Early-Onset Alzheimer Disease and Candidate Risk Genes Involved in Endolysosomal Transport

Brian W Kunkle; Badri N Vardarajan; Adam C Naj; Patrice L Whitehead; Sophie Rolati; Susan Slifer; Regina M Carney; Michael L Cuccaro; Jeffery M Vance; John R Gilbert; Li-San Wang; Lindsay A Farrer; Christiane Reitz; Jonathan L Haines; Gary W Beecham; Eden R Martin; Gerard D Schellenberg; Richard P Mayeux; Margaret A Pericak-Vance

doi:10.1001/jamaneurol.2017.1518

. 2017 Sep 11;74(9):1113–1122. doi: 10.1001/jamaneurol.2017.1518

Early-Onset Alzheimer Disease and Candidate Risk Genes Involved in Endolysosomal Transport

Brian W Kunkle ¹, Badri N Vardarajan ^2,^3,⁴, Adam C Naj ⁵, Patrice L Whitehead ¹, Sophie Rolati ¹, Susan Slifer ¹, Regina M Carney ¹, Michael L Cuccaro ¹, Jeffery M Vance ¹, John R Gilbert ¹, Li-San Wang ⁶, Lindsay A Farrer ^7,^8,^9,^10,¹¹, Christiane Reitz ^2,^3,⁴, Jonathan L Haines ¹², Gary W Beecham ¹, Eden R Martin ¹, Gerard D Schellenberg ⁶, Richard P Mayeux ^2,^3,^4,^13,¹⁴, Margaret A Pericak-Vance ^1,^✉

¹John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, Florida

²The Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, New York, New York

³The Gertrude H. Sergievsky Center, Columbia University, New York, New York

⁴Department of Neurology, College of Physicians and Surgeons, Columbia University, New York, New York

⁵Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia

⁶Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia

⁷Department of Medicine (Biomedical Genetics), Schools of Medicine and Public Health, Boston University, Boston, Massachusetts

⁸Department of Neurology, Schools of Medicine and Public Health, Boston University, Boston, Massachusetts

⁹Department of Ophthalmology, Schools of Medicine and Public Health, Boston University, Boston, Massachusetts

¹⁰Department of Epidemiology, Schools of Medicine and Public Health, Boston University, Boston, Massachusetts

¹¹Department of Biostatistics, Schools of Medicine and Public Health, Boston University, Boston, Massachusetts

¹²Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio

¹³Department of Epidemiology, College of Physicians and Surgeons, Columbia University, New York, New York

¹⁴Department of Psychiatry, College of Physicians and Surgeons, Columbia University, New York, New York

Accepted for Publication: May 19, 2017.

^✉

Corresponding Author: Margaret A. Pericak-Vance, PhD, John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, 1501 NW 10th Ave, Miami, FL 33136 (mpericak@med.miami.edu).

Published Online: July 24, 2017. doi:10.1001/jamaneurol.2017.1518

Author Contributions: Drs Vardarajan and Kunkle contributed equally to this work. Drs Kunkle and Pericak-Vance had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Kunkle, Vardarajan, Haines, Mayeux, Pericak-Vance.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Kunkle, Vardarajan, Carney, Mayeux, Pericak-Vance.

Critical revision of the manuscript for important intellectual content: Kunkle, Vardarajan, Naj, Whitehead, Rolati, Slifer, Cuccaro, Vance, Gilbert, Wang, Farrer, Reitz, Haines, Beecham, Martin, Schellenberg, Mayeux, Pericak-Vance.

Statistical analysis: Kunkle, Vardarajan, Naj, Slifer, Haines, Beecham, Martin, Pericak-Vance.

Obtained funding: Farrer, Haines, Schellenberg, Mayeux, Pericak-Vance.

Administrative, technical, or material support: Carney, Gilbert, Wang, Farrer, Schellenberg, Mayeux, Pericak-Vance.

Supervision: Carney, Gilbert, Farrer, Haines, Schellenberg, Mayeux, Pericak-Vance.

Conflict of Interest Disclosures: None reported.

Funding/Support: This study was supported by grant W81XWH-12-1-0013 from the Department of Defense. The National Institutes of Health, National Institute on Aging (NIA) supported this work through Alzheimer Disease Genetic Consortium (ADGC) grants U01 AG032984 and RC2 AG036528; samples from the National Cell Repository for Alzheimer’s Disease, which receives government support under cooperative agreement grant U24 AG21886 awarded by the NIA, were used in this study. Data for this study were prepared, archived, and distributed by the National Institute on Aging Alzheimer’s Disease Data Storage Site at the University of Pennsylvania (U24-AG041689-01); The Center for Genomics of Alzheimer’s Disease at the University of Pennsylvania (U54-AG052427); Alzheimer’s Disease Core Center at the University of Pennsylvania (P30-AG010124); National Alzheimer’s Coordinating Center, U01 AG016976; NIA LOAD (Columbia University), UF1AG047133, RF1AG015473, U01AG052410-01, U24 AG026395, U24 AG026390, R01AG041797; Banner Sun Health Research Institute P30 AG019610; Boston University, P30 AG013846, U01 AG10483, R01 CA129769, R01 MH080295, R01 AG017173, R01 AG025259, R01 AG048927, R01AG33193, R01 AG009029; Columbia University, P50 AG008702, R37 AG015473, R01 AG037212, R01 AG028786; Duke University, P30 AG028377, AG05128; Emory University, AG025688; Group Health Research Institute, UO1 AG006781, UO1 HG004610, UO1 HG006375, U01 HG008657; Indiana University, P30 AG10133, R01 AG009956, RC2 AG036650; The Johns Hopkins University, P50 AG005146, R01 AG020688; Massachusetts General Hospital, P50 AG005134; Mayo Clinic, P50 AG016574, R01 AG032990, KL2 RR024151; Mount Sinai School of Medicine, P50 AG005138, P01 AG002219; New York University, P30 AG08051, UL1 RR029893, 5R01AG012101, 5R01AG022374, 5R01AG013616, 1RC2AG036502, 1R01AG035137; North Carolina A&T University, P20 MD000546, R01 AG28786-01A1; Northwestern University, P30 AG013854; Oregon Health & Science University, P30 AG008017, R01 AG026916; Rush University, P30 AG010161, R01 AG019085, R01 AG15819, R01 AG17917, R01 AG030146, R01 AG01101, RC2 AG036650, R01 AG22018; University of Alabama at Birmingham, P50 AG016582; University of Arizona, R01 AG031581; University of California, Davis, P30 AG010129; University of California, Irvine, P50 AG016573; University of California, Los Angeles, P50 AG016570; University of California, San Diego, P50 AG005131; University of California, San Francisco, P50 AG023501, P01 AG019724; University of Kentucky, P30 AG028383, AG05144; University of Michigan, P50 AG008671; University of Pennsylvania, P30 AG010124; University of Pittsburgh, P50 AG005133, AG030653, AG041718, AG07562, AG02365; University of Southern California, P50 AG005142; University of Texas Southwestern, P30 AG012300; University of Miami, R01 AG027944, AG010491, AG027944, AG021547, AG019757; University of Washington, P50 AG005136, R01 AG042437; University of Wisconsin, P50 AG033514; Vanderbilt University, R01 AG019085; and Washington University, P50 AG005681, P01 AG03991, P01 AG026276. The Kathleen Price Bryan Brain Bank at Duke University Medical Center is funded by NINDS grant NS39764, NIMH MH60451, and by GlaxoSmithKline. Support was also received from the Alzheimer’s Association (Dr Farrer, IIRG-08-89720; Dr Pericak-Vance, IIRG-05-14147), the US Department of Veterans Affairs Administration, Office of Research and Development, Biomedical Laboratory Research Program, and BrightFocus Foundation (Dr Pericak-Vance, A2111048). The Netherlands Brain Bank (funding via numerous sources, including Stichting MS Research, Brain Net Europe, Hersenstichting Nederland Breinbrekend Werk, International Parkinson Fonds, Internationale Stichting Alzheimer Onderzoek), and Institut de Neuropatologia, Servei Anatomia Patologica, Universitat de Barcelona.

