Anwendung der Next-Generation-Sequencing Methode bei Hundertjährigen, um genetische Varianten zu identifizieren, die für Langlebigkeit beim Menschen prädisponieren

The genetic contribution to adult human lifespan is ~25-30% and is assumed to be determined by rare variants or common variants with rather small effects. The current hypothesis is that long-lived individuals (LLI) are enriched with longevity-associated variants that may compensate for the damaging effects of disease-associated variants and are thought to be of rather low frequency. In this project, we combined next-generation sequencing with case-control association studies to identify new exonic longevity influencing variants as those are more likely to be functionally relevant due to amino acid substitution. To reach this goal, we performed whole genome and exome sequencing of six centenarians (108-114 years) of European origin using two different technologies (SOLiD and Illumina). A fraction of the detected single nucleotide variants (SNVs) were selected for a follow-up by genotyping based on two different approaches. The first approach focused on SNVs with minor allele frequencies (MAF) 1 to 50%, which resulted in 116 SNVs that were genotyped in our German sample of 1,610 LLI and 1,104 controls. Seven significant association signals were obtained and further investigated for a replication experiment in independent French (1,269 LLI and 1,834 younger controls) and Danish populations (910 LLI and 760 controls), but none of the associations could be confirmed. The second approach was an intensive follow-up by focusing on low-frequency variants (MAF≤10%). Using eight different bioinformatic prediction tools to evaluate the functional impact of SNVs and overlaying the initial SNV list with locations associated with genome-wide association (GWAS) hit regions resulted in 48 variants that were selected for genotyping, where three SNVs showed a significant association signal in the German longevity sample. The top-ranking SNV (PCCA=3.7e-08, OR=1.7) was selected for a replication experiment in the Danish population but could not be confirmed. In addition, longevity genes and pathways from known model organisms were used as filter masks for the variant selection.Der genetische Einfluss auf die Lebensspanne liegt beim erwachsenen Menschen bei etwa 25- 30%, und es wird angenommen, dass dieser Beitrag durch seltene oder häufige Varianten mit eher geringen Effekten bestimmt wird. Die aktuelle Hypothese ist, dass bei langlebigen Individuen (LLI) eine Anreicherung langlebigkeitsassoziierter Varianten vorliegt, welche die schädlichen Effekte von krankheitsassoziierten Varianten kompensieren, und dass solche Varianten eine eher niedrige Frequenz aufweisen. In diesem Projekt kombinierten wir die next generation sequencing Methode mit Fall-Kontroll-Assoziationsstudien, um neue exonische Langlebigkeits-Varianten zu identifizieren, welche durch Änderungen der Aminosäuresequenz potentiell funktionell relevant sein könnten. Um dieses Ziel zu erreichen, führten wir mit zwei verschiedenen Technologien (SOLiD und Illumina) eine Gesamtgenom- und Exom-Sequenzierung von sechs Hundertjährigen (108–114 Jahre) europäischen Ursprungs durch. Einige der detektierten Einzelbasenvarianten (single nucleotide variants = SNVs) wurden anhand von zwei verschiedenen Ansätzen für eine nachfolgende Genotypisierung ausgewählt. Im ersten Ansatz lag der Fokus auf exonischen SNVs mit einer Frequenz des seltenen Alles (minor allele frequency = MAF) von 1-50%. Daraus resultierten 116 SNVs, die in unserer deutschen Stichprobe von 1.610 LLI und 1.104 Kontrollen typisiert worden sind. Hier zeigten sich sieben signifikante Assoziationssignale, die in einem folgenden Replikationsexperiment in einer unabhängigen französischen (1.269 LLI und 1.834 jüngere Kontrollen) und dänischen Stichprobe (910 LLI und 760 Kontrollen) untersucht wurden. Allerdings konnte hierbei keine dieser Assoziationen bestätigt werden

Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer's disease

We identified rare coding variants associated with Alzheimer’s disease (AD) in a 3-stage case-control study of 85,133 subjects. In stage 1, 34,174 samples were genotyped using a whole-exome microarray. In stage 2, we tested associated variants (P<1×10-4) in 35,962 independent samples using de novo genotyping and imputed genotypes. In stage 3, an additional 14,997 samples were used to test the most significant stage 2 associations (P<5×10-8) using imputed genotypes. We observed 3 novel genome-wide significant (GWS) AD associated non-synonymous variants; a protective variant in PLCG2 (rs72824905/p.P522R, P=5.38×10-10, OR=0.68, MAFcases=0.0059, MAFcontrols=0.0093), a risk variant in ABI3 (rs616338/p.S209F, P=4.56×10-10, OR=1.43, MAFcases=0.011, MAFcontrols=0.008), and a novel GWS variant in TREM2 (rs143332484/p.R62H, P=1.55×10-14, OR=1.67, MAFcases=0.0143, MAFcontrols=0.0089), a known AD susceptibility gene. These protein-coding changes are in genes highly expressed in microglia and highlight an immune-related protein-protein interaction network enriched for previously identified AD risk genes. These genetic findings provide additional evidence that the microglia-mediated innate immune response contributes directly to AD development

