Association Between Common Variants in RBFOX1, an RNA-Binding Protein, and Brain Amyloidosis in Early and Preclinical Alzheimer Disease

Neha S Raghavan; Logan Dumitrescu; Elizabeth Mormino; Emily R Mahoney; Annie J Lee; Yizhe Gao; Murat Bilgel; David Goldstein; Theresa Harrison; Corinne D Engelman; Andrew J Saykin; Christopher D Whelan; Jimmy Z Liu; William Jagust; Marilyn Albert; Sterling C Johnson; Hyun-Sik Yang; Keith Johnson; Paul Aisen; Susan M Resnick; Reisa Sperling; Philip L De Jager; Julie Schneider; David A Bennett; Matthew Schrag; Badri Vardarajan; Timothy J Hohman; Richard Mayeux

doi:10.1001/jamaneurol.2020.1760

. 2020 Jun 22;77(10):1–11. doi: 10.1001/jamaneurol.2020.1760

Association Between Common Variants in RBFOX1, an RNA-Binding Protein, and Brain Amyloidosis in Early and Preclinical Alzheimer Disease

Neha S Raghavan ^1,^2,^3,⁴, Logan Dumitrescu ^5,⁶, Elizabeth Mormino ⁷, Emily R Mahoney ^5,⁶, Annie J Lee ^1,^2,³, Yizhe Gao ^1,^2,³, Murat Bilgel ⁸, David Goldstein ^1,^2,³, Theresa Harrison ⁹, Corinne D Engelman ¹⁰, Andrew J Saykin ^11,¹², Christopher D Whelan ¹³, Jimmy Z Liu ¹³, William Jagust ⁹, Marilyn Albert ¹⁴, Sterling C Johnson ¹⁵, Hyun-Sik Yang ^16,¹⁷, Keith Johnson ^16,¹⁷, Paul Aisen ¹⁸, Susan M Resnick ⁸, Reisa Sperling ^16,¹⁷, Philip L De Jager ^1,^2,^3,^19,²⁰, Julie Schneider ²¹, David A Bennett ²¹, Matthew Schrag ⁵, Badri Vardarajan ^1,^2,^3,⁴, Timothy J Hohman ^5,⁶, Richard Mayeux ^1,^2,^3,^4,^✉, for the Alzheimer’s Disease Neuroimaging Initiative

¹Department of Neurology, Columbia University Medical Center, New York, New York

²Department of Neurology, The New York Presbyterian Hospital, New York

³Taub Institute for Research on Alzheimer’s Disease and The Aging Brain, Columbia University Medical Center, New York, New York

⁴The Institute for Genomic Medicine, Columbia University Medical Center, New York, New York

⁵Vanderbilt Memory and Alzheimer’s Center, Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee

⁶Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee

⁷Department of Neurology and Neurological Sciences, Stanford University, Stanford, California

⁸Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland

⁹Helen Wills Neuroscience Institute, University of California, Berkeley

¹⁰Department of Population Health Sciences, University of Wisconsin, School of Medicine and Public Health, Madison

¹¹Department of Radiology and Imaging Sciences, Center for Neuroimaging, School of Medicine, Indiana University, Indianapolis

¹²Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis

¹³Research and Early Development, Biogen Inc, Cambridge, Massachusetts

¹⁴Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland

¹⁵Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison

¹⁶Department of Neurology, Massachusetts General Hospital, Boston

¹⁷Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women’s Hospital, Boston, Massachusetts

¹⁸Alzheimer’s Therapeutic Research Institute, Keck School of Medicine, University of Southern California, San Diego

¹⁹Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York, New York

²⁰Cell Circuits Program, Broad Institute, Cambridge, Massachusetts

²¹Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois

Group Information: The Alzheimer’s Disease Neuroimaging Initiative members appear at the end of the article.

Accepted for Publication: March 6, 2020.

^✉

Corresponding Author: Richard Mayeux, MD, Department of Neurology, New York Presbyterian/Columbia University Medical Center, 710 W 168th St, New York, NY 10032 (rpm2@cumc.columbia.edu).

Published Online: June 22, 2020. doi:10.1001/jamaneurol.2020.1760

Author Contributions: Drs Raghavan and Dumitrescu contributed equally to this study. Drs Hohman and Mayeux contributed equally to this study, had full access to all of the data in the study, and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Raghavan, Dumitrescu, Goldstein, Harrison, Whelan, Liu, Bennett, Schrag, Hohman, Mayeux.

Acquisition, analysis, or interpretation of data: Raghavan, Dumitrescu, Mormino, Mahoney, Lee, Gao, Bilgel, Engelman, Saykin, Whelan, Jagust, Albert, S. C. Johnson, Yang, K. Johnson, Aisen, Resnick, Sperling, De Jager, Schneider, Bennett, Schrag, Vardarajan, Hohman, Mayeux.

Drafting of the manuscript: Raghavan, Dumitrescu, Vardarajan, Hohman, Mayeux.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Raghavan, Dumitrescu, Mahoney, Lee, Gao, Goldstein, Yang, Vardarajan, Hohman.

Obtained funding: Mormino, Engelman, Whelan, Jagust, Aisen, Sperling, Bennett, Hohman, Mayeux.

Administrative, technical, or material support: Bilgel, Whelan, Jagust, K. Johnson, Schneider, Bennett, Mayeux.

Supervision: Raghavan, Whelan, Liu, K. Johnson, Aisen, Bennett, Schrag, Vardarajan, Hohman, Mayeux.

Conflict of Interest Disclosures: Dr Mormino reported receiving funding from National Institutes of Health (NIH) grant K01AG051718 during the conduct of the study, personal fees from Roche, and grants from the NIH outside the submitted work. Dr Goldstein reported other support from Q State-Pairnomix, other support from Praxis Therapeutics, other support from Apostle Inc, and personal fees from AstraZeneca, Gilead Sciences, and GoldFinch Bio outside the submitted work. Dr Engelman reported receiving grants from the NIH during the conduct of the study. Dr Saykin reported receiving grants from the NIH during the conduct of the study; other support from Springer-Nature outside the submitted work, and F18-flortaucipir precursor as in-kind support from Avid Radiopharmaceuticals, a subsidiary of Eli Lilly. Dr Whelan reported other support from Biogen during the conduct of the study and is an employee of Biogen. Dr Albert reported receiving grants from the National Institute on Aging (NIA) during the conduct of the study and is a consultant for Eli Lilly. Dr S. C. Johnson reported receiving grants from the NIH during the conduct of the study, personal fees from Roche Diagnostics, and grants and nonfinancial support from Cerveau Technologies outside the submitted work. Dr Aisen reported receiving grants from Eli Lilly and the NIH during the conduct of the study, as well as personal fees from Merck, Roche, Biogen, ImmunoBrain Checkpoint, and Samus outside the submitted work. Dr Sperling reported receiving grants from Eli Lilly and the Alzheimer's Association during the conduct of the study, as well as personal fees from AC Immune, Eisai, Roche, Neurocentria, and Takeda and grants from Janssen outside the submitted work. Dr Schneider reported receiving grants from the NIH during the conduct of the study. Dr Bennett reported receiving grants from the NIH and Illinois during the conduct of the study. Dr Schrag reported receiving grant K76AG060001 from the NIH/NIA and grant R03NS111486 from the NIH/National Institute of Neurological Disorders and Stroke during the conduct of the study, as well as grants from the NIH and a loan repayment grant outside the submitted work. Dr Hohman reported receiving grants from the NIH during the conduct of the study. No other disclosures were reported.