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

Additional Contributions: We thank all research participants for participating in this work and their Alzheimer’s Disease Genetics Consortium. Contributors included Perrie M. Adams, PhD (University of Texas Southwestern Medical Center), Erin Abner, PhD (University of Kentucky, Marilyn S. Albert, PhD (The Johns Hopkins University), Roger L. Albin, MD (University of Michigan), Liana G. Apostolova, MD (University of Indiana), Steven E. Arnold, MD (University of Pennsylvania), Sanjay Asthana, MD (University of Wisconsin), Craig S. Atwood, PhD (University of Wisconsin), Clinton T. Baldwin, PhD (Boston University), Robert C. Barber, PhD (University of North Texas Health Science Center), Michael M. Barmada, PhD (University of Pittsburgh), Lisa L. Barnes, PhD (Rush University), Sandra Barral, PhD (Columbia University), Thomas G. Beach MD, PhD (Banner Sun Health Research Institute), James T. Becker, PhD (University of Pittsburgh), Duane Beekly, BS (University of Washington), Timothy W. Behrens, MD (Genentech), David A. Bennett, MD (Rush University Medical Center), Eileen H. Bigio, MD (Northwestern University), Thomas D. Bird, MD (University of Washington), Deborah Blacker, MD (Massachusetts General Hospital/Harvard Medical School), Bradley F. Boeve, MD (Mayo Clinic), James D. Bowen, MD (Swedish Medical Center), Adam Boxer, MD, PhD (University of California, San Francisco), James R. Burke, MD, PhD (Duke University), Jeffrey M. Burns, MD, MS (University of Kansas Medical Center), Joseph D. Buxbaum, PhD (Mt Sinai School of Medicine), Nigel J. Cairns, PhD, FRCPath (Washington University), Laura B. Cantwell, MPH (University of Pennsylvania), Chuanhai Cao, PhD (University of South Florida), Chris S. Carlson, PhD (Fred Hutchinson Cancer Research Center), Cynthia M. Carlsson, MD (University of Wisconsin), Minerva M. Carrasquillo, PhD (Mayo Clinic), Helena C. Chui, MD (University of South California), Paul K. Crane, MD, PhD (University of Washington), David H. Cribbs, PhD (University of California, Irvine), Elizabeth A. Crocco, MD (University of Miami), Carlos Cruchaga, PhD (Washington University), Charles DeCarli, MD (University of California Davis), Philip L. De Jager, MD, PhD (Broad Institute), F. Yesim Demirci, MD (University of Pittsburgh), Malcolm Dick, MD (University of California Irvine), Dennis W. Dickson, MD (Mayo Clinic), Beth Dombroski, PhD (University of Pennsylvania), Rachelle S. Doody, MD, PhD (Baylor College of Medicine), Ranjan Duara, MD (Mt Sinai Medical Center), Nilufer Ertekin-Taner, MD, PhD (Mayo Clinic), Denis A. Evans, MD (Rush University Medical Center), Kelley M. Faber, MS (Indiana University), Thomas J. Fairchild, PhD (University of Texas North Health Science Center), Kenneth B. Fallon, MD (University of Alabama, Birmingham), David W. Fardo, PhD (University of Kentucky), Martin R. Farlow, MD (Indiana University), Steven Ferris, PhD (New York University), Tatiana M. Foroud, PhD (Indiana University), Matthew P. Frosch, MD, PhD (Massachusetts General Hospital), Douglas R. Galasko, MD (University of California, San Diego), Marla Gearing, PhD (Emory University), Peter St George-Hyslop, MD, FRCP (University of Toronto), Daniel H. Geschwind, MD, PhD (University of California, Los Angeles), Bernardino Ghetti, MD (Indiana University), Alison M. Goate, DPhil (Mt Sinai School of Medicine), Neill R. Graff-Radford, MD (Mayo Clinic), Robert R. Graham, PhD (Genetech), Robert C. Green, MD, MPH (Brigham and Women’s Medical Center/Harvard Medical School), John H. Growdon, MD (Massachusetts General Hospital/Harvard Medical School), Hakon Hakonarson, MD, PhD (Children’s Hospital of Philadelphia), Ronald L. Hamilton, MD (University of Pittsburgh), Kara L. Hamilton-Nelson, MPH (University of Miami), John Hardy, PhD (University College London), Lindy E. Harrell, MD, PhD (University of Alabama, Birmingham), Lawrence S. Honig, MD, PhD (Columbia University), Ryan M. Huebinger, PhD (University of Texas Southwestern Medical Center), Matthew J. Huentelman, PhD (Translational Genomics Research Institute), Christine M. Hulette, MD (Duke University), Bradley T. Hyman, MD, PhD (Massachusetts General Hospital/Harvard Medical School), Gail P. Jarvik, MD, PhD (University of Washington), Lee-Way Jin, MD, PhD (University of California Davis), Gyungah Jun, PhD (Boston University), M. Ilyas Kamboh, PhD (University of Pittsburgh), Mindy J. Katz, MPH (Albert Einstein College of Medicine), Anna Karydas, BA (University of California, San Francisco), John S. K. Kauwe, PhD (Brigham Young University), Jeffrey A. Kaye, MD (Oregon Health and Science University), C. Dirk Keene, MD, PhD (University of Washington), Ronald Kim, MD (University of California Irvine), Neil W. Kowall, MD (Boston University), Joel H. Kramer, PsyD (University of California, San Francisco), Walter A. Kukull, PhD (University of Washington), Frank M. LaFerla PhD (University of California Irvine), James J. Lah, MD, PhD (Emory University), Eric B. Larson, MD, PhD (University of Washington), James B. Leverenz, MD (Cleveland Clinic), Allan I. Levey, MD, PhD (Emory University), Ge Li, MD, PhD (University of Washington), Andrew P. Lieberman, MD, PhD (University of Michigan), Richard B. Lipton, MD (Albert Einstein College of Medicine), Kathryn L. Lunetta, PhD (Boston University), Constantine G. Lyketsos, MD, MHS (The Johns Hopkins University), John Malamon, MSE (University of Pennsylvania), Daniel C. Marson, JD, PhD (University of Alabama, Birmingham), Frank Martiniuk, PhD (New York University), Deborah C. Mash, PhD (University of Miami), Eliezer Masliah, MD (University of California, San Diego), Wayne C. McCormick, MD, PhD (University of Washington), Susan M. McCurry, PhD (University of Washington), Andrew N. McDavid, BA (Fred Hutchinson Cancer Research Center), Stefan McDonough, PhD (Pfizer Worldwide Research and Development), Ann C. McKee, MD (Boston University), Marsel Mesulam, MD (Northwestern University), Bruce L. Miller, MD (University of California, San Francisco), Carol A. Miller, MD (University of Southern California), Joshua W. Miller, PhD (University of California Davis), Thomas J. Montine, MD, PhD (University of Washington), John C. Morris, MD (Washington University), Shubhabrata Mukherjee, PhD (University of Washington), Amanda J. Myers, PhD (University of Miami), Sid O’Bryant, PhD (University of North Texas Health Science Center), John M. Olichney, MD (University of California Davis), Joseph E. Parisi, MD (Mayo Clinic), Amanda Partch, MS (University of Pennsylvania), Henry L. Paulson, MD, PhD (University of Michigan), William Perry, MPH (University of Miami), Elaine Peskind, MD (University of Michigan), Ronald C. Petersen, MD, PhD (Mayo Clinic), Aimee Pierce, MD (University of California Irvine), Wayne W. Poon, PhD (University of California Irvine), Huntington Potter, PhD (University of Colorado), Liming Qu, MS (University of Washington), Joseph F. Quinn, MD (Oregon Health & Science University), Ashok Raj, MD (University of South Florida), Murray Raskind, MD (University of Washington), Eric M. Reiman, MD (Arizona Alzheimer’s Consortium), Barry Reisberg, MD (New York University), Joan S. Reisch, PhD (University of Texas Southwestern Medical Center), John M. Ringman, MD (UCLA), Erik D. Roberson, MD, PhD (University of Alabama, Birmingham), Ekaterina Rogaeva, PhD (University of Toronto), Howard J. Rosen, MD (University of California, San Francisco), Roger N. Rosenberg, MD (University of Texas Southwestern), Donald R. Royall, MD (South Texas Veterans Health Administration Research Education & Clinical Center), Mark A. Sager, MD (University of Wisconsin), Mary Sano, PhD (Mt Sinai School of Medicine), Andrew J. Saykin, PsyD (Indiana University), Julie A. Schneider, MD (Rush University), Lon S. Schneider, MD (University of Southern California), William W. Seeley, MD (University of California, San Francisco), Amanda G. Smith, MD (University of South Florida), Joshua A. Sonnen, MD (University of Washington), Salvatore Spina, MD (Indiana University), Robert A. Stern, PhD (Boston University), Russell H. Swerdlow, MD (University of Kansas Medical Center), Rudolph E. Tanzi, PhD (Massachusetts General Hospital/Harvard Medical School), Tricia A. Thornton-Wells, PhD (Novartis Institutes for Biomedical Research), John Q. Trojanowski, MD, PhD (University of Pennsylvania), Juan C. Troncoso, MD (The Johns Hopkins University), Debby W. Tsuang, MD (University of Washington), Otto Valladares, MS (University of Pennsylvania), Vivianna M. Van Deerlin, MD, PhD (University of Pennsylvania), Linda J. Van Eldik, PhD (University of Kentucky), Harry V. Vinters, MD (UCLA), Jean Paul Vonsattel, MD (Columbia University), Sandra Weintraub, PhD (Northwestern University), Kathleen A. Welsh-Bohmer, PhD (Duke University), Kirk C. Wilhelmsen, MD, PhD (University of North Carolina), Jennifer Williamson, MS (Columbia University), Thomas S. Wingo, MD (Emory University), Randall L. Woltjer, MD, PhD (Oregon Health & Science University), Clinton B. Wright, MD, MS (University of Miami), Chuang-Kuo Wu, MD, PhD (Texas Tech University Health Science Center), Steven G Younkin, MD, PhD (Mayo Clinic), Chang-En Yu, PhD (University of Washington), Lei Yu, PhD (Rush University Medical Center), and Yi Zhao, MS (University of Pennsylvania). We thank D. Stephen Snyder, PhD, and Marilyn Miller, PhD (NIA), who are ex officio Alzheimer’s Disease Genetics Consortium members.

