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1. The Revolution in Management

Author

Freeman, R. Edward, Gilbert, Jr., Daniel R., den Hond, Frank, Series Editor, Enderle, George, Editorial Board Member, Steinmann, Horst, Editorial Board Member, Xiaohe, Lu, Editorial Board Member, Koehn, Daryl, Editorial Board Member, Umezu, Hiro, Editorial Board Member, Scherer, Andreas, Editorial Board Member, Jones, Campbell, Editorial Board Member, Dmytriyev, Sergiy D., editor, and Freeman, R. Edward, editor

Published
2023
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4. Parallel processing of filtered queries in attributed semantic graphs

Author

Lugowski, A, Kamil, S, Buluç, A, Williams, S, Duriakova, E, Oliker, L, Fox, A, and Gilbert, JR

Subjects
Graph analysis systems, Attributed semantic graphs, Graph filtering, Parallel computing, Knowledge discovery, Domain-specific languages, SEJITS, High-performance graph analysis, Networking and Information Technology R&D, Distributed Computing, Computer Software
Abstract

Execution of complex analytic queries on massive semantic graphs is a challenging problem in big-data analytics that requires high-performance parallel computing. In a semantic graph, vertices and edges carry attributes of various types and the analytic queries typically depend on the values of these attributes. Thus, the computation must view the graph through a filter that passes only those individual vertices and edges of interest. Previous investigations have developed Knowledge Discovery Toolbox (KDT), a sophisticated Python library for parallel graph computations. In KDT, the user can write custom graph algorithms by specifying operations between edges and vertices (semiring operations). The user can also customize existing graph algorithms by writing filters. Although the high-level language for this customization enables domain scientists to productively express their graph analytics requirements, the customized queries perform poorly due to the overhead of having to call into the Python virtual machine for each vertex and edge. In this work, we use the Selective Embedded Just-In-Time Specialization (SEJITS) approach to automatically translate semiring operations and filters defined by programmers into a lower-level efficiency language, bypassing the upcall into Python. We evaluate our approach by comparing it with the high-performance Combinatorial BLAS engine and show that our approach combines the benefits of programming in a high-level language with executing in a low-level parallel environment. We increase the system's flexibility by developing techniques that provide users with the ability to define new vertex and edge types from Python. We also present a new Roofline model for graph traversals and show that we achieve performance that is significantly closer to the bounds suggested by the Roofline. Finally, to further understand the complex interaction with the underlying architecture, we present an analysis using performance counters that quantifies the improvement in hardware behavior in the context our SEJITS methodology. Overall, we demonstrate the first known solution to the problem of obtaining high performance from a productivity language when applying graph algorithms selectively on semantic graphs with hundreds of millions of edges and scaling to thousands of processors for graphs.

Published
2015

6. High-productivity and high-performance analysis of filtered semantic graphs

Author

Buluç, A, Duriakova, E, Fox, A, Gilbert, JR, Kamil, S, Lugowski, A, Oliker, L, and Williams, S

Abstract

High performance is a crucial consideration when executing a complex analytic query on a massive semantic graph. In a semantic graph, vertices and edges carry attributes of various types. Analytic queries on semantic graphs typically depend on the values of these attributes, thus, the computation must view the graph through a filter that passes only those individual vertices and edges of interest. Knowledge Discovery Toolbox (KDT), a Python library for parallel graph computations, is customizable in two ways. First, the user can write custom graph algorithms by specifying operations between edges and vertices. These programmer-specified operations are called semiring operations due to KDT's underlying linear-algebraic abstractions. Second, the user can customize existing graph algorithms by writing filters that return true for those vertices and edges the user wants to retain during algorithm execution. For high productivity, both semiring operations and filters are written in a high-level language, resulting in relatively low performance due to the bottleneck of having to call into the Python virtual machine for each vertex and edge. In this work, we use the Selective Embedded JIT Specialization (SEJITS) approach to automatically translate semiring operations and filters defined by programmers into a lower-level efficiency language, bypassing the up call into Python. We evaluate our approach by comparing it with the high-performance Combinatorial BLAS engine, and show our approach enables users to write in high-level languages and still obtain the high performance of low-level code. We also present a new roofline model for graph traversals, and show that our high-performance implementations do not significantly deviate from the roofline. Overall, we demonstrate the first known solution to the problem of obtaining high performance from a productivity language when applying graph algorithms selectively on semantic graphs. © 2013 IEEE.

