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Articles by Najaf Amin
Proteomic signatures of smoking and their associations with risk of incident diseases and mortality in diverse populations - Nature Communications
Abstract Smoking is the most important behavioural determinant of morbidity and mortality. Using machine learning on plasma levels of 2,917 proteins in the UK Biobank (n = 43,914), we develop a proteomic Smoking Index (pSIN) comprising 51 proteins that accurately distinguish current from never smokers (AUC = 0.95; 95% CI 0.94–0.95). Validation in the China Kadoorie Biobank (n = 3,977) shows similar accuracy (AUC = 0.91; 95% CI 0.89–0.92).
Machine learning in Alzheimer’s disease genetics - Nature Communications
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Abstract Traditional statistical approaches have advanced our understanding of the genetics of complex diseases, yet are limited to linear additive models. Here we applied machine learning (ML) to genome-wide data from 41,686 individuals in the largest European consortium on Alzheimer’s disease (AD) to investigate the effectiveness of various ML algorithms in replicating known findings, discovering novel loci, and predicting individuals at risk.
By Matthew Bracher-Smith, Céline Bellenguez, Peter A Holmans, Betty M. Tijms, Marc Hulsman, Itziar de Rojas, Rafael Campos-Martin, Fahri Küçükali, Oliver Peters, Anja Schneider, Martin Dichgans, Dan Rujescu, Norbert Scherbaum, Steffi G. Riedel-Heller, Lucrezia Hausner, Laura Molina-Porcel, Jens Wiltfang, Stefanie Heilmann-Heimbach, Susanne Moebus, Oriol Dols-Icardo, Fermín Moreno, Jordi Pérez-Tur, Maria Bullido, Victoria Alvarez, Mercè Boada, Pablo García-González, Raquel Puerta, Pablo Mir, Luis M. Real, Jose María García-Alberca, Eloy Rodriguez-Rodriguez, Hilkka Soininen, Sami Heikkinen, Jakub Hort, Jesper Qvist Thomassen, Henne Holstege, Philip Scheltens, Vilmantas Giedraitis, Julie Williams, Philippe Amouyel, Anne Boland, Gaël Nicolas, Carole Dufouil, Florence Pasquier, Stéphanie Debette, Edna Grünblatt, Roberta Ghidoni, Daniela Galimberti, Beatrice Arosio, Patrizia Mecocci, Vincenzo Solfrizzi, Lucilla Parnetti, Alessio Squassina, Michael Wagner, Benedetta Nacmias, Fabrizio Piras, Giacomina Rossi, Patrick Kehoe, Kristel Sleegers, Martin Ingelsson, Rebecca Sims, Ole A. Andreassen, Agustín Ruiz, Alfredo M. Ramirez, Alfredo Ramírez, Ruth Frikke-Schmidt, Najaf Amin, Gennady V Roshchupkin, Jean-Charles Lambert, Kristel Van Steen, Cornelia van Duijn, Tsolaki Magda
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Nature
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Machine learning in Alzheimer’s disease genetics
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Scientists just discovered a secret code hidden in your DNA What scientists once dismissed as “junk” DNA may actually be some of the most powerful code in our genome. A new international study reveals that …
By Matthew Bracher-Smith, Federico Melograna, Brittany S. Ulm, Céline Bellenguez, Benjamin Grenier-Boley, Diane Duroux, Peter A Holmans, Betty M. Tijms, Marc Hulsman, Itziar de Rojas, Rafael Campos-Martin, Fahri Küçükali, Oliver Peters, Anja Schneider, Martin Dichgans, Dan Rujescu, Norbert Scherbaum, Jürgen Deckert, Steffi G. Riedel-Heller, Lucrezia Hausner, Laura Molina-Porcel, Emrah Düzel, Timo Grimmer, Jens Wiltfang, Stefanie Heilmann-Heimbach, Susanne Moebus, Thomas Tegos, Nikolaos Scarmeas, Oriol Dols-Icardo, Fermín Moreno, Jordi Pérez-Tur, Maria Bullido, Raquel Sánchez-Valle, Victoria Alvarez, Mercè Boada, Pablo García-González, Raquel Puerta, Pablo Mir, Luis M. Real, Gerard Piñol-Ripoll, Jose María García-Alberca, Eloy Rodriguez-Rodriguez, Hilkka Soininen, Sami Heikkinen, Alexandre de Mendonça, Latchezar Traykov, Jakub Hort, Martin Vyhnálek, Nicolai Sandau, Jesper Qvist Thomassen, Henne Holstege, Inez H.G.B. Ramakers, Frans R Verhey, Philip Scheltens, Caroline Graff, Goran Papenberg, Vilmantas Giedraitis, Julie Williams, Philippe Amouyel, Anne Boland, Jean-Francois Deleuze, Gaël Nicolas, Carole Dufouil, Florence Pasquier, Olivier Hanon, Stéphanie Debette, Edna Grünblatt, Julius Popp, Roberta Ghidoni, Daniela Galimberti, Beatrice Arosio, Patrizia Mecocci, Vincenzo Solfrizzi, Lucilla Parnetti, Alessio Squassina, Lucio Tremolizzo, Barbara Borroni, Michael Wagner, Benedetta Nacmias, Davide Seripa, Innocenzo Rainero, Antonio Daniele, Fabrizio Piras, Giacomina Rossi, Frank Jessen, Patrick Kehoe, Tsolaki Magda, Pascual Sanchez-Juan, Kristel Sleegers, Martin Ingelsson, Mikko Hiltunen, Rebecca Sims, Ole A. Andreassen, Agustín Ruiz, Alfredo M. Ramirez, Alfredo Ramírez, Ruth Frikke-Schmidt, Najaf Amin, Gennady V Roshchupkin, Jean-Charles Lambert, Kristel Van Steen, Cornelia van Duijn, Valentina Escott-Price, Alejo J. Nevado, Atahualpa Castillo, Shima Mehrabian, John van Swieten, Marco Spallazzi, Carlo Masullo
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Integrating the environmental and genetic architectures of aging and mortality - Nature Medicine
Abstract Both environmental exposures and genetics are known to play important roles in shaping human aging. Here we aimed to quantify the relative contributions of environment (referred to as the exposome) and genetics to aging and premature mortality.