Funding/Support: This research was supported in part by NIA grants RF1-AG054023 (Dr Mayeux), K01-AG049164 (Dr Hohman), R01-AG059716 (Dr Hohman), R21-AG059941 (Dr Hohman), HHSN311201600276P, K24-AG046373, R01-AG034962, R01-NS100980, P30AG10161 (Dr Bennett), R01AG15819 (Dr Bennett), R01AG17917 (Dr Bennett), R01-AG056534, R01AG036836 (Dr De Jager), R01AG034570 (Dr Jagust), R01-AG063689 (Drs Aisen and Sperling), U19-AG010483 (Dr Aisen), U01-AG061356 (Drs De Jager and Bennett), U01-AG024904, P30-AG010133 (Dr Saykin), R01-AG054047 (Dr Engelman), and R01-AG019771 (Dr Saykin); the Intramural Research Program of the NIA/NIH; the Vanderbilt Memory and Alzheimer's Center; and The Columbia University Alzheimer’s Disease Research Center grant P50-AG008702. The Vanderbilt Neurosciences Biospecimen Bank is supported by philanthropy from the Kirshner Research Fund. The Anti-Amyloid Treatment in Asymptomatic Alzheimer Disease (A4) study is a secondary prevention trial in preclinical Alzheimer’s disease, aiming to slow cognitive decline associated with brain amyloid accumulation in clinically normal older individuals. The A4 Study is funded by a public-private-philanthropic partnership, including funding from the NIH/NIA, Eli Lilly and Co, Alzheimer’s Association, Accelerating Medicines Partnership, GHR Foundation, an anonymous foundation, and additional private donors, with in-kind support from Avid and Cogstate. The companion observational Longitudinal Evaluation of Amyloid Risk and Neurodegeneration (LEARN) Study is funded by the Alzheimer’s Association and GHR Foundation.