Published
2013

7. High-performance analysis of filtered semantic graphs

Author

Buluç, A, Fox, A, Gilbert, JR, Kamil, S, Lugowski, A, Oliker, L, and Williams, S

Subjects
Domain Specific Languages, Graph Analysis, SEJITS, KDT, High-performance computing
Abstract

High performance is a crucial consideration when executing a complex analytic query on a massive semantic graph. In a semantic graph, vertices and edges carry \attributes" of various types. Analytic queries on semantic graphs typically depend on the values of these attributes; thus, the computation must either view the graph through a filter that passes only those individual vertices and edges of interest, or else must first materialize a subgraph or subgraphs consisting of only the vertices and edges of interest. The filtered approach is superior due to its generality, ease of use, and memory efficiency, but may carry a performance cost. In the Knowledge Discovery Toolbox (KDT), a Python library for parallel graph computations, the user writes filters in a high-level language, but those filters result in relatively low performance due to the bottleneck of having to call into the Python interpreter for each edge. In this work, we use the Selective Embedded JIT Specialization (SEJITS) approach to automatically translate filters defined by programmers into a lower-level efficiency language, bypassing the upcall into Python. We evaluate our approach by comparing it with the high-performance C++ /MPI Combinatorial BLAS engine, and show that the productivity gained by using a high-level filtering language comes without sacrificing performance. Copyright © 2012 by the Association for Computing Machinery, Inc. (ACM).

Published
2012

8. Evidence of novel fine-scale structural variation at autism spectrum disorder candidate loci

Author

Hedges, DJ, Hamilton-Nelson, KL, Sacharow, SJ, Nations, L, Beecham, GW, Kozhekbaeva, ZM, Butler, BL, Cukier, HN, Whitehead, PL, Ma, D, Jaworski, JM, Nathanson, L, Lee, JM, Hauser, SL, Oksenberg, JR, Cuccaro, ML, Haines, JL, Gilbert, JR, and Pericak-Vance, MA

Abstract

Background: Autism spectrum disorders (ASD) represent a group of neurodevelopmental disorders characterized by a core set of social-communicative and behavioral impairments. Gamma-aminobutyric acid (GABA) is the major inhibitory neurotransmitter in the brain, acting primarily via the GABA receptors (GABR). Multiple lines of evidence, including altered GABA and GABA receptor expression in autistic patients, indicate that the GABAergic system may be involved in the etiology of autism. Methods. As copy number variations (CNVs), particularly rare and de novo CNVs, have now been implicated in ASD risk, we examined the GABA receptors and genes in related pathways for structural variation that may be associated with autism. We further extended our candidate gene set to include 19 genes and regions that had either been directly implicated in the autism literature or were directly related (via function or ancestry) to these primary candidates. For the high resolution CNV screen we employed custom-designed 244 k comparative genomic hybridization (CGH) arrays. Collectively, our probes spanned a total of 11 Mb of GABA-related and additional candidate regions with a density of approximately one probe every 200 nucleotides, allowing a theoretical resolution for detection of CNVs of approximately 1 kb or greater on average. One hundred and sixty-eight autism cases and 149 control individuals were screened for structural variants. Prioritized CNV events were confirmed using quantitative PCR, and confirmed loci were evaluated on an additional set of 170 cases and 170 control individuals that were not included in the original discovery set. Loci that remained interesting were subsequently screened via quantitative PCR on an additional set of 755 cases and 1,809 unaffected family members. Results: Results include rare deletions in autistic individuals at JAKMIP1, NRXN1, Neuroligin4Y, OXTR, and ABAT. Common insertion/deletion polymorphisms were detected at several loci, including GABBR2 and NRXN3. Overall, statistically significant enrichment in affected vs. unaffected individuals was observed for NRXN1 deletions. Conclusions: These results provide additional support for the role of rare structural variation in ASD. © 2012 Hedges et al; licensee BioMed Central Ltd.

Published
2012

12. Things to Keep in Mind when Choosing Valves

Author

Welsford, Gilbert, Jr.

Subjects
Natural gas, Petroleum -- Transportation, Valves, Business, Petroleum, energy and mining industries
Abstract

Oils processing occurs in multiple stages and requires specialized piping systems to handle different fluids. There are dedicated piping systems that transport crude oils, natural gases, liquids and semisolid materials. [...]

Published
2021

14. TIGHT SHUT-OFF APPLICATIONS: What to Look for in Valve Selection

Author

Welsford, Gilbert, Jr.

Subjects
Valves, Business, Engineering and manufacturing industries
Abstract

Tight shut-off refers to the condition where there is no leakage of fluid when a valve is in the closed position. This is vital in flow systems where isolation is [...]