X-chromosome-wide association study for Alzheimer's disease
The authors have declared no competing interest. EADB: This study was supported by grants from the Fondation pour la Recherche sur Alzheimer (convention 2022-A-01 and cluster grant), and the JPco-fuND-2 'Multinational research projects on Personalized Medicine for Neurodegenerative Diseases' PREADAPT project (ANR-19-JPW2-0004).
Interplay of Metabolome and Gut Microbiome in Individuals With Major Depressive Disorder vs Control Individuals
Interplay of Metabolome and Gut Microbiome in Individuals With Major Depressive Disorder vs Control Individuals Supplement 1. eTable 1. Results for all models for Lifetime MDD eTable 2. Results of sensitivity analysis eTable 3. Significant Findings and replication in BBMRI-NL Study eTable 4. Novel Findings of the study eTable 5. Results of replication in the PReDICT study eTable 6. Results of Mendelian Randomization analysis eTable 7. Results of horizontal pleiotropy in MR eTable 8.
Exome sequencing identifies rare damaging variants in ATP8B4 and ABCA1 as risk factors for Alzheimer’s disease - Nature Genetics
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Abstract Alzheimer’s disease (AD), the leading cause of dementia, has an estimated heritability of approximately 70%1. The genetic component of AD has been mainly assessed using genome-wide association studies, which do not capture the risk contributed by rare variants2. Here, we compared the gene-based burden of rare damaging variants in exome sequencing data from 32,558 individuals—16,036 AD cases and 16,522 controls.
By Henne Holstege, Marc Hulsman, Rebecca Sims, Shahzad Ahmad, Najaf Amin, Oriol Dols-Icardo, Philippe Amouyel, Claudine Berr, Joshua C. Bis, Stéphanie Debette, Lindsay A Farrer, Maria Fernandez, María Fernández Verified, Nick Fox Verified, Daniela Galimberti, Emmanuelle Génin, Yann Le Guen, Jonathan Haines, M. Arfan Ikram, Robert Kraaij, Merel O. Mol, Kevin Morgan, Adam C. Naj, Valerio Napolioni, Florence Pasquier, Margaret A. Pericak-Vance, Rachel Raybould, Richard Redon, Steffi G. Riedel-Heller, Fernando Rivadeneira, Jonathan M Schott, Sudha Seshadri, Gianfranco Spalletta, André G. Uitterlinden, Jennifer S. Yokoyama, Alfredo Ramirez, Alfredo Ramírez, Simon Mead, Julie Williams, Gaël Nicolas, Céline Bellenguez, Olivier Quenez, Detelina Grozeva, Penny J. Norsworthy, Paola Bossù, J. Nicholas Cochran, Benedetta Nacmias, Pascual Sanchez-Juan, Resie van Spaendonk, Niccolo’ Tesi
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Nature
Verified
Effect of inbreeding on type 2 diabetes-related metabolites in a Dutch genetic isolate
New Results Ayşe Demirkan, Jun Liu, Najaf Amin, Ko Willems van Dijk, Cornelia M. van Duijn doi: https://doi.org/10.1101/618801 Abstract Autozygosity, meaning inheritance of an ancestral allele in the homozygous state is known to lead bi-allelic mutations that manifest their effects through the autosomal recessive inheritance pattern. Autosomal recessive mutations are known to be the underlying cause of several Mendelian metabolic diseases, especially among the offspring of related individuals.
Occupational exposure to pesticides is associated with differential DNA methylation
The current study is the first large-scale epigenome-wide association study assessing associations between occupational exposure to pesticides and DNA methylation levels. Since pesticide exposure can affect organs in the entire body, we used blood methylation to assess the effects of occupational exposure to any type of pesticide on DNA methylation levels.
Occupational exposure to pesticides is associated with differential DNA methylation
The current study is the first large-scale epigenome-wide association study assessing associations between occupational exposure to pesticides and DNA methylation levels. Since pesticide exposure can affect organs in the entire body, we used blood methylation to assess the effects of occupational exposure to any type of pesticide on DNA methylation levels.
Insight in Genome-Wide Association of Metabolite Quantitative Traits by Exome Sequence Analyses
Metabolite quantitative traits carry great promise for epidemiological studies, and their genetic background has been addressed using Genome-Wide Association Studies (GWAS). Thus far, the role of less common variants has not been exhaustively studied. Here, we set out a GWAS for metabolite quantitative traits in serum, followed by exome sequence analysis to zoom in on putative causal variants in the associated genes.
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