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Record W2029563076 · doi:10.1001/jamaneurol.2014.1491

Effects of Multiple Genetic Loci on Age at Onset in Late-Onset Alzheimer Disease

2014· review· en· W2029563076 on OpenAlexafffund
Adam C. Naj, Gyungah Jun, Christiane Reitz, Brian W. Kunkle, William Perry, Yo Son Park, Gary W. Beecham, Ruchita Rajbhandary, Kara L. Hamilton‐Nelson, Li-San Wang, John Kauwe, Matthew J. Huentelman, Amanda Myers, Thomas D. Bird, Bradley F. Boeve, Clinton T. Baldwin, Gail P. Jarvik, Paul K. Crane, Ekaterina Rogaeva, M. Michael Barmada, F. Yesim Demirci, Carlos Cruchaga, Patricia L. Kramer, Nilüfer Ertekin‐Taner, John Hardy, Neill R. Graff‐Radford, Robert C. Green, Eric B. Larson, Peter St George‐Hyslop, Joseph D. Buxbaum, Denis A. Evans, Julie A. Schneider, Kathryn L. Lunetta, M. Ilyas Kamboh, Andrew J. Saykin, Eric M. Reiman, Philip L. De Jager, David Bennett, John C. Morris, Thomas J. Montine, Alison Goate, Deborah Blacker, Debby W. Tsuang, Håkon Håkonarson, Walter A. Kukull, Tatiana Foroud, Eden R. Martin, Jonathan L. Haines, Richard Mayeux, Lindsay A. Farrer, Gerard D. Schellenberg, Margaret A. Pericak‐Vance, Marilyn S. Albert, Roger L. Albin, Liana G. Apostolova, Steven E. Arnold, Robert W. Barber, Lisa L. Barnes, Thomas G. Beach, James T. Becker, Duane Beekly, Eileen H. Bigio, James D. Bowen, Adam Boxer, James R. Burke, Nigel J. Cairns, Laura B. Cantwell, Chuanhai Cao, Chris Carlson, Regina M. Carney, Minerva M. Carrasquillo, Steven L. Carroll, Helena C. Chui, David G. Clark, Jason J. Corneveaux, David H. Cribbs, Elizabeth Crocco, Charles DeCarli, Steven T. DeKosky, Malcolm Dick, Dennis W. Dickson, Ranjan Duara, Kelley M. Faber, Kenneth B. Fallon, Martin R. Farlow, Steven H. Ferris, Matthew P. Frosch, Douglas Galasko, Mary Ganguli, Marla Gearing, Daniel H. Geschwind, Bernardino Ghetti, John R. Gilbert, Jonathan D. Glass, John H. Growdon, Ronald L. Hamilton, Lindy E. Harrell, Elizabeth Head, Lawrence S. Honig, Christine M. Hulette, Bradley T. Hyman, Gregory A. Jicha, Lee‐Way Jin, Anna Karydas, Jeffrey Kaye, Ronald Kim, Edward H. Koo, Neil W. Kowall, Joel H. Kramer, Frank M. LaFerla, James J. Lah, James B. Leverenz, Allan I. Levey, Ge Li, Andrew P. Lieberman, Chiao‐Feng Lin, Oscar L. López, Constantine G. Lyketsos, Wendy J. Mack, Frank Martiniuk, Deborah C. Mash, Eliezer Masliah, Wayne C. McCormick, Susan M. McCurry, Andrew McDavid, Ann C. McKee, Marsel Mesulam, Bruce L. Miller, Carol A. Miller, Joshua W. Miller, Jill R. Murrell, John Olichney, V. Shane Pankratz, Joseph E. Parisi, Henry L. Paulson, Elaine R. Peskind, Ronald Petersen, Aimee Pierce, Wayne W. Poon, Huntington Potter, Joseph F. Quinn, Ashok Raj, Murray A. Raskind, ‌Barry Reisberg, John M. Ringman, Erik D. Roberson, Howard J. Rosen, Roger N. Rosenberg, Mary Sano, Lon S. Schneider, William W. Seeley, Amanda Smith, Joshua A. Sonnen, Salvatore Spina, Robert A. Stern, Rudolph E. Tanzi, Tricia A. Thornton‐Wells, John Q. Trojanowski, Juan C. Troncoso, Otto Valladares, Vivianna M. Van Deerlin, Linda J. Van Eldik, Badri N. Vardarajan, Harry V. Vinters, Jean Paul Vonsattel, Sandra Weıntraub, Kathleen A. Welsh‐Bohmer, Jennifer Williamson, Sarah Wishnek, Randall L. Woltjer, Clinton B. Wright, Steven G. Younkin, Chang‐En Yu, Lei Yu