Role of the Funder/Sponsor: The funding organizations had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Group Information: Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf. A complete listing of ADNI I, Grand Opportunities, II, and III investigators is as follows: Leadership and Infrastructure: principal investigator: Michael W. Weiner, MD, University of California, San Francisco; Alzheimer’s Therapeutic Research Institute (ATRI) principal investigator and director of Coordinating Center Clinical Core: Paul Aisen, MD, University of Southern California; Executive Committee: Michael Weiner, MD, University of California, San Francisco; Paul Aisen, MD, University of Southern California; Ronald Petersen, MD, PhD, Mayo Clinic, Rochester; Clifford R. Jack Jr, MD, Mayo Clinic, Rochester; William Jagust, MD, University of California, Berkeley; John Q. Trojanowki, MD, PhD, University of Pennsylvania; Arthur W. Toga, PhD, University of Southern California; Laurel Beckett, PhD, University of California, Davis; Robert C. Green, MD, MPH, Brigham and Women’s Hospital/Harvard Medical School; Andrew J. Saykin, PsyD, Indiana University; John Morris, MD, Washington University, St Louis; Leslie M. Shaw, University of Pennsylvania; ADNI External Advisory Board: Zaven Khachaturian, PhD, Prevent Alzheimer’s Disease 2020 (chair); Greg Sorensen, MD, Siemens; Maria Carrillo, PhD, Alzheimer’s Association; Lew Kuller, MD, University of Pittsburgh; Marc Raichle, MD, Washington University, St Louis; Steven Paul, MD, Cornell University; Peter Davies, MD, Albert Einstein College of Medicine of Yeshiva University; Howard Fillit, MD, AD Drug Discovery Foundation; Franz Hefti, PhD, Acumen Pharmaceuticals; David Holtzman, MD, Washington University, St Louis; M. Marcel Mesulam, MD, Northwestern University; William Potter, MD, National Institute of Mental Health; Peter Snyder, PhD, Brown University; ADNI 3 Private Partner Scientific Board: Veronika Logovinsky, MD, PhD, Eli Lilly (chair); Data and Publications Committee: Robert C. Green, MD, MPH, Brigham and Women’s Hospital/Harvard Medical School; (chair); Resource Allocation Review Committee: Tom Montine, MD, PhD, University of Washington (chair); Clinical Core Leaders: Ronald Petersen, MD, PhD, Mayo Clinic, Rochester (core principal investigator); Paul Aisen, MD, University of Southern California; Clinical Informatics and Operations: Gustavo Jimenez, MBS, University of Southern California; Michael Donohue, PhD, University of Southern California; Devon Gessert, BS, University of Southern California; Kelly Harless, BA, University of Southern California; Jennifer Salazar, MBS, University of Southern California; Yuliana Cabrera, BS, University of Southern California; Sarah Walter, MSc, University of Southern California; Lindsey Hergesheimer, BS, University of Southern California; Biostatistics Core Leaders and Key Personnel: Laurel Beckett, PhD, University of California, Davis (core principal investigator); Danielle Harvey, PhD, University of California, Davis; Michael Donohue, PhD, University of California, San Diego; MRI Core Leaders and Key Personnel: Clifford R. Jack Jr, MD, Mayo Clinic, Rochester (core principal investigator); Matthew Bernstein, PhD, Mayo Clinic, Rochester; Nick Fox, MD, University of London; Paul Thompson, PhD, University of California, Los Angeles School of Medicine; Norbert Schuff, PhD, University of California, San Francisco, MRI; Charles DeCarli, MD, University of California, Davis; Bret Borowski, RT, Mayo Clinic; Jeff Gunter, PhD, Mayo Clinic; Matt Senjem, MS, Mayo Clinic; Prashanthi Vemuri, PhD, Mayo Clinic; David Jones, MD, Mayo Clinic; Kejal Kantarci, Mayo Clinic; Chad Ward, Mayo Clinic; PET Core Leaders and Key Personnel: William Jagust, MD, University of California, Berkeley (core principal investigator); Robert A. Koeppe, PhD, University of Michigan; Norm Foster, MD, University of Utah; Eric M. Reiman, MD, Banner Alzheimer’s Institute; Kewei Chen, PhD, Banner Alzheimer’s Institute; Chet Mathis, MD, University of Pittsburgh; Susan Landau, PhD, University of California, Berkeley; Neuropathology Core Leaders: John C. Morris, MD, Washington University, St Louis; Nigel J. Cairns, PhD, FRCPath, Washington University, St Louis; Erin Franklin, MS, CCRP Washington University, St Louis; Lisa Taylor-Reinwald, BA, HTL, Washington University, St Louis (past investigator); Biomarkers Core Leaders and Key Personnel: Leslie M. Shaw, PhD, University of Pennsylvania School of Medicine; John Q. Trojanowki, MD, PhD, University of Pennsylvania School of Medicine; Virginia Lee, PhD, MBA, University of Pennsylvania School of Medicine; Magdalena Korecka, PhD, University of Pennsylvania School of Medicine; Michal Figurski, PhD, University of Pennsylvania School of Medicine; Informatics Core Leaders and Key Personnel: Arthur W. Toga, PhD, University of Southern California (core principal investigator); Karen Crawford, University of Southern California; Scott Neu, PhD, University of Southern California; Genetics Core Leaders and Key Personnel: Andrew J. Saykin, PsyD, Indiana University; Tatiana M. Foroud, PhD, Indiana University; Steven Potkin, MD, University of California, Irvine; Li Shen, PhD, Indiana University; Kelley Faber, MS, CCRC, Indiana University; Sungeun Kim, PhD, Indiana University; Kwangsik Nho, PhD, Indiana University; Initial Concept Planning & Development: Michael W. Weiner, MD, University of California, San Francisco; Lean Thal, MD, University of California, San Diego; Zaven Khachaturian, PhD, Prevent Alzheimer’s Disease 2020; Early Project Proposal Development: Leon Thal, MD, University of California, San Diego; Neil Buckholtz, National Institute on Aging; Michael W. Weiner, MD, University of California, San Francisco; Peter J. Snyder, PhD, Brown University; William Potter, MD, National Institute of Mental Health; Steven Paul, MD, Cornell University; Marilyn Albert, PhD, The Johns Hopkins University; Richard Frank, MD, PhD, Richard Frank Consulting; Zaven Khachaturian, PhD, Prevent Alzheimer’s Disease 2020; National Institute on Aging: John Hsiao, MD, National Institute on Aging; Investigators by Site: Oregon Health & Science University: Joseph Quinn, MD; Lisa C. Silbert, MD; Betty Lind, BS; Jeffrey A. Kaye, MD (past investigator); Raina Carter, BA (past investigator); Sara Dolen, BS (past investigator); University of Southern California: Lon S. Schneider, MD; Sonia Pawluczyk, MD; Mauricio Becerra, BS; Liberty Teodoro, RN; Bryan M. Spann, DO, PhD (past investigator); University of California, San Diego: James Brewer, MD, PhD; Helen Vanderswag, RN; Adam Fleisher, MD (past investigator); University of Michigan: Jaimie Ziolkowski, MA, BS, TLL; Judith L. Heidebrink, MD, MS; Joanne L. Lord, LPN, BA, CCRC (past investigator); Mayo Clinic, Rochester: Ronald Petersen, MD, PhD; Sara S. Mason, RN; Colleen S. Albers, RN; David Knopman, MD; Kris Johnson, RN (past investigator); Baylor College of Medicine: Javier Villanueva-Meyer, MD; Valory Pavlik, PhD; Nathaniel Pacini, MA; Ashley Lamb, MA; Joseph S. Kass, MD, LD, FAAN; Rachelle S. Doody, MD, PhD (past investigator); Victoria Shibley, MS (past investigator); Munir Chowdhury, MBBS, MS (past investigator); Susan Rountree, MD (past investigator); Mimi Dang, MD (past investigator); Columbia University Medical Center: Yaakov Stern, PhD; Lawrence S. Honig, MD, PhD; Karen L. Bell, MD; Randy Yeh, MD. Washington University, St Louis: Beau Ances, MD, PhD, MSc; John C. Morris, MD; David Winkfield, BS; Maria Carroll, RN, MSN, GCNS-BC; Angela Oliver, RN, BSN, MSG; Mary L. Creech, RN, MSW (past investigator); Mark A. Mintun, MD (past investigator); Stacy Schneider, APRN, BC, GNP (past investigator); University of Alabama, Birmingham: Daniel Marson, JD, PhD; David Geldmacher, MD; Marissa Natelson Love, MD; Randall Griffith, PhD, ABPP (past investigator); David Clark, MD (past investigator); John Brockington, MD (past investigator); Mount Sinai School of Medicine: Hillel Grossman, MD; Effie Mitsis, PhD (past investigator); Rush University Medical Center: Raj C. Shah, MD; Melissa Lamar, PhD; Patricia Samuels; Wien Center: Ranjan Duara, MD; Maria T. Greig-Custo, MD; Rosemarie Rodriguez, PhD; The Johns Hopkins University: Marilyn Albert, PhD; Chiadi Onyike, MD; Daniel D’Agostino II, BS; Stephanie Kielb, BS (past investigator); New York University: Martin Sadowski, MD, PhD; Mohammed O. Sheikh, MD; Jamika Singleton-Garvin, CCRP; Anaztasia Ulysse Mrunalini Gaikwad; Duke University Medical Center: P. Murali Doraiswamy, MBBS, FRCP; Jeffrey R. Petrella, MD; Olga James, MD; Salvador Borges-Neto, MD; Terence Z. Wong, MD (past investigator); Edward Coleman (past investigator); University of Pennsylvania: Jason H. Karlawish, MD; David A. Wolk, MD; Sanjeev Vaishnavi, MD; Christopher M. Clark, MD (past investigator); Steven E. Arnold, MD (past investigator); University of Kentucky: Charles D. Smith, MD; Greg Jicha, MD; Peter Hardy, PhD; Riham El Khouli, MD; Elizabeth Oates, MD; Gary Conrad, MD; University of Pittsburgh: Oscar L. Lopez, MD; MaryAnn Oakley, MA; Donna M. Simpson, CRNP, MPH; University of Rochester Medical Center: Anton P. Porsteinsson, MD; Kim Martin, RN; Nancy Kowalksi, MS, RNC; Melanie Keltz, RN; Bonnie S. Goldstein, MS, NP (past investigator); Kelly M. Makino, BS (past investigator); M. Saleem Ismail, MD (past investigator); Connie Brand, RN (past investigator); University of California, Irvine IMIND: Gaby Thai, MD; Aimee Pierce, MD; Beatriz Yanez, RN; Elizabeth Sosa, PhD; Megan Witbracht, PhD; University of Texas Southwestern Medical School: Kyle Womack, MD; Dana Mathews, MD, PhD; Mary Quiceno, MD; Emory University: Allan I. Levey, MD, PhD; James J. Lah, MD, PhD; Janet S. Cellar, DNP, PMHCNS-BC; University of Kansas, Medical Center: Jeffrey M. Burns, MD; Russell H. Swerdlow, MD; William M. Brooks, PhD; University of California, Los Angeles: Ellen Woo, PhD; Daniel H.S. Silverman, MD, PhD; Edmond Teng, MD, PhD; Sarah Kremen, MD; Liana Apostolova, MD (past investigator); Kathleen Tingus, PhD (past investigator); Po H. Lu, PsyD (past investigator); George Bartzokis, MD (past investigator); Mayo Clinic, Jacksonville: Neill R Graff-Radford, MBBCH, FRCP (London); Francine Parfitt, MSH, CCRC; Kim Poki-Walker, BA; Indiana University: Martin R. Farlow, MD; Ann Marie Hake, MD; Brandy R. Matthews, MD (past investigator); Jared R. Brosch, MD; Scott Herring, RN, CCRC; Yale University School of Medicine: Christopher H. van Dyck, MD; Richard E. Carson, PhD; Pradeep Varma, MD; McGill University, Montreal-Jewish General Hospital: Howard Chertkow, MD; Howard Bergman, MD; Chris Hosein, MEd; Sunnybrook Health Sciences, Ontario: Sandra Black, MD; Bojana Stefanovic, PhD; Chris (Chinthaka) Heyn, BSC, PhD, MD; U.B.C. Clinic for AD & Related Disorders: Ging-Yuek Robin Hsiung, MD, MHSc; Benita Mudge, BS; Vesna Sossi, PhD; Howard Feldman, MD (past investigator); Michele Assaly, MA (past investigator); Cognitive Neurology - St Joseph's, Ontario: Elizabeth Finger, MD; Stephen Pasternack, MD, PhD; William Pavlosky, MD; Irina Rachinsky, MD (past investigator); Dick Drost, PhD (past investigator); Andrew Kertesz, MD (past investigator); Cleveland Clinic Lou Ruvo Center for Brain Health: Charles Bernick, MD, MPH; Donna Munic, PhD; Northwestern University: Marek-Marsel Mesulam, MD; Emily Rogalski, PhD; Kristine Lipowski, MA; Sandra Weintraub, PhD; Borna Bonakdarpour, MD; Diana Kerwin, MD (past investigator); Chuang-Kuo Wu, MD, PhD (past investigator); Nancy Johnson, PhD (past investigator); Premiere Research Institute (Palm Beach Neurology): Carl Sadowsky, MD; Teresa Villena, MD; Georgetown University Medical Center: Raymond Scott Turner, MD, PhD; Kathleen Johnson, NP Brigid Reynolds, NP; Brigham and Women's Hospital: Reisa A. Sperling, MD; Keith A. Johnson, MD; Gad A. Marshall, MD; Stanford University: Jerome Yesavage, MD; Joy L. Taylor, PhD; Steven Chao, MD, PhD; Barton Lane, MD (past investigator); Allyson Rosen, PhD (past investigator); Jared Tinklenberg, MD (past investigator); Banner Sun Health Research Institute: Edward Zamrini, MD; Christine M. Belden, PsyD; Sherye A. Sirrel, CCRC Boston University: Neil Kowall, MD; Ronald Killiany, PhD; Andrew E. Budson, MD; Alexander Norbash, MD (past investigator); Patricia Lynn Johnson, BA (past investigator); Howard University: Thomas O. Obisesan, MD, MPH; Ntekim E. Oyonumo, MD, PhD; Joanne Allard, PhD; Olu Ogunlana, BPharm; Case Western Reserve University: Alan Lerner, MD; Paula Ogrocki, PhD; Curtis Tatsuoka, PhD; Parianne Fatica, BA, CCRC; University of California, Davis – Sacramento: Evan Fletcher, PhD; Pauline Maillard, PhD; John Olichney, MD; Charles DeCarli, MD; Owen Carmichael, PhD (past investigator); Neurological Care of CNY: Smita Kittur, MD (past investigator); Parkwood Institute: Michael Borrie, MB ChB; T-Y Lee, PhD; Rob Bartha, PhD; University of Wisconsin: Sterling Johnson, PhD; Sanjay Asthana, MD; Cynthia M. Carlsson, MD, MS; Banner Alzheimer's Institute: Pierre Tariot, MD; Anna Burke, MD; Joel Hetelle, BS; Kathryn DeMarco, BS; Nadira Trncic, MD, PhD, CCRC (past investigator); Adam Fleisher, MD (past investigator); Stephanie Reeder, BA (past investigator); Dent Neurologic Institute: Vernice Bates, MD; Horacio Capote, MD; Michelle Rainka, PharmD, CCRP; The Ohio State University: Douglas W. Scharre, MD; Maria Kataki, MD, PhD; Rawan Tarawneh, MD; Albany Medical College: Earl A. Zimmerman, MD; Dzintra Celmins, MD; David Hart, MD; Hartford Hospital, Olin Neuropsychiatry Research Center: Godfrey D. Pearlsoinn, MD; Karen Blank, MD; Karen Anderson, RN; Dartmouth-Hitchcock Medical Center: Laura A. Flashman, PhD; Marc Seltzer, MD; Mary L. Hynes, RN, MPH; Robert B. Santulli, MD (past investigator); Wake Forest University Health Sciences: Kaycee M. Sink, MD, MAS; Mia Yang, MD; Akiva Mintz, MD, PhD; Rhode Island Hospital: Brian R. Ott, MD; Geoffrey Tremont, PhD; Lori A. Daiello, Pharm.D, ScM; Butler Hospital: Courtney Bodge, PhD; Stephen Salloway, MD, MS; Paul Malloy, PhD; Stephen Correia, PhD; Athena Lee, PhD; University of California, San Francisco: Howard J. Rosen, MD; Bruce L. Miller, MD; David Perry, MD; Medical University South Carolina: Jacobo Mintzer, MD, MBA; Kenneth Spicer, MD, PhD; David Bachman, MD; St Joseph’s Health Care: Elizabeth Finger, MD; Stephen Pasternak, MD; Irina Rachinsky, MD; John Rogers, MD; Andrew Kertesz, MD (past investigator); Dick Drost, MD (past investigator); Nathan Kline Institute: Nunzio Pomara, MD; Raymundo Hernando, MD; Antero Sarrael, MD; University of Iowa College of Medicine: Delwyn D. Miller, PharmD, MD; Karen Ekstam Smith, RN; Hristina Koleva, MD; Ki Won Nam, MD; Hyungsub Shim, MD; Susan K. Schultz, MD (past investigator); Cornell University: Norman Relkin, MD, PhD; Gloria Chiang, MD; Michael Lin, MD; Lisa Ravdin, PhD; University of South Florida USF Health Byrd Alzheimer’s Institute: Amanda Smith, MD; Christi Leach, MD; Balebail Ashok Raj, MD (past investigator); Kristin Fargher, MD (past investigator).