Published
2022

16. Philippines braces for the typhoon season amidst COVID-19

Author

Gilbert Jr De Los Santos Bernardino, Xu Lin, Yusuff Adebayo Adebisi, Don Eliseo Lucero-Prisno, and Angel Anthony R Camua

Subjects
2019-20 coronavirus outbreak, History, Coronavirus disease 2019 (COVID-19), lcsh:Public aspects of medicine, Health Policy, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Public Health, Environmental and Occupational Health, Obstetrics and Gynecology, lcsh:RA1-1270, Article, Psychiatry and Mental health, Infectious Diseases, Typhoon, Pediatrics, Perinatology and Child Health, Internal Medicine, Geriatrics and Gerontology, Socioeconomics
Published
2020

22. ESTABLISHING AN ADDITIVE MANUFACTURING (AM) NAVY ENLISTED CLASSIFICATION FOR THE MACHINERY REPAIRMAN TO ENABLE EFFICIENT USE OF AM AND MASS ADOPTION OF THE TECHNOLOGY

Author

Garcia, Gilbert Jr., Edenfield, Bradford L., Yoshida, Kazuma, Hatch, William D., II, Doerr, Kenneth H., Sadagic, Amela, and Business and Public Policy (GSBPP)

Subjects
AM, education, training, standardized training, ComputerApplications_COMPUTERSINOTHERSYSTEMS, 3D printing, AM technology adoption
Abstract

MBA Professional Project Additive Manufacturing (AM) technology has been making significant advances in recent years. While industry has already started exploiting it in its daily operations, the adoption and implementation of AM technology across the Department of Defense (DoD) and Department of the Navy (DoN) are still in very early stages. The major value that DoD and DoN plan to achieve are improvements in operational readiness, cost-savings, and increases in warfighting capability. The DoN has yet to identify the elements of a comprehensive training program across the fleet, including the training of operational level users. Should a Navy Enlisted Classification (NEC) be created for the Machinery Repairman (MR) rating to ensure safe and competent use of AM technology? Does creating an NEC support DoD and DoN policies and guidelines for AM implementation? This report examines those questions and provides a rationale and recommendations for the creation of an AM NEC, including a set of critical skills that should be a part of that training. Methodology developed and used in this report could be applied to examine the needs of other rates beyond MR. Recommended future work emphasizes the need to continue updating the training program recommended in this report, keeping it relevant with advances made in AM technology and our growing understanding about DoN applications of this technology. http://archive.org/details/establishinganad1094562753 Lieutenant, United States Navy Lieutenant Commander, United States Navy Lieutenant, Japan Maritime Self-Defense Force

Published
2019

23. Genetic meta-analysis of diagnosed Alzheimer's disease identifies new risk loci and implicates A beta, tau, immunity and lipid processing (vol 51, pg 414, 2019)

