CG-seq: a toolbox for automatic annotation of genomes by comparative analysis

CG-seq is a software pipeline to identify functional regions such as noncoding RNAs or protein coding genes in a genomic sequence by comparative analysis and multispecies comparison. It takes as input a genomic sequence to annotate and a set of other sequences coming from a variety of species to be compared against the user sequence. The pipeline includes several external software components to perform sequence analysis tasks as well as some new features that were especially developed for the purpose. CG-seq is distributed under the GPL licence. It is available both for command line interface usage or with a Graphical User Interface. It can be downloaded from http://bioinfo.lifl.fr/CGseq. A web version can also be runned from this same website for input data of limited length.CG-seq est une suite logicielle qui permet l'identification de régions fonctionnelles, telles que les ARN non-codants ou les gènes codants, dans une séquence génomique en utilisant le principe de la génomique comparative et de la comparaison entre espèces. Il prend en entrée une séquence à annoter, ainsi que d'autres séquences de référence issues de différentes espèces, et retourne en sortie une liste de régions candidates, avec leur annotation. Pour ce faire, CG-seq intègre plusieurs logiciels d'analyse de séquences existants, ainsi que de nouveaux modules qui ont été développés spécifiquement pour ce travail. CG-seq est distribué sous licence GPL, et téléchargeable à http://bioinfo.lifl.fr/CGseq. Il est disponible pour une utilisation en ligne de commande ou avec une interface graphique. Une version web est également proposée sur ce même site, qui permet de tester CG-seq sur des séquences de longueur raisonnable

miRNA-dependent target regulation: functional characterization of single-nucleotide polymorphisms identified in genome-wide association studies of Alzheimer’s disease

Other miRNA targeting sites identified by less stringent analysis near rs7143400-C/G, rs2847655-T/C, rs610923-C/A and rs9909-G/C. A summary of the genes, PolymiRTSs, effects of minor alleles, targeting miRNAs and miRNA expression alterations observed in AD (when available; refer to the cited references). The grayed miRNAs were also found in the stringent screening described in Fig. 2a in the main text. (XLS 23 kb

Common variants in Alzheimer's disease and risk stratification by polygenic risk scores

Genetic discoveries of Alzheimer's disease are the drivers of our understanding, and together with polygenetic risk stratification can contribute towards planning of feasible and efficient preventive and curative clinical trials. We first perform a large genetic association study by merging all available case-control datasets and by-proxy study results (discovery n = 409,435 and validation size n = 58,190). Here, we add six variants associated with Alzheimer's disease risk (near APP, CHRNE, PRKD3/NDUFAF7, PLCG2 and two exonic variants in the SHARPIN gene). Assessment of the polygenic risk score and stratifying by APOE reveal a 4 to 5.5 years difference in median age at onset of Alzheimer's disease patients in APOE ɛ4 carriers. Because of this study, the underlying mechanisms of APP can be studied to refine the amyloid cascade and the polygenic risk score provides a tool to select individuals at high risk of Alzheimer's disease

Genome-wide meta-analysis for Alzheimer's disease cerebrospinal fluid biomarkers

Amyloid-beta 42 (A?42) and phosphorylated tau (pTau) levels in cerebrospinal fluid (CSF) reflect core features of the pathogenesis of Alzheimer's disease (AD) more directly than clinical diagnosis. Initiated by the European Alzheimer & Dementia Biobank (EADB), the largest collaborative effort on genetics underlying CSF biomarkers was established, including 31 cohorts with a total of 13,116 individuals (discovery n = 8074; replication n = 5042 individuals). Besides the APOE locus, novel associations with two other well-established AD risk loci were observed; CR1 was shown a locus for A?42 and BIN1 for pTau. GMNC and C16orf95 were further identified as loci for pTau, of which the latter is novel. Clustering methods exploring the influence of all known AD risk loci on the CSF protein levels, revealed 4 biological categories suggesting multiple A?42 and pTau related biological pathways involved in the etiology of AD. In functional follow-up analyses, GMNC and C16orf95 both associated with lateral ventricular volume, implying an overlap in genetic etiology for tau levels and brain ventricular volume

Common variants in Alzheimer’s disease and risk stratification by polygenic risk scores