Additional Information: The A4 Study and LEARN Study are led by Dr Reisa Sperling at Brigham and Women’s Hospital, Harvard Medical School and Dr Paul Aisen at ATRI, University of Southern California. The A4 and LEARN Studies are coordinated by ATRI at the University of Southern California, and the data are made available through the Laboratory for Neuro Imaging at the University of Southern California. The participants screening for the A4 Study provided permission to share their deidentified data to advance the quest to find a successful treatment for Alzheimer’s disease. We acknowledge the dedication of all the participants, the site personnel, and all of the partnership team members who continue to make the A4 and LEARN Studies possible. The data on the ADNI study are available at http://www.adni-info.org and the complete A4 Study Team list is available at https://a4study.org/a4-study-team/. Biogen Inc provided support for genotyping of the A4 Study cohort. Data from the Religious Orders Study and Rush Memory and Aging Project (ROS/MAP) can be requested at https://www.radc.rush.edu/. The PLINK program is available at https://www.cog-genomics.org/plink/1.9.

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Corresponding author.

PMCID: PMC7309575 PMID: 32568366

Key Points

Question

Is RBFOX1 associated with brain amyloidosis, as measured by positron emission tomographic imaging, in early and preclinical Alzheimer disease?

Findings

In this genetic association study, a meta-analysis of amyloid positron emission tomographic imaging data collected on 4314 participants in 6 studies noted genome-wide significant associations with single-nucleotide variants in a novel locus, RBFOX1, as well as in APOE. In addition, reduced expression of RBFOX1 appeared to be associated with increased amyloid burden and global cognitive decline during life.