^✉

Corresponding author.

PMCID: PMC5691589 PMID: 28738127

Key Points

Question

Are there additional rare variants that contribute to the risk of early-onset Alzheimer disease?

Findings

This case-control study of whole-exome sequencing of 93 patients within early-onset Alzheimer disease cases followed by testing of candidate risk variants found an association between several endolysosomal-related variants and genes with early-onset and late-onset Alzheimer disease. These included suggestive evidence of association for variants in the genes RIN3 and RUFY1, a significant association with a variant in TCIRG1, and a significant gene-based association with PSD2.

Meaning

This study highlights the involvement of additional endolysosomal genes in the risk of both early- and late-onset Alzheimer disease.

Abstract

Importance

Mutations in APP, PSEN1, and PSEN2 lead to early-onset Alzheimer disease (EOAD) but account for only approximately 11% of EOAD overall, leaving most of the genetic risk for the most severe form of Alzheimer disease unexplained. This extreme phenotype likely harbors highly penetrant risk variants, making it primed for discovery of novel risk genes and pathways for AD.

Objective

To search for rare variants contributing to the risk for EOAD.

Design, Setting, and Participants

In this case-control study, whole-exome sequencing (WES) was performed in 51 non-Hispanic white (NHW) patients with EOAD (age at onset <65 years) and 19 Caribbean Hispanic families previously screened as negative for established APP, PSEN1, and PSEN2 causal variants. Participants were recruited from John P. Hussman Institute for Human Genomics, Case Western Reserve University, and Columbia University. Rare, deleterious, nonsynonymous, or loss-of-function variants were filtered to identify variants in known and suspected AD genes, variants in multiple unrelated NHW patients, variants present in 19 Hispanic EOAD WES families, and genes with variants in multiple unrelated NHW patients. These variants/genes were tested for association in an independent cohort of 1524 patients with EOAD, 7046 patients with late-onset AD (LOAD), and 7001 cognitively intact controls (age at examination, >65 years) from the Alzheimer’s Disease Genetics Consortium. The study was conducted from January 21, 2013, to October 13, 2016.

