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See detailEditorial: Digital Innovation and Data-Driven Research in Neurodegenerative Diseases
Gu, Wei UL; Rong, Panying; Hofmann-Apitius, Martin et al

in Frontiers in Neurology (2022), 13

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See detailInterrogating the effect of enzyme kinetics on metabolism using differentiable constraint-based models
Wilken, St Elmo; Besançon, Mathieu; Kratochvil, Miroslav UL et al

in Metabolic Engineering (2022)

Metabolic models are typically characterized by a large number of parameters. Traditionally, metabolic control analysis is applied to differential equation-based models to investigate the sensitivity of ... [more ▼]

Metabolic models are typically characterized by a large number of parameters. Traditionally, metabolic control analysis is applied to differential equation-based models to investigate the sensitivity of predictions to parameters. A corresponding theory for constraint-based models is lacking, due to their formulation as optimization problems. Here, we show that optimal solutions of optimization problems can be efficiently differentiated using constrained optimization duality and implicit differentiation. We use this to calculate the sensitivities of predicted reaction fluxes and enzyme concentrations to turnover numbers in an enzyme-constrained metabolic model of Escherichia coli. The sensitivities quantitatively identify rate limiting enzymes and are mathematically precise, unlike current finite difference based approaches used for sensitivity analysis. Further, efficient differentiation of constraint-based models unlocks the ability to use gradient information for parameter estimation. We demonstrate this by improving, genome-wide, the state-of-the-art turnover number estimates for E. coli. Finally, we show that this technique can be generalized to arbitrarily complex models. By differentiating the optimal solution of a model incorporating both thermodynamic and kinetic rate equations, the effect of metabolite concentrations on biomass growth can be elucidated. We benchmark these metabolite sensitivities against a large experimental gene knockdown study, and find good alignment between the predicted sensitivities and in vivo metabolome changes. In sum, we demonstrate several applications of differentiating optimal solutions of constraint-based metabolic models, and show how it connects to classic metabolic control analysis. [less ▲]

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See detailSelection of data sets for FAIRification in drug discovery and development: Which, why, and how?
Alharbi, Ebtisam; Gadiya, Yojana; Henderson, David et al

in Drug Discovery Today (2022)

Despite the intuitive value of adopting the Findable, Accessible, Interoperable, and Reusable (FAIR) principles in both academic and industrial sectors, challenges exist in resourcing, balancing long ... [more ▼]

Despite the intuitive value of adopting the Findable, Accessible, Interoperable, and Reusable (FAIR) principles in both academic and industrial sectors, challenges exist in resourcing, balancing long- versus short-term priorities, and achieving technical implementation. This situation is exacerbated by the unclear mechanisms by which costs and benefits can be assessed when decisions on FAIR are made. Scientific and research and development (R&D) leadership need reliable evidence of the potential benefits and information on effective implementation mechanisms and remediating strategies. In this article, we describe procedures for cost–benefit evaluation, and identify best-practice approaches to support the decision-making process involved in FAIR implementation. [less ▲]

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See detailPredictProtein - Predicting Protein Structure and Function for 29 Years
Bernhofer, Michael; Dallago, Christian; Karl, Tim et al

in Nucleic Acids Research (2021)

Since 1992 PredictProtein (https://predictprotein.org) is a one-stop online resource for protein sequence analysis with its main site hosted at the Luxembourg Centre for Systems Biomedicine (LCSB) and ... [more ▼]

Since 1992 PredictProtein (https://predictprotein.org) is a one-stop online resource for protein sequence analysis with its main site hosted at the Luxembourg Centre for Systems Biomedicine (LCSB) and queried monthly by over 3,000 users in 2020. PredictProtein was the first Internet server for protein predictions. It pioneered combining evolutionary information and machine learning. Given a protein sequence as input, the server outputs multiple sequence alignments, predictions of protein structure in 1D and 2D (secondary structure, solvent accessibility, transmembrane segments, disordered regions, protein flexibility, and disulfide bridges) and predictions of protein function (functional effects of sequence variation or point mutations, Gene Ontology (GO) terms, subcellular localization, and protein-, RNA-, and DNA binding). PredictProtein's infrastructure has moved to the LCSB increasing throughput; the use of MMseqs2 sequence search reduced runtime five-fold (apparently without lowering performance of prediction methods); user interface elements improved usability, and new prediction methods were added. PredictProtein recently included predictions from deep learning embeddings (GO and secondary structure) and a method for the prediction of proteins and residues binding DNA, RNA, or other proteins. PredictProtein.org aspires to provide reliable predictions to computational and experimental biologists alike. All scripts and methods are freely available for offline execution in high-throughput settings. [less ▲]