Meaning

In this study, RBFOX1 appeared to be a novel locus associated with positron emission tomographic imaging–derived brain amyloidosis and may be involved in the pathogenesis of Alzheimer disease.

Abstract

Importance

Genetic studies of Alzheimer disease have focused on the clinical or pathologic diagnosis as the primary outcome, but little is known about the genetic basis of the preclinical phase of the disease.

Objective

To examine the underlying genetic basis for brain amyloidosis in the preclinical phase of Alzheimer disease.

Design, Setting, and Participants

In the first stage of this genetic association study, a meta-analysis was conducted using genetic and imaging data acquired from 6 multicenter cohort studies of healthy older individuals between 1994 and 2019: the Anti-Amyloid Treatment in Asymptomatic Alzheimer Disease Study, the Berkeley Aging Cohort Study, the Wisconsin Registry for Alzheimer’s Prevention, the Biomarkers of Cognitive Decline Among Normal Individuals cohort, the Baltimore Longitudinal Study of Aging, and the Alzheimer Disease Neuroimaging Initiative, which included Alzheimer disease and mild cognitive impairment. The second stage was designed to validate genetic observations using pathologic and clinical data from the Religious Orders Study and Rush Memory and Aging Project. Participants older than 50 years with amyloid positron emission tomographic (PET) imaging data and DNA from the 6 cohorts were included. The largest cohort, the Anti-Amyloid Treatment in Asymptomatic Alzheimer Disease Study (n = 3154), was the PET screening cohort used for a secondary prevention trial designed to slow cognitive decline associated with brain amyloidosis. Six smaller, longitudinal cohort studies (n = 1160) provided additional amyloid PET imaging data with existing genetic data. The present study was conducted from March 29, 2019, to February 19, 2020.

Main Outcomes and Measures

A genome-wide association study of PET imaging amyloid levels.

Results

From the 4314 analyzed participants (age, 52-96 years; 2478 participants [57%] were women), a novel locus for amyloidosis was noted within RBFOX1 (β = 0.61, P = 3 × 10⁻⁹) in addition to APOE. The RBFOX1 protein localized around plaques, and reduced expression of RBFOX1 was correlated with higher amyloid-β burden (β = −0.008, P = .002) and worse cognition (β = 0.007, P = .006) during life in the Religious Orders Study and Rush Memory and Aging Project cohort.

Conclusions and Relevance

RBFOX1 encodes a neuronal RNA-binding protein known to be expressed in neuronal tissues and may play a role in neuronal development. The findings of this study suggest that RBFOX1 is a novel locus that may be involved in the pathogenesis of Alzheimer disease.

This genetic association study examines amyloid levels through positron emission tomographic imaging in individuals with or at risk for Alzheimer disease.

Introduction

Alzheimer disease (AD) is a complex polygenic disease with high heritability. Genome-wide association studies (GWAS) have identified more than 25 risk loci that highlight amyloid processing, lipid metabolism, endocytosis, and innate immunity as important biological factors in the development of AD.^1,2 While much of the genetic work on AD has focused on clinical diagnosis as the primary outcome, AD is heterogeneous and has a long preclinical phase when brain amyloid deposition accumulates before the onset of cognitive impairment.³

The development of amyloid positron emission tomographic (PET) imaging tracers has provided a biomarker for diagnosis and risk assessment enabling in vivo detection of fibrillar amyloid-β before the onset of symptoms.⁴ The approval by the US Food and Drug Administration of additional ligands facilitated the application of amyloid PET imaging in clinical practice and in research.⁵ Advancing this biomarker, Jack et al⁶ proposed a model in which brain amyloid-β deposition precedes the onset of neurodegeneration and cognitive dysfunction. This model also implied that an amyloid-β biomarker, such as PET imaging, could identify individuals at the highest risk for AD long before the diagnosis. Several previous genetic investigations of brain amyloidosis using amyloid PET imaging have found an association with the APOE locus.^7,8,9,10,11 However, to our knowledge, there has been no consistent confirmation of other loci.

Therapeutic efforts have begun to shift focus toward identifying and treating individuals in the preclinical phase of disease before onset of neurodegeneration and cognitive decline. Using a PET biomarker of brain amyloidosis to screen participants, the Anti-Amyloid Treatment in Asymptomatic Alzheimer Disease (A4 Study) clinical trial screened more than 4000 asymptomatic older individuals with amyloid PET imaging, of whom 1169 had elevated amyloid levels and were eligible for a prevention trial.^12,13 Clinical information and DNA from these at-risk, asymptomatic study participants provided an opportunity to identify novel genetic associations with brain amyloidosis during the preclinical phase of disease. In addition, the analyses of such data could provide insight into the mechanisms underlying cerebral amyloid accumulation.

Methods

In this genetic association study, participant data were acquired during the screening process in the A4 Study.^12,13 We also included other cohort studies: the Alzheimer Disease Neuroimaging Initiative (ADNI), the Berkeley Aging Cohort Study, the Wisconsin Registry for Alzheimer’s Prevention (WRAP), the Biomarkers of Cognitive Decline Among Normal Individuals: the BIOCARD cohort, and the Baltimore Longitudinal Study of Aging (BLSA). Vanderbilt University and Columbia University institutional review boards approved the data analyses. The present study was conducted from March 29, 2019, to February 19, 2020. This study followed the Strengthening the Reporting of Genetic Association Studies (STREGA) reporting guideline for genotyping, population stratification, haplotype modeling, Hardy-Weinberg equilibrium, and replication.¹⁴ We also describe how the participant data were selected, how quantitative traits were harmonized before analyses, the statistical methods used, and the sources of data.

The A4 Study clinical trial began screening in 2014, recruiting healthy adults aged 65 to 85 years with amyloid PET imaging.^12,13 The ADNI study was launched in 2003 and has included more than 1500 participants aged 55 to 90 years with normal cognition, mild cognitive impairment, or AD. In 2001, WRAP began recruiting participants aged 40 to 65 years who had a parent with autopsy-confirmed or clinically verified AD.^15,16 The BIOCARD study enrolled middle-aged participants who were cognitively intact; 75% of the participants had a first-degree relative with AD. The study began in 1995, stopped in 2005, and was reestablished in 2009, with annual clinical and cognitive assessments.¹⁷ The neuroimaging substudy of the BLSA began in 1994 and included participants without dementia aged 59 to 85 years who had up to 10 years of prospective data collection at baseline.¹⁸ Amyloid imaging with PET and carbon 11 Pittsburgh Compound B (C¹¹PiB) was introduced into the study in 2005.¹⁹ The Berkeley Aging Cohort Study began enrolling cognitively normal individuals recruited from the local community in 2005. For the amyloid PET imaging GWAS, we filtered each data set to individuals older than 50 years who had amyloid PET imaging (either C¹¹PiB or florbetapir) and genetic data available for analysis. Informed consent was obtained from participants in each study.

To validate genetic findings, we used autopsy data from the Religious Orders Study and Rush Memory and Aging Project (ROS/MAP), which were 2 harmonized longitudinal studies enrolling older adults without dementia who underwent annual clinical evaluations and organ donation at death.²⁰ Both studies were approved by an institutional review board of Rush University Medical Center. All participants in ROS/MAP signed an informed consent, an Anatomical Gift Act form, and a repository consent that allows their data to be repurposed. The Rush Alzheimer Disease Center resource sharing hub (https://www.radc.rush.edu/) and the Accelerating Medicines Partnership–AD Knowledge Portal (syn3219045) provided access to the data and are available on request with a data use agreement.

