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Record W3043840611 · doi:10.1101/2020.07.22.20159251

Exome sequencing identifies rare damaging variants in <i>ATP8B4</i> and <i>ABCA1</i> as novel risk factors for Alzheimer’s Disease

2020· preprint· en· W3043840611 on OpenAlexaff
Henne Holstege, Marc Hulsman, Camille Charbonnier, Benjamin Grenier‐Boley, Olivier Quenez, Detelina Grozeva, Jeroen van Rooij, Rebecca Sims, Shahzad Ahmad, Najaf Amin, Penny J. Norsworthy, Oriol Dols‐Icardo, Holger Hummerich, Amit Kawalia, Philippe Amouyel, Gary W. Beecham, Claudine Berr, Joshua C. Bis, Anne Boland, Paola Bossù, Femke H. Bouwman, José Brás, Dominique Campion, J. Nicholas Cochran, Antonio Daniele, Jean‐François Dartigues, Stéphanie Debette, Jean‐François Deleuze, Nicola Denning, Anita L. DeStefano, Lindsay A. Farrer, María Victoria Fernández, Nick C. Fox, Daniela Galimberti, Emmanuelle Génin, Gilles Thomas, Yann Le Guen, Rita Guerreiro, Jonathan L. Haines, Clive Holmes, M. Arfan Ikram, M. Kamran Ikram, Iris E. Jansen, Robert Kraaij, M Lathrop, Afina W. Lemstra, Alberto Lleó, Lauren Luckcuck, Marcel M. A. M. Mannens, Iain Marshall, Eden R. Martin, Carlo Masullo, Richard Mayeux, Patrizia Mecocci, Alun Meggy, Merel O. Mol, Kevin Morgan, R Myers, Benedetta Nacmias, Adam C. Naj, Valerio Napolioni, Florence Pasquier, Pau Pástor, Margaret A. Pericak‐Vance, Rachel Raybould, Richard Redon, Marcel Reinders, Anne‐Claire Richard, Steffi G. Riedel‐Heller, Fernando Rivadeneira, Stéphane Rousseau, Natalie S. Ryan, Salha Saad, Pascual Sánchez‐Juan, Gerard D. Schellenberg, Philip Scheltens, Jonathan M. Schott, Davide Seripa, Sudha Seshadri, Daoud Sie, Erik A. Sistermans, Sandro Sorbi, Resie van Spaendonk, Gianfranco Spalletta, Niccoló Tesi, Betty M. Tijms, André G. Uitterlinden, Sven J. van der Lee, Pieter Jelle Visser, Michael Wagner, David Wallon, Li‐San Wang, Aline Zaréa, Jordi Clarimón, John C. van Swieten, Michael D. Greicius, Jennifer S. Yokoyama, Carlos Cruchaga, John Hardy, Alfredo Ramı́rez, Simon Mead, Wiesje M. van der Flier, Cornelia M. van Duijn, Julie Williams, Gaël Nicolas, Céline Bellenguez, Jean‐Charles Lambert

Bibliographic record

VenuemedRxiv · 2020
Typepreprint
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsMcGill University and Génome Québec Innovation Centre
FundersMedical Research Council
KeywordsTREM2Genome-wide association studyExome sequencingBiologyLoss functionGeneticsGeneGenetic associationDiseaseExomePhenotypeSingle-nucleotide polymorphismMedicineGenotypeInternal medicine

Abstract

fetched live from OpenAlex

The genetic component of Alzheimer’s disease (AD) has been mainly assessed using Genome Wide Association Studies (GWAS), which do not capture the risk contributed by rare variants. 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— in a two-stage analysis. Next to known genes TREM2, SORL1 and ABCA7 , we observed a significant association of rare, predicted damaging variants in ATP8B4 and ABCA1 with AD risk, and a suggestive signal in ADAM10 . Next to these genes, the rare variant burden in RIN3, CLU, ZCWPW1 and ACE highlighted these genes as potential driver genes in AD-GWAS loci. Rare damaging variants in these genes, and in particular loss-of-function variants, have a large effect on AD-risk, and they are enriched in early onset AD cases. The newly identified AD-associated genes provide additional evidence for a major role for APP-processing, Aβ-aggregation, lipid metabolism and microglial function in AD.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.083
GPT teacher head0.337
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations12
Published2020
Admission routes1
Has abstractyes

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