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See detailCOBREXA.jl: constraint-based reconstruction and exascale analysis
Kratochvil, Miroslav UL; Heirendt, Laurent UL; Wilken, St Elmo et al

in Bioinformatics (2021)

COBREXA.jl is a Julia package for scalable, high-performance constraint-based reconstruction and analysis of very large-scale biological models. Its primary purpose is to facilitate the integration of ... [more ▼]

COBREXA.jl is a Julia package for scalable, high-performance constraint-based reconstruction and analysis of very large-scale biological models. Its primary purpose is to facilitate the integration of modern high performance computing environments with the processing and analysis of large-scale metabolic models of challenging complexity. We report the architecture of the package, and demonstrate how the design promotes analysis scalability on several use-cases with multi-organism community models.https://doi.org/10.17881/ZKCR-BT30.Supplementary data are available at Bioinformatics online. [less ▲]

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See detailCardiovascular RNA markers and artificial intelligence may improve COVID-19 outcome: a position paper from the EU-CardioRNA COST Action CA17129
Badimon, Lina; Robinson, Emma L.; Jusic, Amela et al

in Cardiovascular Research (2021)

The coronavirus disease 2019 (COVID-19) pandemic has been as unprecedented as unexpected, affecting more than 105 million people worldwide as of 8 February 2020 and causing more than 2.3 million deaths ... [more ▼]

The coronavirus disease 2019 (COVID-19) pandemic has been as unprecedented as unexpected, affecting more than 105 million people worldwide as of 8 February 2020 and causing more than 2.3 million deaths according to the World Health Organization (WHO). Not only affecting the lungs but also provoking acute respiratory distress, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is able to infect multiple cell types including cardiac and vascular cells. Hence a significant proportion of infected patients develop cardiac events, such as arrhythmias and heart failure. Patients with cardiovascular comorbidities are at highest risk of cardiac death. To face the pandemic and limit its burden, health authorities have launched several fast-track calls for research projects aiming to develop rapid strategies to combat the disease, as well as longer-term projects to prepare for the future. Biomarkers have the possibility to aid in clinical decision-making and tailoring healthcare in order to improve patient quality of life. The biomarker potential of circulating RNAs has been recognized in several disease conditions, including cardiovascular disease. RNA biomarkers may be useful in the current COVID-19 situation. The discovery, validation, and marketing of novel biomarkers, including RNA biomarkers, require multi-centre studies by large and interdisciplinary collaborative networks, involving both the academia and the industry. Here, members of the EU-CardioRNA COST Action CA17129 summarize the current knowledge about the strain that COVID-19 places on the cardiovascular system and discuss how RNA biomarkers can aid to limit this burden. They present the benefits and challenges of the discovery of novel RNA biomarkers, the need for networking efforts, and the added value of artificial intelligence to achieve reliable advances. [less ▲]

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See detailRoad to effective data curation for translational research
Gu, Wei UL; Hasan, Samiul; Rocca-Serra, Philippe et al

in Drug Discovery Today (2020)

Translational research today is data-intensive and requires multi-stakeholder collaborations to generate and pool data together for integrated analysis. This leads to the challenge of harmonization of ... [more ▼]

Translational research today is data-intensive and requires multi-stakeholder collaborations to generate and pool data together for integrated analysis. This leads to the challenge of harmonization of data from different sources with different formats and standards, which is often overlooked during project planning and thus becomes a bottleneck of the research progress. We report on our experience and lessons learnt about data curation for translational research garnered over the course of the eTRIKS program (https://www.etriks.org), a unique, 5-year, cross-organizational, cross-cultural collaboration project funded by the Innovative Medicines Initiative of the EU. Here, we discuss the obstacles and suggest what steps are needed for effective data curation in translational research, especially for projects involving multiple organizations from academia and industry. [less ▲]