Genetic discoveries of Alzheimer’s disease are the drivers of our understanding, and together with polygenetic risk stratification can contribute towards planning of feasible and efficient preventive and curative clinical trials. We first perform a large genetic association study by merging all available case-control datasets and by-proxy study results (discovery n = 409,435 and validation size n = 58,190). Here, we add six variants associated with Alzheimer’s disease risk (near APP, CHRNE, PRKD3/NDUFAF7, PLCG2 and two exonic variants in the SHARPIN gene). Assessment of the polygenic risk score and stratifying by APOE reveal a 4 to 5.5 years difference in median age at onset of Alzheimer’s disease patients in APOE ɛ4 carriers. Because of this study, the underlying mechanisms of APP can be studied to refine the amyloid cascade and the polygenic risk score provides a tool to select individuals at high risk of Alzheimer’s disease.Additional co-authors: Joshua C. Bis, Rebecca Sims, Céline Bellenguez, Inés Quintela, Antonio González-Perez, Miguel Calero, Emilio Franco-Macías, Juan Macías, Rafael Blesa, Laura Cervera-Carles, Manuel Menéndez-González, Ana Frank-García, Jose Luís Royo, Fermin Moreno, Raquel Huerto Vilas, Miquel Baquero, Mónica Diez-Fairen, Carmen Lage, Sebastián García-Madrona, Pablo García-González, Emilio Alarcón-Martín, Sergi Valero, Oscar Sotolongo-Grau, Abbe Ullgren, Adam C. Naj, Afina W. Lemstra, Alba Benaque, Alba Pérez-Cordón, Alberto Benussi, Alberto Rábano, Alessandro Padovani, Alessio Squassina, Alexandre de Mendonça, Alfonso Arias Pastor, Almar A. L. Kok, Alun Meggy, Ana Belén Pastor, Ana Espinosa, Anaïs Corma-Gómez, Angel Martín Montes, Ángela Sanabria, Anita L. DeStefano, Anja Schneider, Annakaisa Haapasalo, Anne Kinhult Ståhlbom, Anne Tybjærg-Hansen, Annette M. Hartmann, Annika Spottke, Arturo Corbatón-Anchuelo, Arvid Rongve, Barbara Borroni, Beatrice Arosio, Benedetta Nacmias, Børge G. Nordestgaard, Brian W. Kunkle, Camille Charbonnier, Carla Abdelnour, Carlo Masullo, Carmen Martínez Rodríguez, Carmen Muñoz-Fernandez, Carole Dufouil, Caroline Graff, Catarina B. Ferreira, Caterina Chillotti, Chandra A. Reynolds, Chiara Fenoglio, Christine Van Broeckhoven, Christopher Clark, Claudia Pisanu, Claudia L. Satizabal, Clive Holmes, Dolores Buiza-Rueda, Dag Aarsland, Dan Rujescu, Daniel Alcolea, Daniela Galimberti, David Wallon, Davide Seripa, Edna Grünblatt, Efthimios Dardiotis, Emrah Düzel, Elio Scarpini, Elisa Conti, Elisa Rubino, Ellen Gelpi, Eloy Rodriguez-Rodriguez, Emmanuelle Duron, Eric Boerwinkle, Evelyn Ferri, Fabrizio Tagliavini, Fahri Küçükali, Florence Pasquier, Florentino Sanchez-Garcia, Francesca Mangialasche, Frank Jessen, Gaël Nicolas, Geir Selbæk, Gemma Ortega, Geneviève Chêne, Georgios Hadjigeorgiou, Giacomina Rossi, Gianfranco Spalletta, Giorgio Giaccone, Giulia Grande, Giuliano Binetti, Goran Papenberg, Harald Hampel, Henri Bailly, Henrik Zetterberg, Hilkka Soininen, Ida K. Karlsson, Ignacio Alvarez, Ildebrando Appollonio, Ina Giegling, Ingmar