Author

Kunkle, BW, Grenier-Boley, B, Sims, R, Bis, JC, Damotte, V, Naj, AC, Boland, A, Vronskaya, M, van der Lee, SJ, Amlie-Wolf, A, Bellenguez, C, Frizatti, A, Chouraki, V, Martin, ER, Sleegers, K, Badarinarayan, N, Jakobsdottir, J, Hamilton-Nelson, KL, Moreno-Grau, S, Olaso, R, Raybould, R, Chen, YN, Kuzma, AB, Hiltunen, M, Morgan, T, Ahmad, S, Vardarajan, BN, Epelbaum, J, Hoffmann, P, Boada, M, Beecham, GW, Garnier, JG, Harold, D, Fitzpatrick, AL, Valladares, O, Moutet, ML, Gerrish, A, Smith, AV, Qu, LM, Bacq, D, Denning, N, Jian, XQ, Zhao, Y, Del Zompo, M, Fox, NC, Choi, SH, Mateo, I, Hughes, JT, Adams, HH, Malamon, J, Sanchez-Garcia, F, Patel, Y, Brody, JA, Dombroski, BA, Naranjo, MCD, Daniilidou, M, Eiriksdottir, G, Mukherjee, S, Wallon, D, Uphill, J, Aspelund, T, Cantwell, LB, Garzia, F, Galimberti, D, Hofer, E, Butkiewicz, M, Fin, B, Scarpini, E, Sarnowski, C, Bush, WS, Meslage, S, Kornhuber, J, White, CC, Song, Y, Barber, RC, Engelborghs, S, Sordon, S, Voijnovic, D, Adams, PM, Vandenberghe, R, Mayhaus, M, Cupples, LA, Albert, MS, De Deyn, PP, Gu, W, Himali, JJ, Beekly, D, Squassina, A, Hartmann, AM, Orellana, A, Blacker, D, Rodriguez-Rodriguez, E, Lovestone, S, Garcia, ME, Doody, RS, Munoz-Fernadez, C, Sussams, R, Lin, HH, Fairchild, TJ, Benito, YA, Holmes, C, Karamujic-Comic, H, Frosch, MP, Thonberg, H, Maier, W, Roshchupkin, G, Ghetti, B, Giedraitis, V, Kawalia, A, Li, S, Huebinger, RM, Kilander, L, Moebus, S, Hernandez, I, Kamboh, MI, Brundin, R, Turton, J, Yang, Q, Katz, MJ, Concari, L, Lord, J, Beiser, AS, Keene, CD, Helisalmi, S, Kloszewska, I, Kukull, WA, Koivisto, AM, Lynch, A, Tarraga, L, Larson, EB, Haapasalo, A, Lawlor, B, Mosley, TH, Lipton, RB, Solfrizzi, V, Gill, M, Longstreth, WT, Montine, TJ, Frisardi, V, Diez-Fairen, M, Rivadeneira, F, Petersen, RC, Deramecourt, V, Alvarez, I, Salani, F, Ciaramella, A, Boerwinkle, E, Reiman, EM, Fievet, N, Rotter, JI, Reisch, JS, Hanon, O, Cupidi, C, Uitterlinden, AGA, Royall, DR, Dufouil, C, Maletta, RG, de Rojas, I, Sano, M, Brice, A, Cecchetti, R, St George-Hyslop, P, Ritchie, K, Tsolaki, M, Tsuang, DW, Dubois, B, Craig, D, Wu, CK, Soininen, H, Avramidou, D, Albin, RL, Fratiglioni, L, Germanou, A, Apostolova, LG, Keller, L, Koutroumani, M, Arnold, SE, Panza, F, Gkatzima, O, Asthana, S, Hannequin, D, Whitehead, P, Atwood, CS, Caffarra, P, Hampel, H, Quintela, I, Carracedo, A, Lannfelt, L, Rubinsztein, DC, Barnes, LL, Pasquier, F, Frolich, L, Barral, S, McGuinness, B, Beach, TG, Johnston, JA, Becker, JT, Passmore, P, Bigio, EH, Schott, JM, Bird, TD, Warren, JD, Boeve, BF, Lupton, MK, Bowen, JD, Proitsi, P, Boxer, A, Powell, JF, Burke, JR, Kauwe, JSK, Burns, JM, Mancuso, M, Buxbaum, JD, Bonuccelli, U, Cairns, NJ, McQuillin, A, Cao, CH, Livingston, G, Carlson, CS, Bass, NJ, Carlsson, CM, Hardy, J, Carney, RM, Bras, J, Carrasquillo, MM, Guerreiro, R, Allen, M, Chui, HC, Fisher, E, Masullo, C, Crocco, EA, DeCarli, C, Bisceglio, G, Dick, M, Ma, L, Duara, R, Graff-Radford, NR, Evans, DA, Hodges, A, Faber, KM, Scherer, M, Fallon, KB, Riemenschneider, M, Fardo, DW, Heun, R, Farlow, MR, Kolsch, H, Ferris, S, Leber, M, Foroud, TM, Heuser, I, Galasko, DR, Giegling, I, Gearing, M, Hull, M, Geschwind, DH, Gilbert, JR, Morris, J, Green, RC, Mayo, K, Growdon, JH, Feulner, T, Hamilton, RL, Harrell, LE, Drichel, D, Honig, LS, Cushion, TD, Huentelman, MJ, Hollingworth, P, Hulette, CM, Hyman, BT, Marshall, R, Jarvik, GP, Meggy, A, Abner, E, Menzies, GE, Jin, LW, Leonenko, G, Real, LM, Jun, GR, Baldwin, CT, Grozeva, D, Karydas, A, Russo, G, Kaye, JA, Kim, R, Jessen, F, Kowall, NW, Vellas, B, Kramer, JH, Vardy, E, LaFerla, FM, Jockel, KH, Lah, JJ, Dichgans, M, Leverenz, JB, Mann, D, Levey, AI, Pickering-Brown, S, Lieberman, AP, Klopp, N, Lunetta, KL, Wichmann, HE, Lyketsos, CG, Morgan, K, Marson, DC, Brown, K, Martiniuk, F, Medway, C, Mash, DC, Nothen, MM, Masliah, E, Hooper, NM, McCormick, WC, Daniele, A, McCurry, SM, Bayer, A, McDavid, AN, Gallacher, J, Mckee, AC, van den Bussche, H, Mesulam, M, Brayne, C, Miller, BL, Riedel-Heller, S, Miller, CA, Miller, JW, Al-Chalabi, A, Morris, JC, Shaw, CE, Myers, AJ, Wiltfang, J, O'Bryant, S, Olichney, JM, Alvarez, V, Parisi, JE, Singleton, AB, Paulson, HL, Collinge, J, Perry, WR, Mead, S, Peskind, E, Cribbs, DH, Rossor, M, Pierce, A, Ryan, NS, Poon, WW, Nacmias, B, Potter, H, Sorbi, S, Quinn, JF, Sacchinelli, E, Raj, A, Spalletta, G, Raskind, M, Caltagirone, C, Bossu, P, Orfei, MD, Reisberg, B, Clarke, R, Reitz, C, Smith, AD, Ringman, JM, Warden, D, Roberson, ED, Wilcock, G, Rogaeva, E, Bruni, AC, Rosen, HJ, Gallo, M, Rosenberg, RN, Ben-Shlomo, Y, Sager, MA, Mecocci, P, Saykin, AJ, Pastor, P, Cuccaro, ML, Vance, JM, Schneider, JA, Schneider, LS, Slifer, S, Seeley, WW, Smith, AG, Sonnen, JA, Spina, S, Stern, RA, Swerdlow, RH, Tang, M, Tanzi, RE, Trojanowski, JQ, Troncoso, JC, Van Deerlin, VM, Van Eldik, LJ, Vinters, HV, Vonsattel, JP, Weintraub, S, Welsh-Bohmer, KA, Wilhelmsen, KC, Williamson, J, Wingo, TS, Woltjer, RL, Wright, CB, Yu, CE, Yu, L, Saba, Y, Pilotto, A, Bullido, MJ, Peters, O, Crane, PK, Bennett, D, Bosco, P, Coto, E, Boccardi, V, De Jager, PL, Lleo, A, Warner, N, Lopez, OL, Ingelsson, M, Deloukas, P, Cruchaga, C, Graff, C, Gwilliam, R, Fornage, M, Goate, AM, Sanchez-Juan, P, Kehoe, PG, Amin, N, Ertekin-Taner, N, Berr, C, Debette, S, Love, S, Launer, LJ, Younkin, SG, Dartigues, JF, Corcoran, C, Ikram, MA, Dickson, DW, Nicolas, G, Campion, D, Tschanz, J, Schmidt, H, Hakonarson, H, Clarimon, J, Munger, R, Schmidt, R, Farrer, LA, Van Broeckhoven, C, O'Donovan, MC, DeStefano, AL, Jones, L, Haines, JL, Deleuze, JF, Owen, MJ, Gudnason, V, Mayeux, R, Escott-Price, V, Psaty, BM, Ramirez, A, Wang, LS, Ruiz, A, van Duijn, CM, Holmans, PA, Seshadri, S, Williams, J, Amouyel, P, Schellenberg, GD, Lambert, JC, Pericak-Vance, MA, ADGC, EADI, Cohorts Heart Aging Res Genomic, and Genetic Environm Risk AD Defining

