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Andrews SJ, Fulton-Howard B, O'Reilly P, Marcora E, Goate AM. Causal Associations Between Modifiable Risk Factors and the Alzheimer's Phenome. Ann Neurol 2021; 89:54-65. [PMID: 32996171 PMCID: PMC8088901 DOI: 10.1002/ana.25918] [Citation(s) in RCA: 63] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 09/22/2020] [Accepted: 09/23/2020] [Indexed: 12/11/2022]
Abstract
OBJECTIVE The purpose of this study was to infer causal relationships between 22 previously reported risk factors for Alzheimer's disease (AD) and the "AD phenome": AD, AD age of onset (AAOS), hippocampal volume, cortical surface area and thickness, cerebrospinal fluid (CSF) levels of amyloid-β (Aβ42 ), tau, and ptau181 , and the neuropathological burden of neuritic plaques, neurofibrillary tangles (NFTs), and vascular brain injury (VBI). METHODS Polygenic risk scores (PRS) for the 22 risk factors were computed in 26,431 AD cases/controls and the association with AD was evaluated using logistic regression. Two-sample Mendelian randomization (MR) was used to infer the causal effect of risk factors on the AD phenome. RESULTS PRS for increased education and diastolic blood pressure were associated with reduced risk for AD. MR indicated that only education was causally associated with reduced risk of AD, delayed AAOS, and increased cortical surface area and thickness. Total- and LDL-cholesterol levels were causally associated with increased neuritic plaque burden, although the effects were driven by single nucleotide polymorphisms (SNPs) within the APOE locus. Diastolic blood pressure and pulse pressure are causally associated with increased risk of VBI. Furthermore, total cholesterol was associated with decreased hippocampal volume; smoking initiation with decreased cortical thickness; type 2 diabetes with an earlier AAOS; and sleep duration with increased cortical thickness. INTERPRETATION Our comprehensive examination of the genetic evidence for the causal relationships between previously reported risk factors in AD using PRS and MR supports a causal role for education, blood pressure, cholesterol levels, smoking, and diabetes with the AD phenome. ANN NEUROL 2021;89:54-65.
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Munn‐Chernoff MA, Johnson EC, Chou Y, Coleman JR, Thornton LM, Walters RK, Yilmaz Z, Baker JH, Hübel C, Gordon S, Medland SE, Watson HJ, Gaspar HA, Bryois J, Hinney A, Leppä VM, Mattheisen M, Ripke S, Yao S, Giusti‐Rodríguez P, Hanscombe KB, Adan RA, Alfredsson L, Ando T, Andreassen OA, Berrettini WH, Boehm I, Boni C, Boraska Perica V, Buehren K, Burghardt R, Cassina M, Cichon S, Clementi M, Cone RD, Courtet P, Crow S, Crowley JJ, Danner UN, Davis OS, Zwaan M, Dedoussis G, Degortes D, DeSocio JE, Dick DM, Dikeos D, Dina C, Dmitrzak‐Weglarz M, Docampo E, Duncan LE, Egberts K, Ehrlich S, Escaramís G, Esko T, Estivill X, Farmer A, Favaro A, Fernández‐Aranda F, Fichter MM, Fischer K, Föcker M, Foretova L, Forstner AJ, Forzan M, Franklin CS, Gallinger S, Giegling I, Giuranna J, Gonidakis F, Gorwood P, Gratacos Mayora M, Guillaume S, Guo Y, Hakonarson H, Hatzikotoulas K, Hauser J, Hebebrand J, Helder SG, Herms S, Herpertz‐Dahlmann B, Herzog W, Huckins LM, Hudson JI, Imgart H, Inoko H, Janout V, Jiménez‐Murcia S, Julià A, Kalsi G, Kaminská D, Karhunen L, Karwautz A, Kas MJ, Kennedy JL, Keski‐Rahkonen A, Kiezebrink K, Kim Y, Klump KL, Knudsen GPS, La Via MC, Le Hellard S, Levitan RD, Li D, Lilenfeld L, Lin BD, Lissowska J, Luykx J, Magistretti PJ, Maj M, Mannik K, Marsal S, Marshall CR, Mattingsdal M, McDevitt S, McGuffin P, Metspalu A, Meulenbelt I, Micali N, Mitchell K, Monteleone AM, Monteleone P, Nacmias B, Navratilova M, Ntalla I, O'Toole JK, Ophoff RA, Padyukov L, Palotie A, Pantel J, Papezova H, Pinto D, Rabionet R, Raevuori A, Ramoz N, Reichborn‐Kjennerud T, Ricca V, Ripatti S, Ritschel F, Roberts M, Rotondo A, Rujescu D, Rybakowski F, Santonastaso P, Scherag A, Scherer SW, Schmidt U, Schork NJ, Schosser A, Seitz J, Slachtova L, Slagboom PE, Slof‐Op't Landt MC, Slopien A, Sorbi S, Świątkowska B, Szatkiewicz JP, Tachmazidou I, Tenconi E, Tortorella A, Tozzi F, Treasure J, Tsitsika A, Tyszkiewicz‐Nwafor M, Tziouvas K, Elburg AA, Furth EF, Wagner G, Walton E, Widen E, Zeggini E, Zerwas S, Zipfel S, Bergen AW, Boden JM, Brandt H, Crawford S, Halmi KA, Horwood LJ, Johnson C, Kaplan AS, Kaye WH, Mitchell J, Olsen CM, Pearson JF, Pedersen