IMPORTANCE: Because APOE locus variants contribute to risk of late-onset Alzheimer disease (LOAD) and to differences in age at onset (AAO), it is important to know whether other established LOAD risk loci also affect AAO in affected participants.
OBJECTIVES: To investigate the effects of known Alzheimer disease risk loci in modifying AAO and to estimate their cumulative effect on AAO variation using data from genome-wide association studies in the Alzheimer Disease Genetics Consortium.
DESIGN, SETTING, AND PARTICIPANTS: The Alzheimer Disease Genetics Consortium comprises 14 case-control, prospective, and family-based data sets with data on 9162 participants of white race/ethnicity with Alzheimer disease occurring after age 60 years who also had complete AAO information, gathered between 1989 and 2011 at multiple sites by participating studies. Data on genotyped or imputed single-nucleotide polymorphisms most significantly associated with risk at 10 confirmed LOAD loci were examined in linear modeling of AAO, and individual data set results were combined using a random-effects, inverse variance-weighted meta-analysis approach to determine whether they contribute to variation in AAO. Aggregate effects of all risk loci on AAO were examined in a burden analysis using genotype scores weighted by risk effect sizes.
MAIN OUTCOMES AND MEASURES: Age at disease onset abstracted from medical records among participants with LOAD diagnosed per standard criteria.
RESULTS: Analysis confirmed the association of APOE with earlier AAO (P = 3.3 × 10(-96)), with associations in CR1 (rs6701713, P = 7.2 × 10(-4)), BIN1 (rs7561528, P = 4.8 × 10(-4)), and PICALM (rs561655, P = 2.2 × 10(-3)) reaching statistical significance (P < .005). Risk alleles individually reduced AAO by 3 to 6 months. Burden analyses demonstrated that APOE contributes to 3.7% of the variation in AAO (R(2) = 0.256) over baseline (R(2) = 0.221), whereas the other 9 loci together contribute to 2.2% of the variation (R(2) = 0.242).
CONCLUSIONS AND RELEVANCE: We confirmed an association of APOE (OMIM 107741) variants with AAO among affected participants with LOAD and observed novel associations of CR1 (OMIM 120620), BIN1 (OMIM 601248), and PICALM (OMIM 603025) with AAO. In contrast to earlier hypothetical modeling, we show that the combined effects of Alzheimer disease risk variants on AAO are on the scale of, but do not exceed, the APOE effect. While the aggregate effects of risk loci on AAO may be significant, additional genetic contributions to AAO are individually likely to be small.
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Naive T cell populations are maintained in the periphery at relatively constant levels via mechanisms that control expansion and contraction and are associated with competition for homeostatic cytokines. It has been shown that in a lymphopenic environment naive T cells undergo expansion due, at least in part, to additional availability of IL-7. We have previously found that T cell-intrinsic deletion of TNFR-associated factor (TRAF) 6 (TRAF6ΔT) in mice results in diminished peripheral CD8 T cell numbers. In this study, we report that whereas naive TRAF6ΔT CD8 T cells exhibit normal survival when transferred into a normal T cell pool, proliferation of naive TRAF6ΔT CD8 T cells under lymphopenic conditions is defective. We identified IL-18 as a TRAF6-activating factor capable of enhancing lymphopenia-induced proliferation (LIP) in vivo, and that IL-18 synergizes with high-dose IL-7 in a TRAF6-dependent manner to induce slow, LIP/homeostatic-like proliferation of naive CD8 T cells in vitro. IL-7 and IL-18 act synergistically to upregulate expression of IL-18R genes, thereby enhancing IL-18 activity. In this context, IL-18R signaling increases PI3K activation and was found to sensitize naive CD8 T cells to a model noncognate self-peptide ligand in a way that conventional costimulation via CD28 could not. We propose that synergistic sensitization by IL-7 and IL-18 to self-peptide ligand may represent a novel costimulatory pathway for LIP.