Main Outcomes and Measures

Alzheimer disease diagnosed according to standard National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer Disease and Related Disorders Association criteria. Association between Alzheimer disease and genetic variants and genes was measured using logistic regression and sequence kernel association test–optimal gene tests, respectively.

Results

Of the 1524 NHW patients with EOAD, 765 (50.2%) were women and mean (SD) age was 60.0 (4.9) years; of the 7046 NHW patients with LOAD, 4171 (59.2%) were women and mean (SD) age was 77.4 (8.6) years; and of the 7001 NHW controls, 4215 (60.2%) were women and mean (SD) age was 77.4 (8.6) years. The gene PSD2, for which multiple unrelated NHW cases had rare missense variants, was significantly associated with EOAD (P = 2.05 × 10⁻⁶; Bonferroni-corrected P value [BP] = 1.3 × 10⁻³) and LOAD (P = 6.22 × 10⁻⁶; BP = 4.1 × 10⁻³). A missense variant in TCIRG1, present in a NHW patient and segregating in 3 cases of a Hispanic family, was more frequent in EOAD cases (odds ratio [OR], 2.13; 95% CI, 0.99-4.55; P = .06; BP = 0.413), and significantly associated with LOAD (OR, 2.23; 95% CI, 1.37-3.62; P = 7.2 × 10⁻⁴; BP = 5.0 × 10⁻³). A missense variant in the LOAD risk gene RIN3 showed suggestive evidence of association with EOAD after Bonferroni correction (OR, 4.56; 95% CI, 1.26-16.48; P = .02, BP = 0.091). In addition, a missense variant in RUFY1 identified in 2 NHW EOAD cases showed suggestive evidence of an association with EOAD as well (OR, 18.63; 95% CI, 1.62-213.45; P = .003; BP = 0.129).

Conclusions and Relevance

The genes PSD2, TCIRG1, RIN3, and RUFY1 all may be involved in endolysosomal transport—a process known to be important to development of AD. Furthermore, this study identified shared risk genes between EOAD and LOAD similar to previously reported genes, such as SORL1, PSEN2, and TREM2.

This case-control study conducts whole-exome sequencing to evaluate endolysosomal-related variants and genes in early-onset Alzheimer disease.

Introduction

Early-onset Alzheimer disease (EOAD), commonly defined as having age-at-onset (AAO) AD before age 65 years, accounts for approximately 10% of all cases of AD. Rare mutations (minor allele frequency <0.001) in APP (351 Entrez Gene), PSEN1 (5663 Entrez Gene), and PSEN2 (5664 Entrez Gene) are the main genetic risk factors for EOAD, which has a prevalence estimated as 54 per 100 000 individuals aged 30 to 65 years, and 98 per 100 000 of those aged 45 to 64 years. The highly penetrant mutations in these genes account for 60% to 70% of familial EOAD and 5% to 10% of EOAD overall, leaving the majority of genetic risk for this most severe form of AD unexplained. Identifying additional loci harboring highly penetrant, rare risk variants for EOAD has been challenging, although research implicates late-onset AD (LOAD) risk genes, such as SORL1 (6653 Entrez Gene) and TREM2 (54209 Entrez Gene), in the development of EOAD, highlighting the potential for shared genes and pathways between the early and late forms of AD. This shared genetic architecture is likely, given their similar pathology and the arbitrary nature of the commonly used criterion of AAO younger than 65 years delineating EOAD from LOAD.

Analysis of EOAD, which has a strong genetic component, should enhance identification of additional AD risk loci as these cases likely harbor rare, highly penetrant risk variants for disease, whereas the more common late-onset phenotype is expected to have a more complex genetic architecture. Following this hypothesis, we performed whole-exome sequencing (WES) in 51 non-Hispanic white (NHW) individuals with EOAD (previously screened negative for known EOAD risk variants in APP, PSEN1, and PSEN2) to search for rare variants contributing to the risk for EOAD. Variant filtering for heterozygous functional rare variants was performed to identify high-priority variants and genes. Identified candidate variants and genes underwent additional testing in large EOAD and LOAD case-control data sets.

Methods

WES of EOAD Cases

Selection of EOAD Cases for Sequencing

Familial and sporadic NHW patients with EOAD with AAO younger than 65 years (mean, 54 years; range, 44-64 years) and thus potentially fitting the profile of either APP, PSEN1, or PSEN2 cases were sequenced for established mutations in these genes on ascertainment to eliminate individuals with known causative genetic factors. Individuals with apolipoprotein E (APOE) (348 Entrez Gene) ε4/4 status, which can exhibit AAO as early as 65 years, were also excluded from sequencing. In total, 51 NHW patients with EOAD were selected for WES from the John P. Hussman Institute for Human Genomics and Case Western Reserve University Alzheimer Disease Cohort (eTable 1 in the Supplement provides details). The study was conducted from January 21, 2013, to October 13, 2016. All cognitively impaired individuals, including any who changed affection status, were evaluated by the John P. Hussman Institute for Human Genomics AD clinical staff, which includes 3 of us: a geriatric psychiatrist (R.M.C.), a neurologist (J.M.V.), and a neuropsychologist (M.L.C.). In addition, 53 individuals (42 with EOAD; 11 unaffected individuals), from 19 Caribbean Hispanic families were selected for WES with mean AAO of 55 years. These families were screened for the absence of APP, PSEN1, PSEN2, MAPT (4137 Entrez Gene), and GRN (2896 Entrez Gene) mutations (eTable 1 in the Supplement). All affected individuals met the internationally recognized standard National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer Disease and Related Disorders Association (NINCDS/ADRA) criteria for AD. The institutional review boards at University of Miami’s Human Subject Research Office, Columbia University’s Human Research Protection Office, and Vanderbilt University Medical Center approved all study procedures, and written informed consent was obtained from all study participants; the participants received financial compensation.

WES, Variant Calling and Quality Control

Variants were normalized using BCFTools and variants with read depth less than 10, variant quality score log-odds less than 0, genotype quality less than 20, and 20 base pair genome mappability scores less than 1 from the Duke Uniqueness Track were removed from further analysis. Reported variants were confirmed with Sanger sequencing. Further details of the WES protocol can be found in the eMethods in the Supplement.