Creating a platform for collaborative genomic research

ABSTRACT Objectives The objective was to deliver a platform to accelerate collaborative research into the genetics of dementia. The project is part of a larger effort to establish a platform for research incorporating components related to epidemiological data, imaging, wearable technology and tissue banking with initiatives to link these different data sets. The genomics platform aimed to address a number of challenges encountered by researchers: • Enabling patient-level linkage between genomic and other datasets • Selection of study cohorts using genotypic and phenotypic characteristics • Collaborating with other groups to perform large-scale meta analysis • Supporting repeatable analysis workflows Approach The genomics platform was developed using a mixture of open source, commercial and bespoke components developed for this project. It is designed to securely handle data and scale to cope with increasing data volumes as well as the quantity and complexity of research undertaken. It is located in a data centre where researchers can access, explore and analyse the data in a secure environment addressing data security and privacy concerns. The platform integrates with a supercomputer allowing complex analysis of data to be undertaken easily using novel code or predefined workflows. The platform was designed and developed through close collaboration with a prominent academic research team. Results We have created a collaborative genomics informatics platform that provides efficient and intuitive selection of study cohorts from multiple data sources, with potentially varying data formats and types, based on both genotypic and phenotypic characteristics. Researchers can combine cohorts definitions using set operations and publish these definitions allowing other researchers to apply them to other datasets. There is an internet accessible portal which allows researchers to share results of their analysis and support meta analysis of this research. A sophisticated search engine allows these results to be found based on genetic information or annotations. This portal accelerates meta-analysis, an intrinsically collaborative endeavour in genomics research. There is also close integration with HPC (High Performance Computing) resources for computationally expensive tasks. The platform is underpinned by analytical workflows that not only accelerate and enable reproducible research, but reduce the technical barriers to its use. The platform enables patient-level linkage with other non-genomics data sources through integration with other research platforms. Conclusion The developed genomics informatics platform provides a step-change in this type of genetic research, accelerating reproducible collaborative research across multiple disparate organisations and data sources, of varying type and complexity

Identification and characterization of two functional variants in the human longevity gene FOXO3

FOXO3 is one of the few established longevity genes. Here, the authors fine-map the FOXO3-longevity association to two intronic SNPs and, using luciferase assays and EMSAs, show that these SNPs affect binding of transcription factors CTCF and SRF and associate with FOXO3 expression

Common polygenic variation enhances risk prediction for Alzheimer's disease.

Background: The identification of subjects at high risk for Alzheimer’s disease is important for prognosis and early intervention. We investigated the polygenic architecture of Alzheimer’s disease (AD) and the accuracy of AD prediction models, including and excluding the polygenic component in the model. Methods: This study used genotype data from the powerful dataset comprising 17,008 cases and 37,154 controls obtained from the International Genomics of Alzheimer’s Project (IGAP). Polygenic score analysis tested whether the alleles identified to associate with disease in one sample set were significantly enriched in the cases relative to the controls in an independent sample. The disease prediction accuracy was investigated by means of sensitivity, specificity, Area Under the receiver operating characteristic Curve (AUC) and positive predictive value (PPV). Results: We observed significant evidence for a polygenic component enriched in Alzheimer’s disease (p=4.9x10-26). This enrichment remained significant after APOE and other genome-wide associated regions were excluded (p=3.4x10 19). The best prediction accuracy AUC=78% was achieved by a logistic regression model with APOE, the polygenic score as predictors and age. When looking at the genetic component only, the PPV was 81%, increasing to 82% when age was added as a predictor. Setting the total normalised polygenic score of greater than 0.91, the positive predictive value has reached 90%. Conclusion: Polygenic score has strong predictive utility of Alzheimer’s disease risk and is a valuable research tool in experimental designs, e.g. for selecting Alzheimer’s disease patients into clinical trials

Publisher Correction: Identification and characterization of two functional variants in the human longevity gene FOXO3

The original version of this Article contained an error in the spelling of the author Robert Häsler, which was incorrectly given as Robert Häesler. This has now been corrected in both the PDF and HTML versions of the Article

Associations between potentially modifiable risk factors and Alzheimer disease: A mendelian randomization study