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See detailA rare loss-of function variant of ADAM17 is associated with late-onset familial Alzheimer disease
Hartl, Daniela; May, Patrick UL; Gu, Wei UL et al

in Molecular Psychiatry (2020), 25(3), 629-639

Common variants of about 20 genes contributing to AD risk have so far been identified through genome-wide association studies (GWAS). However, there is still a large proportion of heritability that might ... [more ▼]

Common variants of about 20 genes contributing to AD risk have so far been identified through genome-wide association studies (GWAS). However, there is still a large proportion of heritability that might be explained by rare but functionally important variants. One of the so far identified genes with rare AD causing variants is ADAM10. Using whole-genome sequencing we now identified a single rare nonsynonymous variant (SNV) rs142946965 [p.R215I] in ADAM17 co-segregating with an autosomal-dominant pattern of late-onset AD in one family. Subsequent genotyping and analysis of available whole-exome sequencing data of additional case/control samples from Germany, the UK and the USA identified five variant carriers among AD patients only. The mutation inhibits pro-protein cleavage and the formation of the active enzyme, thus leading to loss-of-function of ADAM17 α-secretase. Further, we identified a strong negative correlation between ADAM17 and APP gene expression in human brain and present in vitro evidence that ADAM17 negatively controls the expression of APP. As a consequence, p.R215I mutation of ADAM17 leads to elevated Aß formation in vitro. Together our data supports a causative association of the identified ADAM17 variant in the pathogenesis of AD. [less ▲]

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See detailData and knowledge management in translational research: implementation of the eTRIKS platform for the IMI OncoTrack consortium
Gu, Wei UL; Yildirimman, Reha; Van der Stuyft, Emmanuel et al

in BMC Bioinformatics (2019), 20(1), 164

For large international research consortia, such as those funded by the European Union’s Horizon 2020 programme or the Innovative Medicines Initiative, good data coordination practices and tools are ... [more ▼]

For large international research consortia, such as those funded by the European Union’s Horizon 2020 programme or the Innovative Medicines Initiative, good data coordination practices and tools are essential for the successful collection, organization and analysis of the resulting data. Research consortia are attempting ever more ambitious science to better understand disease, by leveraging technologies such as whole genome sequencing, proteomics, patient-derived biological models and computer-based systems biology simulations. [less ▲]

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See detailGenetic meta-analysis of diagnosed Alzheimer's disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing
Kunkle, Brian W.; Grenier-Boley, Benjamin; Sims, Rebecca et al

in Nature Genetics (2019), 51(3), 414

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 ... [more ▼]

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 A$\beta$ 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. [less ▲]

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See detailFractalis: A scalable open-source service for platform-independent interactive visual analysis of biomedical data
Herzinger, Sascha UL; Groues, Valentin UL; Gu, Wei UL et al

in GigaScience (2018)

Background: Translational research platforms share the aim to promote a deeper understanding of stored data by providing visualization and analysis tools for data exploration and hypothesis generation ... [more ▼]

Background: Translational research platforms share the aim to promote a deeper understanding of stored data by providing visualization and analysis tools for data exploration and hypothesis generation. However, such tools are usually platform-bound and are not easily reusable by other systems. Furthermore, they rarely address access restriction issues when direct data transfer is not permitted. In this article we present an analytical service that works in tandem with a visualization library to address these problems. Findings: Using a combination of existing technologies and a platform-specific data abstraction layer we developed a service that is capable of providing existing web-based data warehouses and repositories with platform-independent visual analytical capabilities. The design of this service also allows for federated data analysis by eliminating the need to move the data directly to the researcher. Instead, all operations are based on statistics and interactive charts without direct access to the dataset. Conclusion: The software presented in this article has a potential to help translational researchers achieve a better understanding of a given dataset and quickly generate new hypothesis. Furthermore, it provides a framework that can be used to share and reuse explorative analysis tools within the community. [less ▲]

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See detailPresenting and Sharing Clinical Data using the eTRIKS Standards Master Tree for tranSMART
Barbosa-Silva, Adriano; Bratfalean, Dorina; Gu, Wei UL et al

in Bioinformatics (2018)

Motivation Standardization and semantic alignment have been considered one of the major challenges for data integration in clinical research. The inclusion of the CDISC SDTM clinical data standard into ... [more ▼]