WES Variant Prioritization

Variant Filtering for Rare Nonsynonymous or Loss-of-Function Variants

Filtering of WES variants prioritized for follow-up association testing was performed using KGGSeq and custom perl and bash scripts. Nonsynonymous or loss-of-function variants with a global minor allele frequency of 0.001 or less that were in a heterozygous state and showed autosomal-dominant or X-linked dominant segregation in families, or existed in a heterozygous state in nonfamilial cases, were selected (Figure 1). Deleteriousness of these variants was assessed using Combined Annotation Dependent Depletion (CADD) scores. A detailed description of the filtering steps implemented, including the assumptions behind our choice of minor allele frequency cutoff based on a maximum allele frequency calculation, is provided in the eMethods in the Supplement.

Figure 1. — ADGC indicates Alzheimer Disease Genetics Consortium; CADD, Combined Annotation Dependent Depletion; EOAD, early-onset AD; GDI, gene damaging index; GQ, genotype quality; GWAS, genome-wide association study; HIHG, John P. Hussman Institute for Human Genomics; IBD, identical by dissent; LOAD, late-onset AD; MAF, minor allele frequency; VQSLOD, variant quality score log-odds; WES, whole-exome sequencing.

^aUse familiar segregation and IBD sharing if available.

Prioritization of Variants in Known or Suspected AD Genes

After filtering on the criteria above, we first investigated variation in well-established and recently associated EOAD risk genes (APP, PSEN1, PSEN2, SORL1, and TREM2) and genes previously linked to EOAD (MAPT and GRN), genes within significant genome-wide association study loci (defined as the 21 loci from Lambert et al, which is the largest LOAD genome-wide association study to date), and genes with rare variants recently associated with LOAD through sequencing studies (PLD3 [24646 Entrez Gene], UNC5C [8633 Entrez Gene], and AKAP9 [10142 Entrez Gene]). Clinical significance of identified variants in these genes was assessed using the ClinVar Database on June 29, 2015. Cases carrying known pathogenic variants were excluded from further analysis for novel genes.

Prioritization of Variants in Novel Candidate Genes

The remaining variants in individuals without a known pathogenic mutation were then prioritized for follow-up in the Alzheimer Disease Genetic Consortium (ADGC) EOAD association study, using the following criteria:

Rare nonsynonymous or loss-of-function variants in multiple NHW unrelated patients or families.
Rare, deleterious nonsynonymous or loss-of-function variants in the same gene in multiple NHW unrelated patients or families.
Variants shared between NHW patients with EOAD and 19 Hispanic EOAD families.

Association Testing of Prioritized Variants and Genes

Prioritized variants and genes, both known and novel, were tested for association with EOAD and LOAD in a large case-control cohort from the ADGC.

Cohort Description

A total of 1524 NHW patients with EOAD (AAO≤65 years), 7046 NHW patients with LOAD (AAO>65 years), and 7001 NHW individuals serving as controls (mean age at examination, 77.4 years) from the ADGC had Illumina HumanExome1.0 array data available for analysis. Although EOAD is traditionally defined as having AAO younger than 65 years, we included individuals 65 years in the EOAD cohort to increase our sample size and power. Of the 1524 NHW patients with EOAD, 765 (50.2%) were women and mean (SD) age was 60.0 (4.9) years; of the 7046 NHW patients with LOAD, 4171 (59.2%) were women and mean age was 77.4 (8.6) years; and of the 7001 NHW controls, 4215 (60.2%) were women and mean age was 77.4 (8.6) years.

Samples from several ADGC cohorts were genotyped at 4 sites, including (1) the Center for Applied Genomics, The Children’s Hospital of Pennsylvania, Philadelphia, Pennsylvania (2) Washington University, St Louis, Missouri (3) the Center for Genome Technology, John P. Hussman Institute for Human Genomics, University of Miami, Miami, Florida, and (4) the Robert S. Boas Center for Genomics and Human Genetics, Feinstein Institute for Medical Research, Manhasset, New York (Northshore) (eTable 2 in the Supplement). The ADGC received approval for analysis and use of data from the University of Pennsylvania Institutional Review Board. Participants’ written or oral consents were obtained by their originating studies. A detailed description of ascertainment and the collection of genotype and phenotype data in the individual data sets of the ADGC is available in Naj et al and Sims et al. All affected individuals were adjudicated as possible or probable AD prior to analyses according to NINCDS/ADRDA criteria.

Single Variant and Gene-Based Association

The exome array for the ADGC cohorts contains a total of 252 349 variants, a majority of which are functional rare single-nucleotide variants. A total of 158 165 variants were left for testing after quality control. Prioritized variants from our WES analysis present on the exome chip were assessed with both single-variant and gene-based analysis; genes containing prioritized variants not present on the chip were tested only through gene-based testing. Bonferroni-adjusted significance levels corrected for the number of tests per single variant or gene-based analysis category (eMethods in the Supplement provides further details).

Results

Variants in Known EOAD Genes

Several rare nonsynonymous or loss-of-function mutations in known or suspected EOAD genes were identified in our case series (Table 1). The SORL1 missense mutations were identified in 3 families, 2 of which also have family members with LOAD. Two of these mutations, T588I (present in 4 affected individuals) and T2134M (present in 3 affected individuals) are mutations in the same individuals reported by Cuccaro et al. A third mutation, a novel frameshift variant (Cys1431fs), was identified in 2 sisters, 1 who is APOEε3/4 affected with AAO of 60 years and the other with mild cognitive impairment (age at examination, 69 years; APOEε3/3).

Table 1. Nonsynonymous or Loss-of-Function Variants in Known EOAD/Dementia Genes (Pathogenic in ClinVar and/or Segregating With MAF<0.005).

Gene	Cases, No.	AAO in Cases	Chr:Position:Allele Change	rsID	VEP	Protein Change	MAF^a	CADD Score	Clinical Significance^b
MAPT	3	52, 56, 61	17:44101427:C>T	rs63750424	Missense	R406W	1 × 10⁻⁵	35	Pathogenic
MAPT	1	57	17:44039753:C>T	rs144611688	Missense	T17M	2 × 10⁻⁴	23.6	ND
PSEN1	2	54, 56	14:73637653:C>T	rs63749824	Missense	A79V	6 × 10⁻⁶	33	Pathogenic
PSEN1	1	50	14:73664774:C>G	rs63751019	Missense	R265G	6 × 10⁻⁶	33	Untested
PSEN2	1	48	1:227075813:A>G	rs615757781	Startloss	M174V	5 × 10⁻⁴	15.5	Probably nonpathogenic
SORL1	3	59-82	11:121414334:C>T	Novel	Missense	T588I	Novel	32	ND
SORL1	2	60, 69^c	11:121461788:GC>G	Novel	Frameshift	Cys1431fs	Novel	35	ND
SORL1	3	55-84	11:121498300:C>T	rs142884576	Missense	T2134M	2 × 10⁻⁴	28.6	ND

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Abbreviations: AAO, age at onset; CADD, Combined Annotation Dependent Depletion; Chr, chromosome; EOAD, early-onset Alzheimer disease; MAF, minor allele frequency; ND, no designation in ClinVar; VEP, variant-effect predictor variant consequence.