BACKGROUND: Potentially modifiable risk factors including obesity, diabetes, hypertension, and smoking are associated with Alzheimer disease (AD) and represent promising targets for intervention. However, the causality of these associations is unclear. We sought to assess the causal nature of these associations using Mendelian randomization (MR). METHODS AND FINDINGS: We used SNPs associated with each risk factor as instrumental variables in MR analyses. We considered type 2 diabetes (T2D, NSNPs = 49), fasting glucose (NSNPs = 36), insulin resistance (NSNPs = 10), body mass index (BMI, NSNPs = 32), total cholesterol (NSNPs = 73), HDL-cholesterol (NSNPs = 71), LDL-cholesterol (NSNPs = 57), triglycerides (NSNPs = 39), systolic blood pressure (SBP, NSNPs = 24), smoking initiation (NSNPs = 1), smoking quantity (NSNPs = 3), university completion (NSNPs = 2), and years of education (NSNPs = 1). We calculated MR estimates of associations between each exposure and AD risk using an inverse-variance weighted approach, with summary statistics of SNP-AD associations from the International Genomics of Alzheimer's Project, comprising a total of 17,008 individuals with AD and 37,154 cognitively normal elderly controls. We found that genetically predicted higher SBP was associated with lower AD risk (odds ratio [OR] per standard deviation [15.4 mm Hg] of SBP [95% CI]: 0.75 [0.62-0.91]; p = 3.4 × 10(-3)). Genetically predicted higher SBP was also associated with a higher probability of taking antihypertensive medication (p = 6.7 × 10(-8)). Genetically predicted smoking quantity was associated with lower AD risk (OR per ten cigarettes per day [95% CI]: 0.67 [0.51-0.89]; p = 6.5 × 10(-3)), although we were unable to stratify by smoking history; genetically predicted smoking initiation was not associated with AD risk (OR = 0.70 [0.37, 1.33]; p = 0.28). We saw no evidence of causal associations between glycemic traits, T2D, BMI, or educational attainment and risk of AD (all p > 0.1). Potential limitations of this study include the small proportion of intermediate trait variance explained by genetic variants and other implicit limitations of MR analyses. CONCLUSIONS: Inherited lifetime exposure to higher SBP is associated with lower AD risk. These findings suggest that higher blood pressure--or some environmental exposure associated with higher blood pressure, such as use of antihypertensive medications--may reduce AD risk

Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing

Risk for late-onset Alzheimer’s disease (LOAD), the most prevalent dementia, is partially driven by genetics. To identify LOAD risk loci, we performed a large genome-wide association meta-analysis of clinically diagnosed LOAD (94,437 individuals). We confirm 20 previous LOAD risk loci and identify five new genome-wide loci (IQCK, ACE, ADAM10, ADAMTS1, and WWOX), two of which (ADAM10, ACE) were identified in a recent genome-wide association (GWAS)-by-familial-proxy of Alzheimer’s or dementia. Fine-mapping of the human leukocyte antigen (HLA) region confirms the neurological and immune-mediated disease haplotype HLA-DR15 as a risk factor for LOAD. Pathway analysis implicates immunity, lipid metabolism, tau binding proteins, and amyloid precursor protein (APP) metabolism, showing that genetic variants affecting APP and Aβ processing are associated not only with early-onset autosomal dominant Alzheimer’s disease but also with LOAD. Analyses of risk genes and pathways show enrichment for rare variants (P = 1.32 × 10−7), indicating that additional rare variants remain to be identified. We also identify important genetic correlations between LOAD and traits such as family history of dementia and education

Author Correction: Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing (Nature Genetics, (2019), 51, 3, (414-430), 10.1038/s41588-019-0358-2)

An amendment to this paper has been published and can be accessed via a link at the top of the paper

Genomics and drug profiling of fatal TCF3-HLF-positive acute lymphoblastic leukemia identifies recurrent mutation patterns and therapeutic options

TCF3-HLF-fusion positive acute lymphoblastic leukemia (ALL) is currently incurable. Employing an integrated approach, we uncovered distinct mutation, gene expression, and drug response profiles in TCF3-HLF-positive and treatment-responsive TCF3-PBX1-positive ALL. Recurrent intragenic deletions of PAX5 or VPREB1 were identified in constellation with TCF3-HLF. Moreover somatic mutations in the non-translocated allele of TCF3 and a reduction of PAX5 gene dosage in TCF3-HLF ALL suggest cooperation within a restricted genetic context. The enrichment for stem cell and myeloid features in the TCF3-HLF signature may reflect reprogramming by TCF3-HLF of a lymphoid-committed cell of origin towards a hybrid, drug-resistant hematopoietic state. Drug response profiling of matched patient-derived xenografts revealed a distinct profile for TCF3-HLF ALL with resistance to conventional chemotherapeutics, but sensitivity towards glucocorticoids, anthracyclines and agents in clinical development. Striking on-target sensitivity was achieved with the BCL2-specific inhibitor venetoclax (ABT-199). This integrated approach thus provides alternative treatment options for this deadly disease