Recent advances in high-throughput sequencing allow researchers to examine the transcriptome in more detail than ever before. Using a method known as high-throughput small RNA-sequencing, we can now profile the expression of small regulatory RNAs such as microRNAs and small interfering RNAs (siRNAs) with a great deal of sensitivity. However, there are many other types of small RNAs (<50nt) present in the cell, including fragments derived from snoRNAs (small nucleolar RNAs), snRNAs (small nuclear RNAs), scRNAs (small cytoplasmic RNAs), tRNAs (transfer RNAs), and transposon-derived RNAs. Here, we present a user's guide for CoRAL (Classification of RNAs by Analysis of Length), a computational method for discriminating between different classes of RNA using high-throughput small RNA-sequencing data. Not only can CoRAL distinguish between RNA classes with high accuracy, but it also uses features that are relevant to small RNA biogenesis pathways. By doing so, CoRAL can give biologists a glimpse into the characteristics of different RNA processing pathways and how these might differ between tissue types, biological conditions, or even different species. CoRAL is available at http://wanglab.pcbi.upenn.edu/coral/.
Hippocampal sclerosis of aging (HS-Aging) is a high-morbidity brain disease in the elderly but risk factors are largely unknown. We report the first genome-wide association study (GWAS) with HS-Aging pathology as an endophenotype. In collaboration with the Alzheimer’s Disease Genetics Consortium, data were analyzed from large autopsy cohorts: (#1) National Alzheimer’s Coordinating Center (NACC); (#2) Rush University Religious Orders Study and Memory and Aging Project; (#3) Group Health Research Institute Adult Changes in Thought study; (#4) University of California at Irvine 90+ Study; and (#5) University of Kentucky Alzheimer’s Disease Center. Altogether, 363 HS-Aging cases and 2,303 controls, all pathologically confirmed, provided statistical power to test for risk alleles with large effect size. A two-tier study design included GWAS from cohorts #1-3 (Stage I) to identify promising SNP candidates, followed by focused evaluation of particular SNPs in cohorts #4-5 (Stage II). Polymorphism in the ATP-binding cassette, sub-family C member 9 (ABCC9) gene, also known as sulfonylurea receptor 2, was associated with HS-Aging pathology. In the meta-analyzed Stage I GWAS, ABCC9 polymorphisms yielded the lowest p values, and factoring in the Stage II results, the meta-analyzed risk SNP (rs704178:G) attained genome-wide statistical significance (p = 1.4 × 10(-9)), with odds ratio (OR) of 2.13 (recessive mode of inheritance). For SNPs previously linked to hippocampal sclerosis, meta-analyses of Stage I results show OR = 1.16 for rs5848 (GRN) and OR = 1.22 rs1990622 (TMEM106B), with the risk alleles as previously described. Sulfonylureas, a widely prescribed drug class used to treat diabetes, also modify human ABCC9 protein function. A subsample of patients from the NACC database (n = 624) were identified who were older than age 85 at death with known drug history. Controlling for important confounders such as diabetes itself, exposure to a sulfonylurea drug was associated with risk for HS-Aging pathology (p = 0.03). Thus, we describe a novel and targetable dementia risk factor.
BACKGROUND: Alzheimer’s disease is a common debilitating dementia with known heritability, for which 20 late onset susceptibility loci have been identified, but more remain to be discovered. This study sought to identify new susceptibility genes, using an alternative gene-wide analytical approach which tests for patterns of association within genes, in the powerful genome-wide association dataset of the International Genomics of Alzheimer’s Project Consortium, comprising over 7 m genotypes from 25,580 Alzheimer’s cases and 48,466 controls.
PRINCIPAL FINDINGS: In addition to earlier reported genes, we detected genome-wide significant loci on chromosomes 8 (TP53INP1, p = 1.4×10-6) and 14 (IGHV1-67 p = 7.9×10-8) which indexed novel susceptibility loci.
SIGNIFICANCE: The additional genes identified in this study, have an array of functions previously implicated in Alzheimer’s disease, including aspects of energy metabolism, protein degradation and the immune system and add further weight to these pathways as potential therapeutic targets in Alzheimer’s disease.
An understanding of the anatomic distributions of major neurodegenerative disease lesions is important to appreciate the differential clinical profiles of these disorders and to serve as neuropathological standards for emerging molecular neuroimaging methods. To address these issues, here we present a comparative survey of the topographical distribution of the defining molecular neuropathological lesions among 10 neurodegenerative diseases from a large and uniformly assessed brain collection. Ratings of pathological severity in 16 brain regions from 671 cases with diverse neurodegenerative diseases are summarized and analyzed. These include: 1) amyloid-β and tau lesions in Alzheimer’s disease; 2) tau lesions in three other tauopathies including Pick’s disease, progressive supranuclear palsy and corticobasal degeneration; 3) α-synuclein inclusion ratings in four synucleinopathies including Parkinson’s disease, Parkinson’s disease with dementia, dementia with Lewy bodies, and multiple system atrophy; and 4) TDP-43 lesions in two TDP-43 proteinopathies, including frontotemporal lobar degeneration associated with TDP-43 and amyotrophic lateral sclerosis. The data presented graphically and topographically confirm and extend previous pathological anatomic descriptions and statistical comparisons highlight the lesion distributions that either overlap or distinguish the diseases in each molecular disease category.