Published
2019

24. Intrasubject medication adherence patterns

Author

Russell, Cynthia L., Conn, Vicki S., Ashbaugh, Catherine, Madsen, Richard, Hayes, Karen, and Ross, Gilbert, Jr.

Subjects
Patient compliance -- Research, Immunosuppressive agents -- Research, Health
Abstract

The purpose of this prospective descriptive study was to explore the patterns of intrasubject (between medication) adherence of two similarly timed, twice-daily medications using the Medication Event Management System[R] electronic monitoring cap. Medication adherence was measured for 6 months using electronic monitoring in 25 adult renal-transplant recipients. Data were available from 7,119 electronic medication events. Results indicated that two twice-daily medications scheduled to be taken simultaneously were taken within 5 min of each other 77% of the time and within 10 min, 92% of the time. When only the first scheduled dose of the day was examined, the results are 79% and 95%, respectively. These findings are important to researchers and clinicians who must evaluate medication adherence in transplant recipients while balancing cost and subject burden. This study provides empirical support for monitoring a single immunosuppressive medication electronically to estimate medication adherence with double or triple immunosuppressive drug therapy. Keywords: medication adherence; transplantation; patterns

Published
2007

27. ESTABLISHING AN ADDITIVE MANUFACTURING (AM) NAVY ENLISTED CLASSIFICATION FOR THE MACHINERY REPAIRMAN TO ENABLE EFFICIENT USE OF AM AND MASS ADOPTION OF THE TECHNOLOGY

Author

Hatch, William D., II, Doerr, Kenneth H., Sadagic, Amela, Business and Public Policy (GSBPP), Garcia, Gilbert Jr., Edenfield, Bradford L., Yoshida, Kazuma, Hatch, William D., II, Doerr, Kenneth H., Sadagic, Amela, Business and Public Policy (GSBPP), Garcia, Gilbert Jr., Edenfield, Bradford L., and Yoshida, Kazuma