Skoog, Ingvild Saltvedt, Innocenzo Rainero, Irene Rosas Allende, Jakub Hort, Janine Diehl-Schmid, Jasper Van Dongen, Jean-Sebastien Vidal, Jenni Lehtisalo, Jens Wiltfang, Jesper Qvist Thomassen, Johannes Kornhuber, Jonathan L. Haines, Jonathan Vogelgsang, Juan A. Pineda, Juan Fortea, Julius Popp, Jürgen Deckert, Katharina Buerger, Kevin Morgan, Klaus Fließbach, Kristel Sleegers, Laura Molina-Porcel, Lena Kilander, Leonie Weinhold, Lindsay A. Farrer, Li-San Wang, Luca Kleineidam, Lucia Farotti, Lucilla Parnetti, Lucio Tremolizzo, Lucrezia Hausner, Luisa Benussi, Lutz Froelich, M. Arfan Ikram, M. Candida Deniz-Naranjo, Magda Tsolaki, Maitée Rosende-Roca, Malin Löwenmark, Marc Hulsman, Marco Spallazzi, Margaret A. Pericak-Vance, Margaret Esiri, María Bernal Sánchez-Arjona, Maria Carolina Dalmasso, María Teresa Martínez-Larrad, Marina Arcaro, Markus M. Nöthen, Marta Fernández-Fuertes, Martin Dichgans, Martin Ingelsson, Martin J. Herrmann, Martin Scherer, Martin Vyhnalek, Mary H. Kosmidis, Mary Yannakoulia, Matthias Schmid, Michael Ewers, Michael T. Heneka, Michael Wagner, Michela Scamosci, Miia Kivipelto, Mikko Hiltunen, Miren Zulaica, Montserrat Alegret, Myriam Fornage, Natalia Roberto, Natasja M. van Schoor, Nazib M. Seidu, Nerisa Banaj, Nicola J. Armstrong, Nikolaos Scarmeas, Norbert Scherbaum, Oliver Goldhardt, Oliver Hanon, Oliver Peters, Olivia Anna Skrobot, Olivier Quenez, Ondrej Lerch, Paola Bossù, Paolo Caffarra, Paolo Dionigi Rossi, Paraskevi Sakka, Per Hoffmann, Peter A. Holmans, Peter Fischer, Peter Riederer, Qiong Yang, Rachel Marshall, Rajesh N. Kalaria, Richard Mayeux, Rik Vandenberghe, Roberta Cecchetti, Roberta Ghidoni, Ruth Frikke-Schmidt, Sandro Sorbi, Sara Hägg, Sebastiaan Engelborghs, Seppo Helisalmi, Sigrid Botne Sando, Silke Kern, Silvana Archetti, Silvia Boschi, Silvia Fostinelli, Silvia Gil, Silvia Mendoza, Simon Mead, Simona Ciccone, Srdjan Djurovic, Stefanie Heilmann-Heimbach, Steffi Riedel-Heller, Teemu Kuulasmaa, Teodoro del Ser, Thibaud Lebouvier, Thomas Polak, Tiia Ngandu, Timo Grimmer, Valentina Bessi, Valentina Escott-Price, Vilmantas Giedraitis, Vincent Deramecourt, Wolfgang Maier, Xueqiu Jian, Yolande A. L. Pijnenburg, EADB contributors, The GR@ACE study group, DEGESCO consortium, IGAP (ADGC, CHARGE, EADI, GERAD), PGC-ALZ consortia, Patrick Gavin Kehoe, Guillermo Garcia-Ribas, Pascual Sánchez-Juan, Pau Pastor, Jordi Pérez-Tur, Gerard Piñol-Ripoll, Adolfo Lopez de Munain, Jose María García-Alberca, María J. Bullido, Victoria Álvarez, Alberto Lleó, Luis M. Real, Pablo Mir, Miguel Medina, Philip Scheltens, Henne Holstege, Marta Marquié, María Eugenia Sáez, Ángel Carracedo, Philippe Amouyel, Gerard D. Schellenberg, Julie Williams, Sudha Seshadri, Cornelia M. van Duijn, Karen A. Mather, Raquel Sánchez-Valle, Manuel Serrano-Ríos, Adelina Orellana, Lluís Tárraga, Kaj Blennow, Martijn Huisman, Ole A. Andreassen, Danielle Posthuma, Jordi Clarimón, Mercè Boada, Wiesje M. van der Flier, Alfredo Ramirez, Jean-Charles Lambert, Sven J. van der Lee & Agustín Rui