^{^a}

Kaviar Database MAF.

^{^b}

According to ClinVar.

^{^c}

Individual has mild cognitive impairment.

A PSEN1 missense mutation (A79V) previously reported in a LOAD family and classified as pathogenic by ClinVar was identified in 2 individuals with AAO of 54 years (APOEε3/4) and 56 years (APOEε3/4). An additional PSEN1 missense mutation was identified in a patient with AAO of 50 years (APOEε3/3), and a PSEN2 start-loss mutation (rs61757781) was present in an individual with AAO of 48 years (APOEε3/3). MAPT R406W, previously reported in both frontotemporal dementia with parkinsonism-17 and AD, was shared by 2 siblings and an unrelated participant. The individuals with the MAPT R406W and PSEN1 A79V mutations were removed from further analyses owing to their ClinVar pathogenic classification. All other variants were novel or rated as probable nonpathogenic or untested in ClinVar (Table 1). Only 1 of the known EOAD gene variants was available from the ADGC exome chip study, a start-loss mutation in PSEN2 (rs1757781), which showed no evidence for association in the EOAD or LOAD sample.

Twenty-six rare variants, 16 of which are deleterious according to CADD (eTable 3 in the Supplement), were present in known or suspected LOAD genes in our EOAD case series, including a frameshift variant in HLA-DRB1 (3123 Entrez Gene) and missense variants in ABCA7 (10347 Entrez Gene), AKAP9 (10142 Entrez Gene), CD2AP (23607 Entrez Gene), EPHA1 (2041 Entrez Gene), MS4A4A (51338 Entrez Gene), RIN3 (79890 Entrez Gene), and UNC5C. Five of the known LOAD variants were on the exome chip, including a rare RIN3 missense variant (rs150221413), which showed suggestive evidence of association with EOAD at a Bonferroni correction level of P = .01 for 5 variants tested (odds ratio [OR], 4.56 (95% CI, 1.26-16.48; P = .02 without adjustment for APOE, Bonferroni-corrected P value (BP) = 0.091; P = .024 with APOE adjustment) and, although not significant, was more frequent in LOAD cases than controls (minor allele frequency, 0.0008 and 0.0004 in cases vs controls, respectively; OR, 1.79; 95% CI, 0.65-4.87; P = .23, BP>.99) (Table 2; eTable 4 in the Supplement reports secondary model results).

Table 2. Summary of Top Results for Each Prioritization Method.

		Prioritization Category
		Variants in Known Genes	Rare, Deleterious Variants in Multiple Cases	Genes With Rare, Deleterious Variants in Multiple Unrelated Cases		Shared Variants Between NHW and Hispanic Cases
Results
Gene		RIN3	RUFY1	PSD2		TCIRG1
Chr: position: allele change		14:93022240:G>T	5:179036506:t>G	5:139216541:G>A	5:139216759:G>A	11:67810477:C>T
MAF^a		5 × 10⁻⁴	1 × 10⁻³	6 × 10⁻⁴	1 × 10⁻⁵	7 × 10⁻⁴
CADD score		23.6	16	28.5	27.4	13.2
EOAD SV	OR (95% CI)	4.56 (1.26-16.48)	18.63 (1.62-213.45)	…		2.13 (0.99-4.55)
EOAD SV	P value	0.02	3.8 × 10⁻³	…		0.06
EOAD gene	OR (95% CI)	…	…	…		…
EOAD gene	P value	…	…	2.0 × 10^−6,^b		…
LOAD SV	OR (95% CI)	1.79 (0.65-4.87)	2.50 (0.28-21.73)	…		2.23 (1.37-3.62)
LOAD SV	P value	0.23	0.32	…		7.2 × 10^−4,b
LOAD gene	OR (95% CI)	…	…	…		…
LOAD gene	P value	…	…	6.2 × 10^−6,^b		…
No. of variants tested (BPsig)		5 (.010)	43 (1.1 × 10⁻³)	676 (7.4 × 10⁻⁵)		7 (5.0 × 10⁻³)

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Abbreviations: BPsig, Bonferroni P value significance level; CADD, Combined Annotation Dependent Depletion; Ellipses indicate that these tests were not available; EOAD, early-onset Alzheimer disease; LOAD, late-onset Alzheimer disease; MAF, minor allele frequency; NHW, non-Hispanic white; OR, odds ratio; SV, single variant; VEP, variant effect predictor variant consequence.

^{^a}

Kaviar Database MAF.

^{^b}

Meets Bonferroni level.

Genomic control inflation factors (GIFs) and quantile-quantile plots show that our analyses are not inflated and are valid or conservative (ie, the EOAD single-variant tests) in terms of distribution of results (GIF<1.1) (eFigures 1-4 in the Supplement). The quantile-quantile and GIFs for variants with allele counts of 10 or more show only slight inflation for the EOAD single-variant tests (GIF = 1.15), although finding may be due to our unbalanced case-control sample, as rescaling λ for 1000 cases and 1000 controls produces a GIF of 0.92 (eFigures 5 and 6 in the Supplement).

Novel Candidate Variants and Genes

Variants Present in Multiple Unrelated Cases

After removing variants in highly mutable genes (based on high gene damage index scores), 108 rare deleterious variants in 106 genes were present in 2 or more unrelated individuals. Of these, 43 variants were testable in the ADGC exome chip data set. A missense variant in RUFY1 (80230 Entrez Gene), present in 4 ADGC association cases and no controls, showed evidence of an association with EOAD (OR, 18.63; 95% CI, 1.62-213.45; P = .003, BP = .129), nearing a Bonferroni-corrected significance level of P = 1 × 10⁻³ for 43 variants tested (Table 2; eTable 4 in the Supplement provides secondary model results). All variants occurred in cases (4 EOAD and 3 LOAD), with 3 of 4 EOAD patients carrying APOE ε4 (P = .28) and 1 patient with LOAD carrying APOEε4 (P = .07). The rarity of the variant makes it difficult to conclude whether the effect in EOAD is spurious or due to a chance correlation between APOEε4 and the RUFY1 variant. Four other variants, including a missense variant present in 2 WES EOAD cases in the gene NAA60, showed nominal significance in the ADGC data set (eTable 5 in the Supplement).