Abstract

MBA Professional Project, Additive Manufacturing (AM) technology has been making significant advances in recent years. While industry has already started exploiting it in its daily operations, the adoption and implementation of AM technology across the Department of Defense (DoD) and Department of the Navy (DoN) are still in very early stages. The major value that DoD and DoN plan to achieve are improvements in operational readiness, cost-savings, and increases in warfighting capability. The DoN has yet to identify the elements of a comprehensive training program across the fleet, including the training of operational level users. Should a Navy Enlisted Classification (NEC) be created for the Machinery Repairman (MR) rating to ensure safe and competent use of AM technology? Does creating an NEC support DoD and DoN policies and guidelines for AM implementation? This report examines those questions and provides a rationale and recommendations for the creation of an AM NEC, including a set of critical skills that should be a part of that training. Methodology developed and used in this report could be applied to examine the needs of other rates beyond MR. Recommended future work emphasizes the need to continue updating the training program recommended in this report, keeping it relevant with advances made in AM technology and our growing understanding about DoN applications of this technology., http://archive.org/details/establishinganad1094562753, Lieutenant, United States Navy, Lieutenant Commander, United States Navy, Lieutenant, Japan Maritime Self-Defense Force

Published
2019

38. Property.

Author

Gallo, Victor J., Wanke, Blaine J., Traverso, Terence F., Zuber, Joseph, Damiani, Byron D., Jr., and Khachadourian, Gilbert, Jr.

Subjects
Real property -- Surveys
Published
1990

39. Business associations and professions.

Author

Richards, Kimberly, Re, Matthew R., Denious, Susan R., Khachadourian, Gilbert, Jr., Jaffe, Saul, Anderson, Dorothy, Dawson, Susan K., Olsan, Jeremy L., Gallo, Victor J., Damiani, Byron D., Jr., and Wanke, Blaine J.

Subjects
Trade and professional associations -- Surveys
Published
1990

40. Meta-analysis of genetic association with diagnosed Alzheimer's disease identifies novel risk loci and implicates Abeta, Tau, immunity and lipid processing

