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Record W4223529834 · doi:10.1038/s41598-022-09825-2

Manifestations of Alzheimer’s disease genetic risk in the blood are evident in a multiomic analysis in healthy adults aged 18 to 90

2022· review· en· W4223529834 on OpenAlexafffund
Laura Heath, John C. Earls, Andrew T. Magis, Sergey A. Kornilov, Jennifer C. Lovejoy, Cory C. Funk, Noa Rappaport, Benjamin A. Logsdon, Lara M. Mangravite, Brian W. Kunkle, Eden R. Martin, Adam C. Naj, Nilüfer Ertekin‐Taner, Todd E. Golde, Leroy Hood, Nathan D. Price, Erin L. Abner, Perrie M. Adams, Marilyn S. Albert, Roger L. Albin, Mariet Allen, Alexandre Amlie‐Wolf, Liana G. Apostolova, Steven E. Arnold, Sanjay Asthana, Craig Atwood, Clinton T. Baldwin, Robert C. Barber, Lisa L. Barnes, Sandra Barral, Thomas G. Beach, James T. Becker, Gary W. Beecham, Duane Beekly, David Bennett, Eileen H. Bigio, Thomas D. Bird, Deborah Blacker, Bradley F. Boeve, James D. Bowen, Adam Boxer, James R. Burke, Jeffrey M. Burns, William S. Bush, Mariusz Butkiewicz, Joseph D. Buxbaum, Nigel J. Cairns, Laura B. Cantwell, Chuanhai Cao, Chris Carlson, Cynthia M. Carlsson, Regina M. Carney, Helena C. Chui, Paul K. Crane, David H. Cribbs, Elizabeth Crocco, Michael L. Cuccaro, Philip L. De Jager, Charles DeCarli, Malcolm Dick, Dennis W. Dickson, Beth A. Dombroski, Rachelle S. Doody, Ranjan Duara, Denis A. Evans, Kelley M. Faber, Thomas Fairchild, Kenneth B. Fallon, David W. Fardo, Martin R. Farlow, Lindsay A. Farrer, Steven H. Ferris, Tatiana Foroud, Matthew P. Frosch, Douglas Galasko, Marla Gearing, Daniel H. Geschwind, Bernardino Ghetti, John R. Gilbert, Alison Goate, Robert C. Green, John H. Growdon, Jonathan L. Haines, Håkon Håkonarson, Ronald L. Hamilton, Kara L. Hamilton‐Nelson, Lindy E. Harrell, Lawrence S. Honig, Ryan M. Huebinger, Matthew J. Huentelman, Christine M. Hulette, Bradley T. Hyman, Gail P. Jarvik, Lee‐Way Jin, Gyungah Jun, M. Ilyas Kamboh, Anna Karydas, Mindy J. Katz, C. Dirk Keene, Ronald Kim, Neil W. Kowall, Joel H. Kramer, Walter A. Kukull, Amanda Kuzma, Frank M. LaFerla, James J. Lah, Eric B. Larson, James B. Leverenz, Allan I. Levey, Andrew P. Lieberman, Richard B. Lipton, Kathryn L. Lunetta, Constantine G. Lyketsos, John Malamon, Daniel Marson, Frank Martiniuk, Deborah C. Mash, Eliezer Masliah, Richard Mayeux, Wayne C. McCormick, Susan M. McCurry, Andrew McDavid, Ann C. McKee, Marsel Mesulam, Bruce L. Miller, Carol A. Miller, Joshua W. Miller, Thomas J. Montine, John C. Morris, Shubhabrata Mukherjee, Amanda Myers, Sid E. O’Bryant, John Olichney, Joseph E. Parisi, Henry L. Paulson, Margaret A. Pericak‐Vance, William Perry, Elaine R. Peskind, Ronald Petersen, Aimee Pierce, Wayne W. Poon, Huntington Potter, Liming Qu, Joseph F. Quinn, Ashok Raj, Murray A. Raskind, Eric M. Reiman, ‌Barry Reisberg, Joan Reisch, Christiane Reitz, John M. Ringman, Erik D. Roberson, Ekaterina Rogaeva, Howard J. Rosen, Roger N. Rosenberg, Donald R. Royall, Mark A. Sager, Mary Sano, Andrew J. Saykin, Gerard D. Schellenberg, Julie A. Schneider, Lon S. Schneider, William W. Seeley, Susan Slifer, Amanda Smith, Yeunjoo E. Song, Joshua A. Sonnen, Salvatore Spina, Peter St George‐Hyslop, Robert A. Stern, Russell H. Swerdlow, Mitchell Tang, Rudolph E. Tanzi, John Q. Trojanowski, Juan C. Troncoso, Debby W. Tsuang, Otto Valladares, Vivianna M. Van Deerlin, Linda J. Van Eldik, Jeffery M. Vance, Badri N. Vardarajan, Harry V. Vinters, Jean Paul Vonsattel, Li-San Wang, Sandra Weıntraub, Kathleen A. Welsh‐Bohmer, Patrice L. Whitehead, Kirk C. Wilhelmsen, Jennifer Williamson, Thomas S. Wingo, Randall L. Woltjer, Clinton B. Wright, Chuang‐Kuo Wu, Steven G. Younkin, Chang‐En Yu, Lei Yu, Yi Zhao