Genes With Variants in Multiple Unrelated Cases

Filtering to genes with rare, deleterious nonsynonymous or loss-of-function variants in multiple unrelated individuals left 747 genes, 676 of which were testable in the ADGC EOAD association study (Bonferroni-critical P = 7.40 × 10⁻⁵ for 676 genes tested). The gene PSD2 (84249 Entrez Gene) met genome-wide significance in both EOAD (P = 2.05 × 10⁻⁶, BPsig = 1.3 × 10⁻³, APOE-adjusted P = 1.55 × 10⁻⁵) and LOAD (P = 6.22 × 10⁻⁶, BP = 4.1 × 10⁻³, APOE-adjusted P = 2.30 × 10⁻⁴) cohorts when all variants in the gene were included in a gene-based test. The APOE-adjusted results are slightly less significant, likely due to smaller sample sizes of these analyses (ie, absence of APOE genotype for all participants) or minor correlation between APOE and PSD2 risk genotypes. With restriction of the analyses to high or moderate consequence variants with CADD scores of 15 or higher, the signal for association was strengthened further (EOAD P = 1.68 × 10⁻⁶) (eTable 5 in the Supplement). Several additional genes (LIN37 [55957 Entrez Gene], SLC22A17 [51310 Entrez Gene], LRRC16B [90668 Entrez Gene], and HSD17B2 [3294 Entrez Gene]) showed suggestive evidence of association with EOAD (P < .005) (eTable 5 in the Supplement).

Variants Present in Both NHW and Hispanic Individuals

Thirty rare missense or loss-of-function variants, 6 of which were scored as deleterious by CADD, were shared between our NHW and Hispanic WES cohorts. Seven of these variants were included on the exome chip (Bonferroni-critical P = 7 × 10⁻³ for 7 variants tested). A missense variant in TCIRG1 (10312 Entrez Gene) (CADD Phred score, 13.2), present in NHW patients with EOAD (AAO, 57 years) and segregating in 3 Hispanic siblings with EOAD who were aged 56, 59, and 63 years (Figure 2), was more frequent in cases than controls (minor allele frequency, 3.2 × 10⁻³ and 1.4 × 10⁻³, respectively) in the ADGC EOAD cohort (OR, 2.13; 95% CI, 0.99-4.55; P = .06, BP = .413, APOE-adjusted P = .38), and this difference was significant in the ADGC LOAD cohort (OR, 2.23; 95% CI, 1.37-3.62; P = 7.2 × 10⁻⁴, BP = 5.0 × 10⁻³, APOE-adjusted P = 2.0 × 10⁻³) (Table 2; eTable 4 in the Supplement provides secondary model results). Of the16 rare nonsynonymous or loss-of-function variants prioritized in the Hispanic family, 6 are on the exome chip, with the TCIRG1 variant being the only variant showing association with AD. Furthermore, gene-based results for genes containing the 10 other variants showed nominal association with EOAD only for the gene COL3A1 (1281 Entrez Gene) (P = .03), although this gene test is not comprehensive since it relies only on the variants available on the exome chip.

Figure 2. — AAE indicates age at examination; AAO, age at onset; *APOE*, apolipoprotein E; and C/T, cytosine/thymine. Dashes beneath the symbols indicate that data are unavailable.

Discussion

Accumulating evidence points to alterations of the endolysosomal pathway as playing key roles in AD, with variation in several genes of the pathway recognized as risk factors for AD, including SORL1, BIN1 (274 Entrez Gene), PICALM (8301 Entrez Gene), RIN3, PTK2B (2185 Entrez Gene), MEF2C (4208 Entrez Gene), and CD2AP. Some of the earliest neuropathologic changes of AD (eg, enlargement of endosomal compartments, accumulation of phagocytic vacuoles, and lysosomal deficiencies) are endocytic in nature. These abnormalities develop well before manifestation of clinical symptoms, but appear critical in the dysregulation of amyloid precursor protein processing thought to be essential in AD pathology. The gene SORL1, which guides APP to the endocytic pathway for recycling and has been linked to EOAD in several studies, highlights a likely role for endocytosis in EOAD. Steps along the pathway include vesicle formation through membrane budding, vesicle transport, docking, cargo capture, and sorting in the early endosome, endosome maturation (late endosome), and, finally, degradation in lysosomes. It is likely that genic alterations in each of these steps contribute to the swelling of endosomal vesicles and ultimate accumulation of amyloid β in neurites that is thought to promote AD.

Following a hypothesis that rare functional variants are responsible for EOAD, we filtered WES data on 53 NHW patients with EOAD based on consequence, deleteriousness, ethnic-specific (which have been shown to aid in the identification of true causal disease variants), and population-specific minor allele frequencies. In addition to identifying several known and novel mutations in known or suspected EOAD genes (GRN, MAPT, PSEN1, PSEN2, SORL1, and TREM2), we report several candidate genes for EOAD involved in the endolysosomal pathway, including RUFY1, PSD2, TCIRG1, and the known LOAD risk gene RIN3 (Table 2). These results adjusted for principal components only, but were supported for PSD2 in secondary analyses adjusting for age, sex, and principal components (P = 1.23 × 10⁻³); however, these analyses do not show an association in the other candidate genes (eTable 4 in the Supplement), possibly due to the older mean age of the control participants. In addition, although PSD2, TCIRG1, and RIN3 are associated with AD even with adjustment for APOE, the rarity of the RUFY1 variant, which occurs only in AD, makes its evaluation in APOE-adjusted analysis difficult. All 4 genes participate in different steps of the endolysosomal pathway, highlighting the likelihood that alterations in many endocytic genes can increase the risk of EOAD.

The PSD2 gene appears to play an early role through its synthesis of phospholipids critical to maturation of transport vesicles and vacuoles integral to the pathway. Disturbance of the formation of these vesicles and vacuoles is critical in proper processing of endosomal debris. The importance of PSD2 to AD in this process potentially revolves around the formation and proper maintenance of phosphatidylethanolamine, a function for which PSD2 is essential. This enzyme, which is decreased in AD brains, has been shown to regulate the γ-secretase activity integral to APP processing and to positively regulate autophagy and longevity in yeast.

Both RUFY1, which binds vesicles containing the endosomal traffic regulator phosphatidylinositol-3-phosphate, and RIN3, a known LOAD risk gene, appear to be critical to the development and regulation of the early endosome, a major site of Aβ peptide generation that is markedly enlarged within neurons in AD brains. While RUFY1 binds phosphatidylinositol-3-phosphate, which is deficient in brain tissue from both humans with AD and AD mouse models, it also is required for normal RAB31 (11031 Entrez Gene) function, a Rab5 (5868 Entrez Gene) family protein. Rab5, a key regulator of early endosome formation, increases amyloid β production and is stimulated and stabilized by RIN3.

TCIRG1, a gene that was found to share a prioritized variant between NHW and Caribbean Hispanic patients, is located in the lysosome, where it appears to be critical for acidification of vacuoles working to remove debris via the endolysosomal pathway. In AD, disturbed lysosomal degradation is of key importance in aberrant vacuole turnover. Furthermore, this gene has recently been associated with absolute counts of neutrophils, which are key components of innate immunity that have been linked to development of AD, including 1 study that found 10 times more neutrophils in the brain tissue of patients with AD. The sharing of this variant in both NHW and Caribbean Hispanic populations supports the generalizability of this gene as a potential risk locus for both of these populations.