Author

Kunkle, BW, Grenier-Boley, B, Sims, R, Bis, JC, Naj, AC, Boland, A, Vronskaya, M, van der Lee, SJ, Amlie-Wolf, A, Bellenguez, C, Frizatti, A, Chouraki, V, Martin, ER, Sleegers, K, Badarinarayan, N, Jakobsdottir, J, Hamilton-Nelson, KL, Aloso, R, Raybould, R, Chen, Y, Kuzma, AB, Hiltunen, M, Morgan, T, Ahmad, S, Vardarajan, BN, Epelbaum, J, Hoffmann, P, Boada, M, Beecham, GW, Garnier, JG, Harold, D, Fitzpatrick, AL, Valladares, O, Moutet, ML, Gerrish, A, Smith, AV, Qu, L, Bacq, D, Denning, N, Jian, X, Zhao, Y, Zompo, MD, Fox, NC, Grove, ML, Choi, SH, Mateo, I, Hughes, JT, Adams, HH, Malamon, J, Garcia, FS, Patel, Y, Brody, JA, Dombroski, B, Naranjo, MCD, Daniilidou, M, Eiriksdottir, G, Mukherjee, S, Wallon, D, Uphill, J, Aspelund, T, Cantwell, LB, Garzia, F, Galimberti, D, Hofer, E, Butkiewics, M, Fin, B, Scarpini, E, Sarnowski, C, Bush, W, Meslage, S, Kornhuber, J, White, CC, Song, Y, Barber, RC, Engelborghs, S, Pichler, S, Voijnovic, D, Adams, PM, Vandenberghe, R, Mayhaus, M, Cupples, LA, Albert, MS, De Deyn, PP, Gu, W, Himali, JJ, Beekly, D, Squassina, A, Hartmann, AM, Orellana, A, Blacker, D, Rodriguez-Rodriguez, E, Lovestone, S, Garcia, ME, Doody, RS, Fernadez, CM, Sussams, R, Lin, H, Fairchild, TJ, Benito, YA, Holmes, C, Comic, H, Frosch, MP, Thonberg, H, Maier, W, Roschupkin, G, Ghetti, B, Giedraitis, V, Kawalia, A, Li, S, Huebinger, RM, Kilander, L, Moebus, S, Hernández, I, Kamboh, MI, Brundin, R, Turton, J, Yang, Q, Katz, MJ, Concari, L, Lord, J, Beiser, AS, Keene, CD, Helisalmi, S, Kloszewska, I, Kukull, WA, Koivisto, AM, Lynch, A, Tarraga, L, Larson, EB, Haapasalo, A, Lawlor, B, Mosley, TH, Lipton, RB, Solfrizzi, V, Gill, M, Longstreth Jr, WT, Montine, TJ, Frisardi, V, Ortega-Cubero, S, Rivadeneira, F, Petersen, RC, Deramecourt, V, Ciaramella, A, Boerwinkle, E, Reiman, EM, Fievet, N, Caltagirone, C, Rotter, JI, Reisch, JS, Hanon, O, Cupidi, C, Uitterlinden, AG, Royall, DR, Dufouil, C, Maletta, RG, Moreno-Grau, S, Sano, M, Brice, A, Cecchetti, R, St George-Hyslop, P, Ritchie, K, Tsolaki, M, Tsuang, DW, Dubois, B, Craig, D, Wu, CK, Soininen, H, Avramidou, D, Albin, RL, Fratiglioni, L, Germanou, A, Apostolova, LG, Keller, L, Koutroumani, M, Arnold, SE, Panza, F, Gkatzima, O, Asthana, S, Hannequin, D, Whitehead, P, Atwood, CS, Caffarra, P, Hampel, H, Baldwin, CT, Lannfelt, L, Rubinsztein, DC, Barnes, LL, Pasquier, F, Frölich, L, Barral, S, McGuinness, B, Beach, TG, Johnston, JI, Becker, JT, Passmore, P, Bigio, EH, Schott, JM, Bird, TD, Warren, JD, Boeve, BF, Lupton, MK, Bowen, JD, Proitsi, P, Boxer, A, Powell, JF, Burke, JR, Kauwe, JK, Burns, JM, Mancuso, M, Buxbaum, JD, Bonuccelli, U, Cairns, NJ, McQuillin, A, Cao, C, Livingston, G, Carlson, CS, Bass, NJ, Carlsson, CM, Hardy, J, Carney, RM, Bras, J, Carrasquillo, MM, Guerreiro, R, Allen, M, Chui, HC, Fisher, E, Cribbs, DH, Masullo, C, Crocco, EA, DeCarli, C, Bisceglio, G, Dick, M, Ma, L, Duara, R, Graff-Radford, NR, Evans, DA, Hodges, A, Faber, KM, Scherer, M, Fallon, KB, Riemenschneider, M, Fardo, DW, Heun, R, Farlow, MR, Ferris, S, Leber, M, Foroud, TM, Heuser, I, Galasko, DR, Giegling, I, Gearing, M, Hüll, M, Geschwind, DH, Gilbert, JR, Morris, J, Green, RC, Mayo, K, Growdon, JH, Feulner, T, Hamilton, RL, Harrell, LE, Drichel, D, Honig, LS, Cushion, TD, Huentelman, MJ, Hollingworth, P, Hulette, CM, Hyman, BT, Marshall, R, Jarvik, GP, Meggy, A, Abner, E, Menzies, G, Jin, LW, Leonenko, G, Jun, G, Grozeva, D, Karydas, A, Russo, G, Kaye, JA, Kim, R, Jessen, F, Kowall, NW, Vellas, B, Kramer, JH, Vardy, E, LaFerla, FM, Jöckel, KH, Lah, JJ, Dichgans, M, Leverenz, JB, Mann, D, Levey, AI, Pickering-Brown, S, Lieberman, AP, Klopp, N, Lunetta, KL, Wichmann, HE, Lyketsos, CG, Morgan, K, Marson, DC, Brown, K, Martiniuk, F, Medway, C, Mash, DC, Nöthen, MM, Masliah, E, Hooper, NM, McCormick, WC, Daniele, A, McCurry, SM, Bayer, A, McDavid, AN, Gallacher, J, McKee, AC, van den Bussche, H, Mesulam, M, Brayne, C, Miller, BL, Riedel-Heller, S, Miller, CA, Miller, JW, Al-Chalabi, A, Morris, JC, Shaw, CE, Myers, AJ, Wiltfang, J, O’Bryant, S, Coto, E, Olichney, JM, Alvarez, V, Parisi, JE, Singleton, AB, Paulson, HL, Collinge, J, Perry, W, Mead, S, Peskind, E, Rosser, M, Pierce, A, Ryan, N, Poon, WW, Nacmias, B, Potter, H, Sorbi, S, Quinn, JF, Sacchinelli, E, Raj, A, Spalletta, G, Raskind, M, Bossù, P, Reisberg, B, Clarke, R, Reitz, C, Smith, AD, Ringman, JM, Warden, D, Roberson, ED, Wilcock, G, Rogaeva, E, Bruni, AC, Rosen, HJ, Gallo, M, Rosenberg, RN, Ben-Shlomo, Y, Sager, MA, Mecocci, P, Saykin, AJ, Pastor, P, Cuccaro, ML, Vance, JM, Schneider, JA, Schneider, LS, Seeley, WW, Smith, AG, Sonnen, JA, Spina, S, Stern, RA, Swerdlow, RH, Tanzi, RE, Trojanowski, JQ, Troncoso, JC, Van Deerlin, VM, Van Eldik, LJ, Vinters, HV, Vonsattel, JP, Weintraub, S, Welsh-Bohmer, KA, Wilhelmsen, KC, Williamson, J, Wingo, TS, Woltjer, RL, Wright, CB, Yu, CE, Yu, L, Crane, PK, Bennett, DA, Boccardi, V, De Jager, PL, Warner, N, Lopez, OL, McDonough, S, Ingelsson, M, Deloukas, P, Cruchaga, C, Graff, C, Gwilliam, R, Fornage, M, Goate, AM, Sanchez-Juan, P, Kehoe, PG, Amin, N, Ertekin-Taner, N, Berr, C, Debette, S, Love, S, Launer, LJ, Younkin, SG, Dartigues, JF, Corcoran, C, Ikram, MA, Dickson, DW, Campion, D, Tschanz, J, Schmidt, H, Hakonarson, H, Munger, R, Schmidt, R, Farrer, LA, Van Broeckhoven, C, O’Donovan, MC, DeStefano, AL, Jones, L, Haines, JL, Deleuze, JF, Owen, MJ, Gudnason, V, Mayeux, R, Escott-Price, V, Psaty, BM, Ruiz, A, Ramirez, A, Wang, LS, van Duijn, CM, Holmans, PA, Seshadri, S, Williams, J, Amouyel, P, Schellenberg, GD, Lambert, JC, Pericak-Vance, MA, Bis, JC [0000-0002-3409-1110], Garnier, JG [0000-0003-4991-763X], Smith, AV [0000-0001-9088-234X], Denning, N [0000-0001-8467-7382], Vandenberghe, R [0000-0001-6237-2502], Himali, JJ [0000-0003-1391-9481], Rodriguez-Rodriguez, E [0000-0001-7742-677X], Frisardi, V [0000-0003-0764-7387], Ortega-Cubero, S [0000-0003-0520-9439], Hanon, O [0000-0002-4697-122X], Brice, A [0000-0002-0941-3990], Albin, RL [0000-0002-0629-608X], Buxbaum, JD [0000-0001-8898-8313], Bass, NJ [0000-0002-4481-778X], Fisher, E [0000-0003-2850-9936], Bayer, A [0000-0002-7514-248X], Gallacher, J [0000-0002-2394-5299], Brayne, C [0000-0001-5307-663X], Riedel-Heller, S [0000-0003-4321-6090], Al-Chalabi, A [0000-0002-4924-7712], and Apollo - University of Cambridge Repository