RNA is often altered post-transcriptionally by the covalent modification of particular nucleotides; these modifications are known to modulate the structure and activity of their host RNAs. The recent discovery that an RNA methyl-6 adenosine demethylase (FTO) is a risk gene in obesity has brought to light the significance of RNA modifications to human biology. These noncanonical nucleotides, when converted to cDNA in the course of RNA sequencing, can produce sequence patterns that are distinguishable from simple base-calling errors. To determine whether these modifications can be detected in RNA sequencing data, we developed a method that can not only locate these modifications transcriptome-wide with single nucleotide resolution, but can also differentiate between different classes of modifications. Using small RNA-seq data we were able to detect 92% of all known human tRNA modification sites that are predicted to affect RT activity. We also found that different modifications produce distinct patterns of cDNA sequence, allowing us to differentiate between two classes of adenosine and two classes of guanine modifications with 98% and 79% accuracy, respectively. To show the robustness of this method to sample preparation and sequencing methods, as well as to organismal diversity, we applied it to a publicly available yeast data set and achieved similar levels of accuracy. We also experimentally validated two novel and one known 3-methylcytosine (3mC) sites predicted by HAMR in human tRNAs. Researchers can now use our method to identify and characterize RNA modifications using only RNA-seq data, both retrospectively and when asking questions specifically about modified RNA.
Eleven susceptibility loci for late-onset Alzheimer’s disease (LOAD) were identified by previous studies; however, a large portion of the genetic risk for this disease remains unexplained. We conducted a large, two-stage meta-analysis of genome-wide association studies (GWAS) in individuals of European ancestry. In stage 1, we used genotyped and imputed data (7,055,881 SNPs) to perform meta-analysis on 4 previously published GWAS data sets consisting of 17,008 Alzheimer’s disease cases and 37,154 controls. In stage 2, 11,632 SNPs were genotyped and tested for association in an independent set of 8,572 Alzheimer’s disease cases and 11,312 controls. In addition to the APOE locus (encoding apolipoprotein E), 19 loci reached genome-wide significance (P < 5 × 10(-8)) in the combined stage 1 and stage 2 analysis, of which 11 are newly associated with Alzheimer's disease.
High-density SNP genotyping technology provides a low-cost, effective tool for conducting Genome Wide Association (GWA) studies. The wide adoption of GWA studies has indeed led to discoveries of disease- or trait-associated SNPs, some of which were subsequently shown to be causal. However, the nearly universal shortcoming of many GWA studies–missing heritability–has prompted great interest in searching for other types of genetic variation, such as copy number variation (CNV). Certain CNVs have been reported to alter disease susceptibility. Algorithms and tools have been developed to identify CNVs using SNP array hybridization intensity data. Such an approach provides an additional source of data with almost no extra cost. In this unit, we demonstrate the steps for calling CNVs from Illumina SNP array data using PennCNV and performing association analysis using R and PLINK.
SUMMARY: We report our new DRAW+SneakPeek software for DNA-seq analysis. DNA resequencing analysis workflow (DRAW) automates the workflow of processing raw sequence reads including quality control, read alignment and variant calling on high-performance computing facilities such as Amazon elastic compute cloud. SneakPeek provides an effective interface for reviewing dozens of quality metrics reported by DRAW, so users can assess the quality of data and diagnose problems in their sequencing procedures. Both DRAW and SneakPeek are freely available under the MIT license, and are available as Amazon machine images to be used directly on Amazon cloud with minimal installation.
AVAILABILITY: DRAW+SneakPeek is released under the MIT license and is available for academic and nonprofit use for free. The information about source code, Amazon machine images and instructions on how to install and run DRAW+SneakPeek locally and on Amazon elastic compute cloud is available at the National Institute on Aging Genetics of Alzheimer’s Disease Data Storage Site (http://www.niagads.org/) and Wang lab Web site (http://wanglab.pcbi.upenn.edu/).