Through association analysis of our candidate genes in a large AD cohort from the ADGC, we also add to accumulating evidence pointing to overlap of risk genes involved in both EOAD and LOAD, with both TCIRG1 and PSD2 associated with EOAD and LOAD. This overlap of genetic architecture between the early- and late-onset forms of the disease has been previously identified for the genes SORL1, TREM2, and PSEN1. In addition, the ε4 allele of APOE, the strongest genetic risk factor for LOAD, also drives the risk for EOAD in ε4/ε4 individuals with AAO of approximately 65 years and accentuates endosome pathology at early stages of AD, a finding that is in line with other evidence pointing toward endolysosomal pathology occurring in the early stages of AD and promoting earlier onset of AD. In addition, although some differences in neuropathology of EOAD and LOAD have been identified, many pathologic features overlap between the early and late forms of AD.

Limitations

There are several limitations to our study. First, some variants prioritized in the WES analysis were not present on the exome chip, making assessment of their impact impossible in the present study. These variants should be further examined in large, case-control association studies to determine their potential risk to AD. Second, although we followed up our prioritized WES variants in a sizeable case-control sample, the power of this sample for assessment of very rare variants is limited, and replication of these results in other large case-control samples will be necessary. Finally, while several studies suggest that our top results are involved in endolysosomal transport, additional wet-laboratory studies will need to confirm that this pathway is the mechanism through which these genes increase the risk for AD.

Conclusions

Using a combined strategy of bioinformatics filtering of WES of EOAD cases, followed by testing of prioritized variants and genes in a large EOAD and LOAD cohort, we have identified several novel EOAD candidate genes, 2 of which were also associated with LOAD. Taken together, our results highlight endolysosomal alterations in multiple genes as risk factors for EOAD and point to additional genes conferring risk of both EOAD and LOAD.

Supplement.

eTable 1. Demographic Characteristics of NHW and Hispanic Whole Exome Sequencing Samples

eTable 2. Demographic Characteristics of the ADGC EOAD Association Study Samples

eTable 3. Deleterious Variants (CADD≥15) in Known or Suspected LOAD Genes

eTable 4. Secondary Model (Age, Sex and Principal Component Adjusted) SNV and Gene-Based Association Results

eTable 5. Nominally Significant Results From Testing of Prioritized Gene Variants in the ADGC Association Study

eMethods. Detailed Methodology

eFigure 1. Genomic Inflation Factor and QQ-Plot for the EOAD SNV Association Analysis (No Allele Count Filter)

eFigure 2. Genomic Inflation Factor and QQ-Plot for the LOAD SNV Association Analysis (No Allele Count Filter)

eFigure 3. Genomic Inflation Factor and QQ-Plot for the EOAD Gene-Based Association Analysis

eFigure 4. Genomic Inflation Factor and QQ-Plot for the LOAD Gene-Based Association Analysis

eFigure 5. Genomic Inflation Factor and QQ-Plot for the EOAD SNV Association Analysis (Allele Count ≥10 Filter)

eFigure 6. Genomic Inflation Factor and QQ-Plot for the LOAD SNV Association Analysis (Allele Count ≥10 Filter)

Click here for additional data file.^{(384.2KB, pdf)}

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement.

eTable 1. Demographic Characteristics of NHW and Hispanic Whole Exome Sequencing Samples

eTable 2. Demographic Characteristics of the ADGC EOAD Association Study Samples

eTable 3. Deleterious Variants (CADD≥15) in Known or Suspected LOAD Genes

eTable 4. Secondary Model (Age, Sex and Principal Component Adjusted) SNV and Gene-Based Association Results

eTable 5. Nominally Significant Results From Testing of Prioritized Gene Variants in the ADGC Association Study

eMethods. Detailed Methodology

eFigure 1. Genomic Inflation Factor and QQ-Plot for the EOAD SNV Association Analysis (No Allele Count Filter)

eFigure 2. Genomic Inflation Factor and QQ-Plot for the LOAD SNV Association Analysis (No Allele Count Filter)

eFigure 3. Genomic Inflation Factor and QQ-Plot for the EOAD Gene-Based Association Analysis

eFigure 4. Genomic Inflation Factor and QQ-Plot for the LOAD Gene-Based Association Analysis

eFigure 5. Genomic Inflation Factor and QQ-Plot for the EOAD SNV Association Analysis (Allele Count ≥10 Filter)

eFigure 6. Genomic Inflation Factor and QQ-Plot for the LOAD SNV Association Analysis (Allele Count ≥10 Filter)

Click here for additional data file.^{(384.2KB, pdf)}

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PERMALINK

Early-Onset Alzheimer Disease and Candidate Risk Genes Involved in Endolysosomal Transport

Brian W Kunkle, PhD, MPH

Badri N Vardarajan, PhD

Adam C Naj, PhD

Patrice L Whitehead, BS

Sophie Rolati, MS

Susan Slifer, MS

Regina M Carney, MD

Michael L Cuccaro, PhD

Jeffery M Vance, MD, PhD

John R Gilbert, PhD

Li-San Wang, PhD

Lindsay A Farrer, PhD

Christiane Reitz, MD, PhD

Jonathan L Haines, PhD

Gary W Beecham, PhD

Eden R Martin, PhD

Gerard D Schellenberg, PhD

Richard P Mayeux, MD, MSc

Margaret A Pericak-Vance, PhD

Key Points

Question

Findings

Meaning

Abstract

Importance

Objective

Design, Setting, and Participants

Main Outcomes and Measures

Results

Conclusions and Relevance

Introduction

Methods

WES of EOAD Cases

Selection of EOAD Cases for Sequencing

WES, Variant Calling and Quality Control

WES Variant Prioritization

Variant Filtering for Rare Nonsynonymous or Loss-of-Function Variants

Figure 1. Analysis Strategy Summary, Including Prioritization of Candidate Variants and Their Testing, in Alzheimer Disease (AD) Case-Control Data Sets.

Prioritization of Variants in Known or Suspected AD Genes

Prioritization of Variants in Novel Candidate Genes

Association Testing of Prioritized Variants and Genes

Cohort Description

Single Variant and Gene-Based Association

Results

Variants in Known EOAD Genes

Table 1. Nonsynonymous or Loss-of-Function Variants in Known EOAD/Dementia Genes (Pathogenic in ClinVar and/or Segregating With MAF<0.005).

Table 2. Summary of Top Results for Each Prioritization Method.

Novel Candidate Variants and Genes

Variants Present in Multiple Unrelated Cases

Genes With Variants in Multiple Unrelated Cases

Variants Present in Both NHW and Hispanic Individuals

Figure 2. Pedigree of Hispanic Family Segregating a Rare C>T Variant in TCIRG1.

Discussion

Limitations

Conclusions

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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Links to NCBI Databases