Subjects
Aging, 4202 Epidemiology, Genome-wide association study, Disease, Neurodegenerative, Biology, 3101 Biochemistry and Cell Biology, Alzheimer's Disease, 3105 Genetics, 03 medical and health sciences, 0302 clinical medicine, Clinical Research, Acquired Cognitive Impairment, Genetics, medicine, 2.1 Biological and endogenous factors, Dementia, Gene, 030304 developmental biology, Genetic association, 2 Aetiology, 0303 health sciences, Prevention, Human Genome, 42 Health Sciences, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Lipid metabolism, medicine.disease, Brain Disorders, 3. Good health, Meta-analysis, Neurological, Alzheimer's disease, 030217 neurology & neurosurgery, 31 Biological Sciences
Abstract

IntroductionLate-onset Alzheimer’s disease (LOAD, onset age > 60 years) is the most prevalent dementia in the elderly1, and risk is partially driven by genetics2. Many of the loci responsible for this genetic risk were identified by genome-wide association studies (GWAS)3–8. To identify additional LOAD risk loci, the we performed the largest GWAS to date (89,769 individuals), analyzing both common and rare variants. We confirm 20 previous LOAD risk loci and identify four new genome-wide loci (IQCK, ACE, ADAM10, and ADAMTS1). Pathway analysis of these data implicates the immune system and lipid metabolism, and for the first time tau binding proteins and APP metabolism. These findings show that genetic variants affecting APP and Aβ processing are not only associated with early-onset autosomal dominant AD but also with LOAD. Analysis of AD risk genes and pathways show enrichment for rare variants (P = 1.32 × 10−7) indicating that additional rare variants remain to be identified.

Published
2018
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