Motivation Standardization and semantic alignment have been considered one of the major challenges for data integration in clinical research. The inclusion of the CDISC SDTM clinical data standard into the tranSMART i2b2 via a guiding master ontology tree positively impacts and supports the efficacy of data sharing, visualization and exploration across datasets. Results We present here a schema for the organization of SDTM variables into the tranSMART i2b2 tree along with a script and test dataset to exemplify the mapping strategy. The eTRIKS master tree concept is demonstrated by making use of fictitious data generated for four patients, including 16 SDTM clinical domains. We describe how the usage of correct visit names and data labels can help to integrate multiple readouts per patient and avoid ETL crashes when running a tranSMART loading routine. Availability The eTRIKS Master Tree package and test datasets are publicly available at https://doi.org/10.5281/zenodo.1009098 and a functional demo installation at https://public.etriks.org/transmart/datasetExplorer/ under eTRIKS - Master Tree branch, where the discussed examples can be visualized. [less ▲]

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See detailSmartR: An open-source platform for interactive visual analytics for translational research data.
Herzinger, Sascha UL; Gu, Wei UL; Satagopam, Venkata UL et al

in Bioinformatics (2017)

In translational research, efficient knowledge exchange between the different fields of expertise is crucial. An open platform that is capable of storing a multitude of data types such as clinical, pre ... [more ▼]

In translational research, efficient knowledge exchange between the different fields of expertise is crucial. An open platform that is capable of storing a multitude of data types such as clinical, pre-clinical, or OMICS data combined with strong visual analytical capabilities will significantly accelerate the scientific progress by making data more accessible and hypothesis generation easier. The open data warehouse tranSMART is capable of storing a variety of data types and has a growing user community including both academic institutions and pharmaceutical companies. tranSMART, however, currently lacks interactive and dynamic visual analytics and does not permit any post-processing interaction or exploration. For this reason, we developed SmartR , a plugin for tranSMART, that equips the platform not only with several dynamic visual analytical workflows, but also provides its own framework for the addition of new custom workflows. Modern web technologies such as D3.js or AngularJS were used to build a set of standard visualizations that were heavily improved with dynamic elements. Contact: reinhard.schneider@uni.lu. Supplementary information: Supplementary data are available at Bioinformatics online. Availability: : The source code is licensed under the Apache 2.0 License and is freely available on GitHub: https://github.com/transmart/SmartR. [less ▲]

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See detailIDENTIFICATION OF A RARE GENE VARIANT THAT IS ASSOCIATED WITH FAMILIAL ALZHEIMER DISEASE AND REGULATES APP EXPRESSION
Hartl, Daniela; May, Patrick UL; Gu, Wei UL et al

in Alzheimer's and Dementia: the Journal of the Alzheimer's Association (2017), 13(7, Supplement), 648

Background Genetic mutations leading to familial forms of Alzheimer disease (AD) have so far been reported for a few genes including APP, PSEN1 and PSEN2, UNC5C, PLD3, ABCA7, TTC3, and possibly ADAM10 ... [more ▼]

Background Genetic mutations leading to familial forms of Alzheimer disease (AD) have so far been reported for a few genes including APP, PSEN1 and PSEN2, UNC5C, PLD3, ABCA7, TTC3, and possibly ADAM10. With the advent of whole exome and whole genome sequencing approaches new genes and mutations are likely to be identified. Methods We analyzed the genetic cause of AD in a large multiplex family with an autosomal-dominant pattern of inheritance with LOAD. The family lacked pathogenic mutations of known AD genes. We performed whole-genome sequencing (WGS) in six family members (two affected and four unaffected) and prioritized rare, potential damaging, variants that segregated with disease. Variants were further characterized by subsequent molecular analyzes in human brain and cell culture models. Results We identified a single rare nonsynonymous variant co-segregating with AD. The mutation inhibits pro-protein cleavage and the formation of the active enzyme, thus leading to a loss-of-function of the gene. We further found a strong negative correlation between the identified gene and APP gene expression in human brain and in cells over-expressing the gene. The negative regulation of APP expression was only observed for the wt gene, but not for mutated forms, thus causing beside the loss of enzyme function a decoupling of both APPexpression and subsequent beta-amyloid formation. The identity of the gene will be presented on the conference. Conclusions This novel pathway strongly supports a causative association of the identified gene with the pathogenesis of AD. [less ▲]