Genotyping was performed in each study on different platforms. Data from all cohorts underwent a quality control²¹ process to filter variants not successfully genotyped (missing >5%), out of Hardy-Weinberg equilibrium (P > 1 × 10⁻⁶), or with low minor allele frequency (<1%). Participants were excluded for poor genotypic efficiency (missing >1% of variants) if reported and genotyped sex differed if cryptic relatedness was identified (removed second-degree or closer relatives) or if large-scale differences in ethnicity/race were identified by principal component detection. After these filters, imputation was performed using the European samples from the HRC r1.1.2016 reference panel (Build 37 Assembly 19) and SHAPEIT phasing on the Michigan imputation server.²² Postimputation genotype data were filtered for imputation quality (R² >0.9) and minor allele frequency (<1%). A summary of the quality control process performed on each data set is reported in eTable 1 in the Supplement.

Amyloid PET Imaging Acquisition

Protocols for amyloid acquisition differed by site (eTable 2 in the Supplement). The A4 Study is a large, multisite trial with florbetapir F 18 (¹⁸F) amyloid PET imaging data acquired 50 to 70 minutes postinjection. ADNI ¹⁸F-florbetapir and C¹¹PiB data were acquired using a dynamic 3-dimensional scan on various scanner platforms with four 5-minute frames acquired 50 to 70 minutes postinjection. Berkeley C¹¹PiB data were acquired using a full dynamic protocol for 90 minutes (35 total frames) in a scanner (ECAT EXACT HR+ PET; Siemens). BIOCARD and BLSA C¹¹PiB data were acquired on a scanner (GE Advance; GE Healthcare) using a 70-minute dynamic protocol. Similarly, WRAP C¹¹PiB data were acquired on a scanner using a dynamic 70-minute protocol (ECAT EXACT HR+; Siemens). In all studies, images were reconstructed, averaged, spatially aligned, interpolated, and smoothed using study-specific pipelines. Mean standard uptake value ratio and distribution volume ratio calculations varied by site; all sites used whole or gray matter cerebellum as the reference region.

Harmonization of Amyloid Data

Harmonization was performed from composite cortical values within each site. To ensure that all amyloid values were on the same scale, we applied a gaussian mixture model²³ using a modification of a recently developed harmonization algorithm.²⁴ Gaussian mixture models were estimated among individuals who were cognitively normal within each cohort, and the mean (SD) was applied to the entire sample. In all cases, a 2-component model fit the data, confirming that global amyloid PET imaging followed a bimodal distribution reflecting amyloid-negative and amyloid-positive groups. Mean standard uptake value ratios were scaled and normalized using the mean and SD estimated from the predicted amyloid-negative gaussian distribution. The harmonization appropriately overlaid all data sets onto a common scale (eFigure 1 in the Supplement). As noted in the original harmonization manuscript, C¹¹PiB has a larger dynamic range compared with ¹⁸F-florbetapir ligands, including a higher ceiling and wider distribution, particularly among amyloid-positive individuals.²⁴ Consistently, we observed higher values among the harmonized C¹¹PiB samples. An alternative approach to harmonization is to use the characteristics of both gaussian distributions to transform all C¹¹PiB values to ¹⁸F-florbetapir values.²⁴ As a sensitivity analysis, we performed harmonization using this full transformation and compared results.

Data on RNA sequencing from the dorsolateral prefrontal cortex of individuals participating in ROS/MAP were used for validation of candidate genes from the GWAS analysis. Details of the RNA sequencing methods have been published previously.²⁵

Autopsy measures of β-amyloid were quantified in ROS/MAP using immunohistochemistry.²⁶ Immunohistochemistry estimates of amyloid (anti-Aβ) were quantified from 8 brain regions, including the angular gyrus, hippocampus, entorhinal, inferior temporal, calcarine, middle frontal, superior frontal, and anterior cingulate cortices.

In ROS/MAP, a comprehensive neuropsychological protocol was completed at each study visit. For the present analysis, we leveraged both a global composite measure of cognition, quantified previously based on z scores from 17 total tests that assess 5 different cognitive domains (semantic memory, episodic memory, perceptual orientation, perceptual speed, and working memory)²⁷ and the Mini-Mental State Examination.²⁸

Additional human brain tissues from Vanderbilt University Medical Center were obtained from decedents with AD (n = 5) and age-matched controls (n = 5) after approval of the Vanderbilt University Medical Institutional Review Board. Fixed tissue was sectioned at 50 μm on a vibratome (Leica Biosystems) to produce floating sections. Antigen retrieval was performed by heating sections to 95 °C in a borate buffer for 20 minutes. Sections were photobleached for 48 hours using a light-emitting diode microarry (HTG Supply), blocked in bovine serum albumin, 4%, and incubated with the primary antibody (anti-RBFOX1; Atlas, 1:100; Cathepsin B; R&D, 1:500; or pan-neurofilament; Biolegend, 1:150) overnight. After washing, sections were incubated with a conjugated secondary antibody (Alexa Fluor; Abcam, 1:1000) for 4 hours and then were washed, counterstained with methoxy-X04 (100 μM; Tocris) to identify amyloid-β and tau aggregates, and mounted to slides (Prolong Glass Antifade Mountant; Invitrogen). Images were produced on a laser scanning confocal microscope (LSM710; Zeiss) using ×20 or ×63 objectives and a minimum resolution of at least 1024 × 1024 pixels. Images then were processed (ImageJ).^29,30

Statistical Analysis

Genome-wide association studies were completed using PLINK, version 1.9³¹ and R, version 3.6.2 (R Project for Statistical Computing), with additive coding and the harmonized continuous amyloid PET metric set as a quantitative outcome. Genome-wide association studies were completed in each cohort separately. Covariates included age, sex, and the first 3 principal components to account for unmeasured population stratification. Meta-analyses of all results were performed using the inverse-weighted method in METAL.³² Results were restricted to variants present in all cohorts. Significance was set a priori to P = 5 × 10⁻⁸. The R packages EasyStrata,³³ qqman,³⁴ and Metafor³⁵ were used for data visualization, with additional variant-level visualization completed using LocusZoom.³⁶

We used RNA sequencing data from ROS/MAP to validate candidate genes or loci. First, we assessed the association between gene expression and amyloid-β using linear regression. Immunohistochemistry measures of amyloid-β were square root transformed before analysis. Covariates included age at death, sex, and postmortem interval. For analyses of longitudinal cognitive performance, we performed a mixed-effects regression model with the same covariates. The interval (years prior to death) and intercept were entered as both fixed and random effects in all longitudinal models.