Bibliographic record

VenueScientific Reports · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsOccupational Cancer Research CentreUniversity of Toronto
FundersNational Institute on Minority Health and Health DisparitiesNational Center for Research ResourcesNational Human Genome Research InstituteNational Institute of Mental HealthNational Institute on AgingUniversity of California, IrvineUniversity of California, San FranciscoNational Institute of Biomedical Imaging and BioengineeringUniversity of California, Los AngelesNational Institutes of HealthMedical Research CouncilHersenstichtingNational Cancer InstituteStichting MS ResearchNewcastle UniversityU.S. Department of DefenseBrightFocus FoundationEli Lilly and CompanyAlzheimer's Research TrustJohns Hopkins UniversityUniversity of WashingtonYork UniversityBiogenUniversity of MiamiBioClinicaNorthwestern UniversityEmory UniversityUniversity of PennsylvaniaVanderbilt UniversityNorth Bristol NHS TrustBristol-Myers SquibbUniversity of California, DavisEisaiHoward Hughes Medical InstituteMassachusetts General HospitalNational Institute of Neurological Disorders and StrokeUniversity of PittsburghUniversity of California, San DiegoU.S. Department of Veterans AffairsOffice of Research and DevelopmentAlzheimer's AssociationWellcome TrustUniversity of Southern CaliforniaUniversitat de BarcelonaRush University
KeywordsGenome-wide association studySNPSingle-nucleotide polymorphismBiologyDiseaseGeneticsGenetic associationAlleleBioinformaticsMedicineGenotypeInternal medicineGene

Abstract

fetched live from OpenAlex

Genetics play an important role in late-onset Alzheimer's Disease (AD) etiology and dozens of genetic variants have been implicated in AD risk through large-scale GWAS meta-analyses. However, the precise mechanistic effects of most of these variants have yet to be determined. Deeply phenotyped cohort data can reveal physiological changes associated with genetic risk for AD across an age spectrum that may provide clues to the biology of the disease. We utilized over 2000 high-quality quantitative measurements obtained from blood of 2831 cognitively normal adult clients of a consumer-based scientific wellness company, each with CLIA-certified whole-genome sequencing data. Measurements included: clinical laboratory blood tests, targeted chip-based proteomics, and metabolomics. We performed a phenome-wide association study utilizing this diverse blood marker data and 25 known AD genetic variants and an AD-specific polygenic risk score (PGRS), adjusting for sex, age, vendor (for clinical labs), and the first four genetic principal components; sex-SNP interactions were also assessed. We observed statistically significant SNP-analyte associations for five genetic variants after correction for multiple testing (for SNPs in or near NYAP1, ABCA7, INPP5D, and APOE), with effects detectable from early adulthood. The ABCA7 SNP and the APOE2 and APOE4 encoding alleles were associated with lipid variability, as seen in previous studies; in addition, six novel proteins were associated with the e2 allele. The most statistically significant finding was between the NYAP1 variant and PILRA and PILRB protein levels, supporting previous functional genomic studies in the identification of a putative causal variant within the PILRA gene. We did not observe associations between the PGRS and any analyte. Sex modified the effects of four genetic variants, with multiple interrelated immune-modulating effects associated with the PICALM variant. In post-hoc analysis, sex-stratified GWAS results from an independent AD case-control meta-analysis supported sex-specific disease effects of the PICALM variant, highlighting the importance of sex as a biological variable. Known AD genetic variation influenced lipid metabolism and immune response systems in a population of non-AD individuals, with associations observed from early adulthood onward. Further research is needed to determine whether and how these effects are implicated in early-stage biological pathways to AD. These analyses aim to complement ongoing work on the functional interpretation of AD-associated genetic variants.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.050
GPT teacher head0.355
Teacher spread0.305 · 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 designObservational
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

Citations28
Published2022
Admission routes2
Has abstractyes

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