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See detailRare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer's disease
Sims, Rebecca; van der Lee, Sven J.; Naj, Adam C. et al

in Nature Genetics (2017), 49

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See detailThe miRNome of Alzheimer's disease: consistent downregulation of the miR-132/212 cluster
Pichler, Sabrina; Gu, Wei UL; Hartl, Daniela et al

in Neurobiology of Aging (2016), 50

MicroRNAs (miRNAs) are small noncoding RNA molecules, with essential functions in RNA silencing and post-transcriptional regulation of gene expression. miRNAs appear to regulate the development and ... [more ▼]

MicroRNAs (miRNAs) are small noncoding RNA molecules, with essential functions in RNA silencing and post-transcriptional regulation of gene expression. miRNAs appear to regulate the development and function of the nervous system. Alterations of miRNA expression have been associated with Alzheimer's disease (AD). To characterize the AD miRNA signature, we examined genome-wide miRNA and mRNA expression patterns in the temporal cortex of AD and control samples. We validated our miRNA results by semiquantitative real-time polymerase chain reaction (PCR) in independent prefrontal cortex. Furthermore, we separated gray and white matter brain sections to identify the cellular origin of the altered miRNA expression. We observed genome-wide downregulation of hsa-miR-132-3p and hsa-miR-212-3p in AD with a stronger decrease in gray matter AD samples. We further identified 10 differently expressed transcripts achieving genome-wide levels of significance. Significantly deregulated miRNAs and mRNAs were correlated and examined for potential binding sites (in silico). This miRNome-wide study in AD provides supportive evidence and corroborates an important contribution of miR-132/212 and corresponding target mRNAs to the pathogenesis of AD. [less ▲]

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See detailIntegration and Visualization of Translational Medicine Data for Better Understanding of Human Diseases.
Satagopam, Venkata UL; Gu, Wei UL; Eifes, Serge et al

in Big data (2016), 4(2), 97-108

Translational medicine is a domain turning results of basic life science research into new tools and methods in a clinical environment, for example, as new diagnostics or therapies. Nowadays, the process ... [more ▼]

Translational medicine is a domain turning results of basic life science research into new tools and methods in a clinical environment, for example, as new diagnostics or therapies. Nowadays, the process of translation is supported by large amounts of heterogeneous data ranging from medical data to a whole range of -omics data. It is not only a great opportunity but also a great challenge, as translational medicine big data is difficult to integrate and analyze, and requires the involvement of biomedical experts for the data processing. We show here that visualization and interoperable workflows, combining multiple complex steps, can address at least parts of the challenge. In this article, we present an integrated workflow for exploring, analysis, and interpretation of translational medicine data in the context of human health. Three Web services-tranSMART, a Galaxy Server, and a MINERVA platform-are combined into one big data pipeline. Native visualization capabilities enable the biomedical experts to get a comprehensive overview and control over separate steps of the workflow. The capabilities of tranSMART enable a flexible filtering of multidimensional integrated data sets to create subsets suitable for downstream processing. A Galaxy Server offers visually aided construction of analytical pipelines, with the use of existing or custom components. A MINERVA platform supports the exploration of health and disease-related mechanisms in a contextualized analytical visualization system. We demonstrate the utility of our workflow by illustrating its subsequent steps using an existing data set, for which we propose a filtering scheme, an analytical pipeline, and a corresponding visualization of analytical results. The workflow is available as a sandbox environment, where readers can work with the described setup themselves. Overall, our work shows how visualization and interfacing of big data processing services facilitate exploration, analysis, and interpretation of translational medicine data. [less ▲]

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See detailAmyloid-β Protein Precursor Cleavage Products in Postmortem Ventricular Cerebrospinal Fluid of Alzheimer’s Disease Patients
Hartl, Daniela; Gu, Wei UL; Mayhaus, Manuel et al

in Journal of Alzheimer's Disease (2015), 47(2), 365-372

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See detailReproducible Research Results R3
Trefois, Christophe UL; Jarosz, Yohan UL; Gu, Wei UL et al

Poster (2014, December)

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See detailGene-wide analysis detects two new susceptibility genes for Alzheimer's disease.
Escott-Price, Valentina; Bellenguez, Celine; Wang, Li-San et al

in PloS one (2014), 9(6), 94661

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 ... [more ▼]

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 = 1.4x10-6) and 14 (IGHV1-67 p = 7.9x10-8) 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. [less ▲]

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