Results

Clinical data for the 3154 individuals in the A4 Study included those whose race/ethnicity was determined genetically to be non-Hispanic white (n = 2960), African American (n = 89), and Hispanic (n = 105). In addition, 6 amyloid PET data sets with participants of non-Hispanic white ethnicity (n = 1160) were analyzed (Table 1). Together, the participants ranged from age 52 to 96 years; 2478 of the participants (57%) were women. With the exception of the 2 ADNI cohorts, 99% of the participants had normal cognition; with those cohorts added, cognition was normal in 90% of the participants. Analysis of variance of each demographic variable indicated significant differences across the cohorts (Table 1). For example, percent women (F_8,4305 = 10.8, P < .001), age (F_8,4305 = 58.5, P < .001), and percent APOE-positive (F_8,4305 = 3.2, P < .001) were significantly different between groups.

Table 1. Amyloid PET GWAS Participant Characteristics by Data Set.

Characteristic	Mean (SD)^a
Characteristic	A4 NHW	A4 AA	A4 Hispanic	ADNI	ADNI	Berkeley	BIOCARD	BLSA	WRAP
Amyloid acquisition	¹⁸F-florbetapir	¹⁸F-florbetapir	¹⁸F-florbetapir	¹⁸F-florbetapir	C¹¹PiB	C¹¹PiB	C¹¹PiB	C¹¹PiB	C¹¹PiB
No. of participants	2960	89	105	623	88	172	44	144	89
Women, No. (%)	1768 (60)	63 (71)	63 (60)	279 (45)	27 (31)	101 (59)	28 (64)	91 (63)	58 (65)
Normal cognition, No. (%)	2960 (100)	89 (100)	105 (100)	217 (33)	63 (72)	172 (100)	44 (100)	138 (96)	87 (98)
Age, y	71.4 (4.8)	70.3 (4.6)	71.9 (4.9)	74.6 (7.6)	76.5 (7.3)	74.4 (6.4)	70.8 (6.1)	77.2 (7.9)	67.3 (6.2)
APOE4 carriers, No. (%)	1057 (36)	33 (37)	33 (31)	255 (41)	45 (51)	48 (28)	14 (32)	39 (27)	35 (39)
Amyloid (standardized)	1.4 (2.5)	0.49 (1.5)	2.2 (4.5)	2.7 (3.4)	3.9 (3.0)	1.8 (4.1)	2.0 (4.5)	3.9 (6.4)	2.7 (5.1)
NC participants only	1.4 (2.5)	0.49 (1.5)	2.2 (4.5)	1.4 (2.8)	2.2 (2.6)	1.8 (4.1)	2.0 (4.5)	3.5 (6.1)	2.3 (4.7)
AD participants only	NA	NA	NA	5.2 (3.1)	5.1 (2.7)	NA	NA	15.3 (11.3)	NA

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Abbreviations: A4, Anti-Amyloid Treatment in Asymptomatic Alzheimer Disease screening cohort; AA, African American; AD, Alzheimer disease; ADNI, Alzheimer Disease Neuroimaging Initiative; BIOCARD, Biomarkers of Cognitive Decline Among Normal Individuals cohort; BLSA, Baltimore Longitudinal Study of Aging; GWAS, genome-wide association studies; NA, not applicable; NC, normal cognition; NHW, non-Hispanic white; PET, positron emission tomographic; C¹¹PiB, Pittsburgh Compound B; WRAP, Wisconsin Registry for Alzheimer’s Prevention.

^{^a}

Analysis of variance indicated significant differences (P < .001) across cohorts for all demographic categories.

Combining GWAS statistics and harmonized PET imaging amyloid data from each cohort, we completed a meta-analysis of all 6 studies to identify novel genetic associations with brain amyloid levels (n = 4314). We observed a robust association with brain amyloidosis at the APOE locus (top single-nucleotide variant [SNV; formerly SNP]: rs6857, β = 1.67, P = 5.79 × 10⁻¹³²), similar in magnitude to previous reports.^7,8,9,10,11 To determine whether other genes in the APOE region contributed to the association, we performed conditional analyses covarying for APOE ε4 and APOE ε2 status. All associations in the region were no longer significant (eFigure 2 in the Supplement).

We observed a novel risk locus on chromosome 16p.13.3 (top SNV: rs56081887, β = 0.61, P = 3 × 10⁻⁹) that included RBFOX1 (Figure 1A and B). Ten SNVs within RBFOX1 reached genome-wide significance in meta-analysis; the top 2 are displayed in Table 2. RBFOX1 variants were associated with increased amyloid levels in all data sets except for Hispanic individuals in the A4 Study (Figure 1C); however, the small sample size of the Hispanic cohort and the observation that a higher proportion of amyloid-positive individuals were Hispanic (40%) compared with the African American cohort (16%) precluded firm conclusions. All genome-wide significant SNVs in RBFOX1 were in moderate to high linkage disequilibrium (non-Hispanic white r² all >0.84; African American r² all >0.53). Results for all variants with P < 19 × 10⁻⁵ are presented in eTable 3 in the Supplement. The corresponding QQ-plot is presented in eFigure 3 in the Supplement. There was no compelling evidence for an interaction with APOE ε4. Results were consistent when applying the alternative harmonization algorithm.

Table 2. Top 2 Genome-Wide RBFOX1 Variants^a.

Single-nucleotide variant	Chr:BP	MAF	Gene	Function	Meta-analysis
Single-nucleotide variant	Chr:BP	MAF	Gene	Function	No.	β (95% CI)	P value
rs56081887	16:6903160	0.09	RBFOX1	Intron	4314	0.61 (0.41-0.81)	3 × 10⁹
rs34860942	16:6919189	0.09	RBFOX1	Intron	4314	0.59 (0.39-0.80)	8 × 10⁹

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Abbreviations: BP, base pair; Chr, chromosome; MAF, minor allele frequency.

^{^a}

Top 2 outside of APOE.

To validate and augment genetic findings, we analyzed RNA sequencing data from the prefrontal cortex in 600 individuals from the ROS/MAP study (Table 3). Lower levels of RBFOX1 messenger RNA (mRNA) in prefrontal cortex were associated with a higher amyloid β burden (β = −0.008, P = .002) (eFigure 4 in the Supplement). Associations remained significant when covarying for differences in cell type composition across samples (eTable 4 in the Supplement). Lower RBFOX1 mRNA levels were also associated with poorer global cognitive performance at the final visit before death (β = 0.007, P = .006) and a faster rate of global cognitive decline across all study visits (β = 0.001, P = 4 × 10⁻⁵) (eFigure 5 in the Supplement). Expression of RBFOX1 explained 1.5% of the variance in cognitive trajectories beyond covariates and remained statistically significant when covarying for amyloid and tau, which explained 5% and 15% of variance in cognitive trajectories, respectively. When assessing the results of the Mini-Mental State Examination for clinical interpretation, an SD decrease in RBFOX1 was associated with an annual 0.2-point decrease in the Mini-Mental State Examination score.

Table 3. ROS/MAP Participant Characteristics.

Characteristic	Brain tissue gene expression^a
No. of participants	600
Age at death, mean (SD), y	88.61 (6.64)
Education, mean (SD), y	16.48 (3.51)
Women, No. (%)	384 (64)
Non-Hispanic white, No. (%)	586 (98)
APOE4 carriers, No. (%)	151 (25)
AD, No. (%)	215 (36)
Postmortem interval, mean (SD), h	6.83 (4.86)

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Abbreviations: AD, Alzheimer disease; ROS/MAP, Religious Orders Study and Rush Memory and Aging Project.

