, we describe the identification of rare coding mutations in the endothelin-converting enzyme 2 (ECE2) gene in 1 late-onset AD family, and additional case-control cohort analysis indicates ECE2 variants associated with the risk of developing AD. The 2 mutations (R186C and F751S) located in the peptidase domain in the ECE2 protein were found to severely impair the enzymatic activity of ECE2 in Aβ degradation. We further evaluated the effect of the R186C mutation in mutant APP-knockin mice. Overexpression of wild-type ECE2 in the hippocampus reduced amyloid load and plaque formation, and improved learning and memory deficits in the AD model mice. However, the effect was abolished by the R186C mutation in ECE2. Taken together, the results demonstrated that ECE2 peptidase mutations contribute to AD pathogenesis by impairing Aβ degradation, and overexpression of ECE2 alleviates AD phenotypes. This study indicates that ECE2 is a risk gene for AD development and pharmacological activation of ECE2 could be a promising strategy for AD treatment.

Genotype patterns at PICALM, CR1, BIN1, CLU , and APOE genes are associated with episodic memory

Several genome-wide association studies (GWAS) have associated variants in late-onset Alzheimer disease (LOAD) susceptibility genes; however, these single nucleotide polymorphisms (SNPs) have very modest effects, suggesting that single SNP approaches may be inadequate to identify genetic risks. An alternative approach is the use of multilocus genotype patterns (MLGPs) that combine SNPs at different susceptibility genes. Using data from 1,365 subjects in the National Institute on Aging Late-Onset Alzheimer's Disease Family Study, we conducted a family-based association study in which we tabulated MLGPs for SNPs at CR1, BIN1, CLU, PICALM, and APOE. We used generalized estimating equations to model episodic memory as the dependent endophenotype of LOAD and the MLGPs as predictors while adjusting for sex, age, and education. Several genotype patterns influenced episodic memory performance. A pattern that included PICALM and CLU was the strongest genotypic profile for lower memory performance (β = -0.32, SE = 0.19, p = 0.021). The effect was stronger after addition of APOE (p = 0.016). Two additional patterns involving PICALM, CR1, and APOE and another pattern involving PICALM, BIN1, and APOE were also associated with significantly poorer memory performance (β = -0.44, SE = 0.09, p = 0.009 and β = -0.29, SE = 0.07, p = 0.012) even after exclusion of patients with LOAD. We also identified genotype pattern involving variants in PICALM, CLU, and APOE as a predictor of better memory performance (β = 0.26, SE = 0.10, p = 0.010). MLGPs provide an alternative analytical approach to predict an individual's genetic risk for episodic memory performance, a surrogate indicator of LOAD. Identifying genotypic patterns contributing to the decline of an individual's cognitive performance may be a critical step along the road to preclinical detection of Alzheimer disease.

Identification of Novel Loci for Alzheimer Disease and Replication of CLU, PICALM, and BIN1 in Caribbean Hispanic Individuals

To identify novel loci for late-onset Alzheimer disease (LOAD) in Caribbean Hispanic individuals and to replicate the findings in a publicly available data set from the National Institute on Aging Late-Onset Alzheimer's Disease Family Study. Nested case-control genome-wide association study. The Washington Heights-Inwood Columbia Aging Project and the Estudio Familiar de Influencia Genetica de Alzheimer study. Five hundred forty-nine affected and 544 unaffected individuals of Caribbean Hispanic ancestry. The Illumina HumanHap 650Y chip for genotyping. Clinical diagnosis or pathologically confirmed diagnosis of LOAD. The strongest support for allelic association was for rs9945493 on 18q23 (P=1.7×10(-7)), but 22 additional single-nucleotide polymorphisms (SNPs) had a P value less than 9×10(-6) under 3 different analyses: unadjusted and stratified by the presence or absence of the APOE ε4 allele. Of these SNPs, 5 SNPs (rs4669573 and rs10197851 on 2p25.1; rs11711889 on 3q25.2; rs1117750 on 7p21.1; and rs7908652 on 10q23.1) were associated with LOAD in an independent cohort from the National Institute on Aging Late-Onset Alzheimer's Disease Family Study. We also replicated genetic associations for CLU, PICALM, and BIN1. Our genome-wide search of Caribbean Hispanic individuals identified several novel genetic variants associated with LOAD and replicated these associations in a white cohort. We also replicated associations in CLU, PICALM, and BIN1 in the Caribbean Hispanic cohort.

Telephone Assessment of Cognitive Function in the Late-Onset Alzheimer's Disease Family Study

Administration of cognitive test batteries by telephone has been shown to be a valid and cost-effective means of assessing cognition, but it remains relatively uncommon in epidemiological research. To develop composite cognitive measures and assess how much of the variability in their scores is associated with mode of test administration (ie, in person or by telephone). Cross-sectional cohort study. Late-Onset Alzheimer's Disease Family Study conducted at 18 centers across the United States. A total of 1584 persons, 368 with dementia, from 646 families. Scores on composite measures of memory and cognitive function derived from a battery of 7 performance tests administered in person (69%) or by telephone (31%) by examiners who underwent a structured performance-based training program with annual recertification. Based in part on the results of a factor analysis of the 7 tests, we developed summary measures of working memory, declarative memory, episodic memory, semantic memory, and global cognition. In linear regression analyses, mode of test administration accounted for less than 2% of the variance in the measures. In mixed-effects models, variability in cognitive scores due to center was small relative to variability due to differences between individuals and families. In epidemiologic research on aging and Alzheimer disease, assessment of cognition by telephone has little effect on performance and provides operational flexibility and a means of reducing both costs and missing data.

Analyses of the National Institute on Aging Late-Onset Alzheimer's Disease Family Study: Implication of Additional Loci

Analyses of the National Institute on Aging Late-Onset Alzheimer's Disease Family Study: Implication of Additional Loci

Analyses of the National Institute on Aging Late-Onset Alzheimer's Disease Family Study

To identify putative genetic loci related to the risk of late-onset Alzheimer disease (LOAD). Linkage analysis and family-based and case-control association analyses from a genomewide scan using approximately 6000 single-nucleotide polymorphic markers at an average intermarker distance of 0.65 cM. The National Institute on Aging Genetics Initiative for Late-Onset Alzheimer's Disease (NIA-LOAD) was created to expand the resources for studies to identify additional genes contributing to the risk for LOAD. We investigated 1902 individuals from 328 families with LOAD and 236 unrelated control subjects. Clinical diagnosis of LOAD. The strongest overall finding was at chromosome 19q13.32, confirming the effect of the apolipoprotein E gene on LOAD risk in the family-based and case-control analyses. However, single-nucleotide polymorphisms at the following loci were also statistically significant in 1 or more of the analyses performed: 7p22.2, 7p21.3, and 16q21 in the linkage analyses; 17q21.31 and 22q11.21 in the family-based association analysis; and 7q31.1 and 22q12.3 in the case-control analysis. Positive associations at 7q31.1 and 20q13.33 were also significant in the meta-analysis results in a publicly available database. Several additional loci may harbor genetic variants associated with LOAD. This data set provides a wealth of phenotypic and genotypic information for use as a resource in discovery and confirmatory research.