Bibliographic record

VenueJAMA Neurology · 2014
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsOccupational Cancer Research CentreUniversity of Toronto
FundersNational Human Genome Research InstituteNational Institute of Mental HealthNational Institute on AgingNational Institute of Biomedical Imaging and BioengineeringCanadian Institutes of Health ResearchGenentechNational Center for Advancing Translational SciencesMedical Research CouncilHersenstichtingNational Cancer InstituteStichting MS ResearchNewcastle UniversityBroad InstituteWellcome TrustNational Institute of Environmental Health SciencesNorthern California Institute for Research and EducationUniversity of WashingtonAlzheimer's Disease Neuroimaging InitiativeNational Center for Research ResourcesF. Hoffmann-La RocheVanderbilt UniversityChildren's Hospital of PhiladelphiaNational Institute of Neurological Disorders and StrokeUniversity of PittsburghU.S. Department of Veterans AffairsOffice of Research and DevelopmentMassachusetts General HospitalMedpaceParkinson's UKBrigham and Women's HospitalWashington University in St. LouisGlaxoSmithKlinePfizerElanNovartisUniversitat de BarcelonaRush UniversitySynarcDana FoundationFONDATION ALZHEIMERAlzheimer's AssociationAlzheimer's Research TrustEli Lilly and CompanyBristol-Myers SquibbAstraZenecaEisaiHoward Hughes Medical Institute
KeywordsApolipoprotein EDiseaseAlzheimer's diseaseAge of onsetSingle-nucleotide polymorphismGenotypeGenome-wide association studyMedicineGeneticsBiologyInternal medicineOncologyGene

Abstract

fetched live from OpenAlex

IMPORTANCE: Because APOE locus variants contribute to risk of late-onset Alzheimer disease (LOAD) and to differences in age at onset (AAO), it is important to know whether other established LOAD risk loci also affect AAO in affected participants. OBJECTIVES: To investigate the effects of known Alzheimer disease risk loci in modifying AAO and to estimate their cumulative effect on AAO variation using data from genome-wide association studies in the Alzheimer Disease Genetics Consortium. DESIGN, SETTING, AND PARTICIPANTS: The Alzheimer Disease Genetics Consortium comprises 14 case-control, prospective, and family-based data sets with data on 9162 participants of white race/ethnicity with Alzheimer disease occurring after age 60 years who also had complete AAO information, gathered between 1989 and 2011 at multiple sites by participating studies. Data on genotyped or imputed single-nucleotide polymorphisms most significantly associated with risk at 10 confirmed LOAD loci were examined in linear modeling of AAO, and individual data set results were combined using a random-effects, inverse variance-weighted meta-analysis approach to determine whether they contribute to variation in AAO. Aggregate effects of all risk loci on AAO were examined in a burden analysis using genotype scores weighted by risk effect sizes. MAIN OUTCOMES AND MEASURES: Age at disease onset abstracted from medical records among participants with LOAD diagnosed per standard criteria. RESULTS: Analysis confirmed the association of APOE with earlier AAO (P = 3.3 × 10(-96)), with associations in CR1 (rs6701713, P = 7.2 × 10(-4)), BIN1 (rs7561528, P = 4.8 × 10(-4)), and PICALM (rs561655, P = 2.2 × 10(-3)) reaching statistical significance (P < .005). Risk alleles individually reduced AAO by 3 to 6 months. Burden analyses demonstrated that APOE contributes to 3.7% of the variation in AAO (R(2) = 0.256) over baseline (R(2) = 0.221), whereas the other 9 loci together contribute to 2.2% of the variation (R(2) = 0.242). CONCLUSIONS AND RELEVANCE: We confirmed an association of APOE (OMIM 107741) variants with AAO among affected participants with LOAD and observed novel associations of CR1 (OMIM 120620), BIN1 (OMIM 601248), and PICALM (OMIM 603025) with AAO. In contrast to earlier hypothetical modeling, we show that the combined effects of Alzheimer disease risk variants on AAO are on the scale of, but do not exceed, the APOE effect. While the aggregate effects of risk loci on AAO may be significant, additional genetic contributions to AAO are individually likely to be small.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.005
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.290
Teacher spread0.270 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations196
Published2014
Admission routes2
Has abstractyes

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