^{^a}

Gene expression data were collected from prefrontal cortex tissue of participants from ROS/MAP. Brain amyloid levels were measured using immunohistochemistry.

In the microscopic evaluation, RBFOX1 protein localized to neurons in control brains and colocalized with neuropil threads inside dystrophic neurites surrounding amyloid plaques in AD brains (Figure 2). In addition, we observed some colocalization of RBFOX1 with neurofibrillary tangles in AD. Both observations support a potential role for RBFOX1 in AD pathogenesis.

Figure 2. — A, In postmortem control human brain tissue, *RBFOX* 1 (red) is localized to neurons (neurofilament, green). B, In Alzheimer disease brain, *RBFOX1* localizes to neuropil threads around β-amyloid plaques (methoxy-X04, blue). C, In Alzheimer disease brain, *RBFOX1* is present in tau tangles (arrowheads) and neuropil threads running through dystrophic neurites (cathepsin B, green) surrounding β-amyloid plaques (methoxy-X04, blue). Insets: cross-section through a dystrophic neurite showing lysosomes (green) surrounding a core of tau (blue) on which *RBFOX1* (red) is enriched. Scale bar is 20 μm. Width of inset box is 12 μm.

Discussion

The goal of this investigation was to examine the genetic basis of brain amyloidosis in preclinical AD. Using a collection of 6 publicly available data sets in a meta-analysis, we replicated the previously reported association between APOE and brain amyloidosis. In addition, we identified a novel locus on chromosome 16p13.3, RBFOX1, which encodes ataxin-2–binding protein, an RNA-binding protein. In support of the genetic findings reported herein, evidence for an association between variants in the RBFOX1 locus and AD were observed in an African American GWAS of AD (rs79537509, P = 5.3 × 10⁻⁷) (B. Kunkle, PhD, written communication, September 19, 2019), in a family-based study,³⁷ and in a study of cerebral glucose metabolism in ADNI.³⁸

Previous studies have used amyloid PET imaging to investigate the genetic basis of brain amyloidosis. A meta-analysis of 3 PET-PiB GWAS (n = 983) showed an association with APOE but no other genome-wide significant loci.⁸ In contrast, using ¹⁸F-florbetapir PET imaging within the ADNI cohort, 2 GWAS studies by Ramanan et al^10,11 reported associations between brain amyloidosis and APOE and 2 other loci in a cross-sectional and longitudinal analysis, respectively: BCHE (butyrylcholinesterase) and IL1RAP (interleukin-1 receptor accessory protein). Although we observed an association for the BCHE SNV (rs509208, P = .007), the association was solely driven by the ADNI cohort. Therefore, neither previous locus was detected in the present study. The small sample size of previous studies likely limited the ability to detect the association with RBFOX1.

RBFOX1 encodes an RNA-binding protein expressed in muscle, heart, and neurons and is a member of the evolutionarily conserved Fox-1 family of RNA-binding proteins that bind to ataxin-2 and regulate alternative splicing.³⁹ In addition, mammalian RBFOX1 is present in the cytoplasm where it binds to 3 prime untranslated regions of multiple mRNAs, regulating their stability.⁴⁰ RBFOX1 is a highly conserved protein that can regulate splicing and transcriptional networks in human neuronal development, particularly in neuronal migration and synapse network formation within the cerebral cortex.^40,41 In addition to a potential role as the binding protein for ataxin-2 in spinocerebellar ataxia type 2, deletions and other structural variants in the RBFOX1 gene increase the risk of generalized epilepsy, intellectual disability, autism spectrum disorder, and developmental disorders associated with aggression.^42,43,44

While the exact mechanisms relating dysfunctional human RBFOX proteins with various neuropsychiatric disorders are not fully understood, there is evidence for multiple possible molecular causal pathways. Downregulation of RBFOX1 leads to destabilization of both nuclear and cytoplasmic mRNAs encoding for synaptic transmission proteins and loss of synaptic function in AD.^45,46 RBFOX1 may regulate alternative splicing of APP,⁴⁷ which may be particularly relevant to the amyloid associations observed in the present analysis. Alternatively, downregulation of RBFOX1 in AD may directly affect the stability and abundance of mRNAs that encode synaptic transmission proteins.⁴⁵ Furthermore, because FOX1 and ataxin-2 are also present in the trans-Golgi network, a trafficking or recycling mechanism might be implicated. Clearly, additional experimental work will be needed to clarify the potential role of RBFOX1 in brain amyloidosis and AD dementia. Aberrant colocalization of disease-associated proteins has been previously reported in other neurodegenerative diseases, such as the TDP-43 protein in amyotrophic lateral sclerosis and frontotemporal lobar degeneration.⁴⁸ We found colocalization of the RBFOX1 protein not only just around amyloid plaques but also with neurofibrillary tangles. These results imply that the protein may play a general role in AD-related proteinopathy.

We also observed associations between variants in the APOE region and brain amyloidosis, consistent with previous reports leveraging autopsy measures of neuropathologic characteristics,⁴⁹ cerebrospinal fluid biomarkers of amyloidosis,⁵⁰ and PET biomarkers of amyloidosis.^8,10,11 The locus surrounding APOE, chromosome 19q13.32, includes a number of potential genes, such as TOMM40, APOC1, and PVRL2 (eFigure 2 in the Supplement), but conditional analyses indicated that the genetic association was driven by APOE. APOE is thought to relate to AD through an amyloid clearance pathway, with APOE ε4 associated with earlier deposition of amyloid even during preclinical stages of disease.

Strengths and Limitations

The strengths of this study include the large sample size, the number of asymptomatic individuals allowing a focus on preclinical disease, and comprehensive validation analyses at the RNA and protein level. Study limitations include clinical heterogeneity across studies, overrepresentation of non-Hispanic white women with high levels of education, and our reliance on harmonized data acquired on different scanners and processed in different ways. Although we limited these factors statistically when possible, residual confounding cannot be ruled out.

Conclusions

To our knowledge, this is the largest GWAS of PET amyloid imaging; we report a novel genetic risk locus for brain amyloidosis within RBFOX1. Additional evidence at the transcript and protein level may further implicate RBFOX1 as a novel genetic risk locus for brain amyloidosis and a candidate for early progression in AD.

Supplement.

eTable 1. Summary of Imputation and Quality Control Measures Performed on Each Data set

eTable 2. Summary of Protocols for Amyloid Acquisition by Site

eTable 3. Top PET Amyloid Meta-analysis GWAS Associations (P < 1e-5)

eTable 4. RBFOX1 Brain Expression Associations With Amyloid Burden Adjusted for Cell-Type Composition

eFigure 1. Density Histogram of Normalized Amyloid PET Measures per Study

eFigure 2. Locus Zoom Plots of the APOE Region in Conditional Analyses

eFigure 3. QQ-Plot for the Meta-analysis of Amyloid PET Across All Cohorts

eFigure 4. Lower RBFOX1 Expression in the Prefrontal Cortex Was Associated With Higher Amyloid Burden

eFigure 5. Lower RBFOX1 Expression in the Prefrontal Cortex Was Associated With a Faster Rate of Global Cognitive Decline

Click here for additional data file.^{(1.7MB, pdf)}

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