Multiancestry analysis of the HLA locus in Alzheimer's and Parkinson's diseases uncovers a shared adaptive immune response mediated by HLA-DRB1*04 subtypes

Across multiancestry groups, we analyzed Human Leukocyte Antigen (HLA) associations in over 176,000 individuals with Parkinson's disease (PD) and Alzheimer's disease (AD) versus controls. We demonstrate that the two diseases share the same protective association at the HLA locus. HLA-specific fine-mapping showed that hierarchical protective effects of HLA-DRB1*04 subtypes best accounted for the association, strongest with HLA-DRB1*04:04 and HLA-DRB1*04:07, and intermediary with HLA-DRB1*04:01 and HLA-DRB1*04:03. The same signal was associated with decreased neurofibrillary tangles in postmortem brains and was associated with reduced tau levels in cerebrospinal fluid and to a lower extent with increased Aβ42. Protective HLA-DRB1*04 subtypes strongly bound the aggregation-prone tau PHF6 sequence, however only when acetylated at a lysine (K311), a common posttranslational modification central to tau aggregation. An HLA-DRB1*04-mediated adaptive immune response decreases PD and AD risks, potentially by acting against tau, offering the possibility of therapeutic avenues

Genetic Associations Between Modifiable Risk Factors and Alzheimer Disease

Importance: An estimated 40% of dementia is potentially preventable by modifying 12 risk factors throughout the life course. However, robust evidence for most of these risk factors is lacking. Effective interventions should target risk factors in the causal pathway to dementia. Objective: To comprehensively disentangle potentially causal aspects of modifiable risk factors for Alzheimer disease (AD) to inspire new drug targeting and improved prevention. Design, setting, and participants: This genetic association study was conducted using 2-sample univariable and multivariable mendelian randomization. Independent genetic variants associated with modifiable risk factors were selected as instrumental variables from genomic consortia. Outcome data for AD were obtained from the European Alzheimer & Dementia Biobank (EADB), generated on August 31, 2021. Main analyses were conducted using the EADB clinically diagnosed end point data. All analyses were performed between April 12 and October 27, 2022. Exposures: Genetically determined modifiable risk factors. Main outcomes and measures: Odds ratios (ORs) and 95% CIs for AD were calculated per 1-unit change of genetically determined risk factors. Results: The EADB-diagnosed cohort included 39 106 participants with clinically diagnosed AD and 401 577 control participants without AD. The mean age ranged from 72 to 83 years for participants with AD and 51 to 80 years for control participants. Among participants with AD, 54% to 75% were female, and among control participants, 48% to 60% were female. Genetically determined high-density lipoprotein (HDL) cholesterol concentrations were associated with increased odds of AD (OR per 1-SD increase, 1.10 [95% CI, 1.05-1.16]). Genetically determined high systolic blood pressure was associated with increased risk of AD after adjusting for diastolic blood pressure (OR per 10-mm Hg increase, 1.22 [95% CI, 1.02-1.46]). In a second analysis to minimize bias due to sample overlap, the entire UK Biobank was excluded from the EADB consortium; odds for AD were similar for HDL cholesterol (OR per 1-SD unit increase, 1.08 [95% CI, 1.02-1.15]) and systolic blood pressure after adjusting for diastolic blood pressure (OR per 10-mm Hg increase, 1.23 [95% CI, 1.01-1.50]). Conclusions and relevance: This genetic association study found novel genetic associations between high HDL cholesterol concentrations and high systolic blood pressure with higher risk of AD. These findings may inspire new drug targeting and improved prevention implementation

Common variants in Alzheimer’s disease and risk stratification by polygenic risk scores

Genetic discoveries of Alzheimer’s disease are the drivers of our understanding, and together with polygenetic risk stratification can contribute towards planning of feasible and efficient preventive and curative clinical trials. We first perform a large genetic association study by merging all available case-control datasets and by-proxy study results (discovery n = 409,435 and validation size n = 58,190). Here, we add six variants associated with Alzheimer’s disease risk (near APP, CHRNE, PRKD3/NDUFAF7, PLCG2 and two exonic variants in the SHARPIN gene). Assessment of the polygenic risk score and stratifying by APOE reveal a 4 to 5.5 years difference in median age at onset of Alzheimer’s disease patients in APOE ɛ4 carriers. Because of this study, the underlying mechanisms of APP can be studied to refine the amyloid cascade and the polygenic risk score provides a tool to select individuals at high risk of Alzheimer’s disease.Fil: Dalmasso, Maria Carolina. Gobierno de la Provincia de la Pampa. Ministerio Publico. Laboratorio de Genetica Forense.; Argentina. Universitat zu Köln; Alemania. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Confluencia; ArgentinaFil: de Rojas, Itziar. Universitat Internacional de Catalunya; España. Instituto de Salud Carlos Iii (isciii); EspañaFil: Moreno Grau, Sonia. Universitat Internacional de Catalunya; España. Instituto de Salud Carlos Iii (isciii); EspañaFil: Tesi, Niccolo. Vrije Universiteit Amsterdam; Países Bajos. Delft University of Technology; Países BajosFil: Grenier Boley, Benjamin. Universite Lille; FranciaFil: Andrade, Victor. Universitat zu Köln; Alemania. Universitat Bonn; AlemaniaFil: Pedersen, Nancy L.. Karolinska Huddinge Hospital. Karolinska Institutet; SueciaFil: Stringa, Najada. University of Amsterdam; Países BajosFil: Zettergren, Anna. University of Gothenburg; SueciaFil: Hernández, Isabel. Universitat Internacional de Catalunya; España. Instituto de Salud Carlos Iii (isciii); EspañaFil: Montrreal, Laura. Universitat Internacional de Catalunya; EspañaFil: Antúnez, Carmen. Hospital Clínico Universitario Virgen de la Arrixaca; EspañaFil: Antonell, Anna. Universidad de Barcelona; EspañaFil: Tankard, Rick M.. Murdoch University; AustraliaFil: Bis, Joshua C.. University of Washington; Estados UnidosFil: Sims, Rebecca. Cardiff University; Reino UnidoFil: Bellenguez, Céline. Universite Lille; FranciaFil: Quintela, Inés. Universidad de Santiago de Compostela; EspañaFil: González Perez, Antonio. Centro Andaluz de Estudios Bioinformáticos; EspañaFil: Calero, Miguel. Instituto de Salud Carlos Iii (isciii); España. Fundación Reina Sofia; EspañaFil: Franco Macías, Emilio. Universidad de Sevilla; EspañaFil: Macías, Juan. Hospital Universitario de Valme; EspañaFil: Blesa, Rafael. Instituto de Salud Carlos Iii (isciii); España. Universitat Autònoma de Barcelona; EspañaFil: Cervera Carles, Laura. Instituto de Salud Carlos Iii (isciii); España. Universitat Autònoma de Barcelona; EspañaFil: Menéndez González, Manuel. Universidad de Oviedo; EspañaFil: Frank García, Ana. Instituto de Salud Carlos Iii (isciii); España. Universidad Autónoma de Madrid; España. Instituto de Investigacion del Hospital de la Paz.; España. Hospital Universitario La Paz; EspañaFil: Royo, Jose Luís. Universidad de Málaga; EspañaFil: Moreno, Fermin. Instituto de Salud Carlos Iii (isciii); España. Hospital Universitario Donostia; España. Instituto Biodonostia; EspañaFil: Huerto Vilas, Raquel. Hospital Universitari Santa Maria de Lleida; España. Institut de Recerca Biomedica de Lleida; EspañaFil: Baquero, Miquel. Hospital Universitari i Politècnic La Fe; Españ

Polygenic resilience scores capture protective genetic effects for Alzheimer’s disease

Polygenic risk scores (PRSs) can boost risk prediction in late-onset Alzheimer’s disease (LOAD) beyond apolipoprotein E (APOE) but have not been leveraged to identify genetic resilience factors. Here, we sought to identify resilience-conferring common genetic variants in (1) unaffected individuals having high PRSs for LOAD, and (2) unaffected APOE-ε4 carriers also having high PRSs for LOAD. We used genome-wide association study (GWAS) to contrast “resilient” unaffected individuals at the highest genetic risk for LOAD with LOAD cases at comparable risk. From GWAS results, we constructed polygenic resilience scores to aggregate the addictive contributions of risk-orthogonal common variants that promote resilience to LOAD. Replication of resilience scores was undertaken in eight independent studies. We successfully replicated two polygenic resilience scores that reduce genetic risk penetrance for LOAD. We also showed that polygenic resilience scores positively correlate with polygenic risk scores in unaffected individuals, perhaps aiding in staving off disease. Our findings align with the hypothesis that a combination of risk-independent common variants mediates resilience to LOAD by moderating genetic disease risk

Multiancestry analysis of the HLA locus in Alzheimer's and Parkinson's diseases uncovers a shared adaptive immune response mediated by <i>HLA-DRB1*04</i> subtypes

Across multiancestry groups, we analyzed Human Leukocyte Antigen (HLA) associations in over 176,000 individuals with Parkinson's disease (PD) and Alzheimer's disease (AD) versus controls. We demonstrate that the two diseases share the same protective association at the HLA locus. HLA-specific fine-mapping showed that hierarchical protective effects of HLA-DRB1*04 subtypes best accounted for the association, strongest with HLA-DRB1*04:04 and HLA-DRB1*04:07, and intermediary with HLA-DRB1*04:01 and HLA-DRB1*04:03. The same signal was associated with decreased neurofibrillary tangles in postmortem brains and was associated with reduced tau levels in cerebrospinal fluid and to a lower extent with increased Aβ42. Protective HLA-DRB1*04 subtypes strongly bound the aggregation-prone tau PHF6 sequence, however only when acetylated at a lysine (K311), a common posttranslational modification central to tau aggregation. An HLA-DRB1*04-mediated adaptive immune response decreases PD and AD risks, potentially by acting against tau, offering the possibility of therapeutic avenues.</p