NL, Strober M, Werge T, Whiteman DC, Woodside DB, Grove J, Henders AK, Larsen JT, Parker R, Petersen LV, Jordan J, Kennedy MA, Birgegård A, Lichtenstein P, Norring C, Landén M, Mortensen PB, Polimanti R, McClintick JN, Adkins AE, Aliev F, Bacanu S, Batzler A, Bertelsen S, Biernacka JM, Bigdeli TB, Chen L, Clarke T, Degenhardt F, Docherty AR, Edwards AC, Foo JC, Fox L, Frank J, Hack LM, Hartmann AM, Hartz SM, Heilmann‐Heimbach S, Hodgkinson C, Hoffmann P, Hottenga J, Konte B, Lahti J, Lahti‐Pulkkinen M, Lai D, Ligthart L, Loukola A, Maher BS, Mbarek H, McIntosh AM, McQueen MB, Meyers JL, Milaneschi Y, Palviainen T, Peterson RE, Ryu E, Saccone NL, Salvatore JE, Sanchez‐Roige S, Schwandt M, Sherva R, Streit F, Strohmaier J, Thomas N, Wang J, Webb BT, Wedow R, Wetherill L, Wills AG, Zhou H, Boardman JD, Chen D, Choi D, Copeland WE, Culverhouse RC, Dahmen N, Degenhardt L, Domingue BW, Frye MA, Gäebel W, Hayward C, Ising M, Keyes M, Kiefer F, Koller G, Kramer J, Kuperman S, Lucae S, Lynskey MT, Maier W, Mann K, Männistö S, Müller‐Myhsok B, Murray AD, Nurnberger JI, Preuss U, Räikkönen K, Reynolds MD, Ridinger M, Scherbaum N, Schuckit MA, Soyka M, Treutlein J, Witt SH, Wodarz N, Zill P, Adkins DE, Boomsma DI, Bierut LJ, Brown SA, Bucholz KK, Costello EJ, Wit H, Diazgranados N, Eriksson JG, Farrer LA, Foroud TM, Gillespie NA, Goate AM, Goldman D, Grucza RA, Hancock DB, Harris KM, Hesselbrock V, Hewitt JK, Hopfer CJ, Iacono WG, Johnson EO, Karpyak VM, Kendler KS, Kranzler HR, Krauter K, Lind PA, McGue M, MacKillop J, Madden PA, Maes HH, Magnusson PK, Nelson EC, Nöthen MM, Palmer AA, Penninx BW, Porjesz B, Rice JP, Rietschel M, Riley BP, Rose RJ, Shen P, Silberg J, Stallings MC, Tarter RE, Vanyukov MM, Vrieze S, Wall TL, Whitfield JB, Zhao H, Neale BM, Wade TD, Heath AC, Montgomery GW, Martin NG, Sullivan PF, Kaprio J, Breen G, Gelernter J, Edenberg HJ, Bulik CM, Agrawal A. Shared genetic risk between eating disorder‐ and substance‐use‐related phenotypes: Evidence from genome‐wide association studies. Addict Biol 2021; 26:e12880. [DOI: 10.1111/adb.12880] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Revised: 12/09/2019] [Accepted: 01/13/2020] [Indexed: 02/01/2023]
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Andrews SJ, Goate AM. Mitochondrial DNA copy number is associated with cognitive impairment. Alzheimers Dement 2020. [DOI: 10.1002/alz.047543] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Hassenstab J, Aschenbrenner AJ, Balota DA, McDade E, Lim YY, Fagan AM, Benzinger TL, Cruchaga C, Goate AM, Morris JC, Bateman RJ. Remote cognitive assessment approaches in the Dominantly Inherited Alzheimer Network (DIAN). Alzheimers Dement 2020. [DOI: 10.1002/alz.038144] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Bateman RJ, Aschenbrenner AJ, Benzinger TL, Clifford D, Coalier K, Cruchaga C, Fagan AM, Farlow MR, Goate AM, Gordon BA, Hassenstab J, Jack CR, Koeppe RA, McDade E, Mills S, Morris JC, Salloway SP, Santacruz A, Snyder PJ, Wang G, Xiong C, Snider BJ, Mummery CJ, Surti GM, Hannequin D, Wallon D, Berman S, Lah JJ, Jiménez‐Velazquez IZ, Roberson ED, Dyck CH, Honig LS, Sanchez‐Valle R, Brooks WS, Gauthier S, Masters CL, Galasko DR, Brosch JR, Hsiung GR, Jayadev S, Formaglio M, Masellis M, Clarnette R, Pariente J, Dubois B, Pasquier F, Andersen SW, Holdridge KC, Mintun MA, Sims JR, Yaari R, Baudler M, Delmar P, Doody R, Fontoura P, Kerchner GA. Overview of dominantly inherited AD and top‐line DIAN‐TU results of solanezumab and gantenerumab. Alzheimers Dement 2020. [DOI: 10.1002/alz.041129] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Farlow MR, Bateman RJ, Aschenbrenner AJ, Benzinger TL, Clifford D, Coalier K, Cruchaga C, Fagan AM, Goate AM, Gordon BA, Hassenstab J, Jack CR, Koeppe RA, McDade E, Mills S, Morris JC, Salloway SP, Santacruz A, Snyder PJ, Wang G, Xiong C, Snider BJ, Mummery CJ, Surti GM, Hannequin D, Wallon D, Berman S, Lah JJ, Jiménez‐Velazquez IZ, Roberson ED, van Dyck CH, Honig LS, Sanchez‐Valle R, Brooks WS, Gauthier S, Masters CL, Galasko DR, Brosch JR, Hsiung GR, Jayadev S, Formaglio M, Masellis M, Clarnette R, Pariente J, Dubois B, Pasquier F, Andersen SW, Holdridge KC, Mintun MA, Sims JR, Yaari R. Solanezumab in‐depth outcomes. Alzheimers Dement 2020. [DOI: 10.1002/alz.038028] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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T.C.W. J, Qian L, Liang SA, Pipalia NH, Chao MJ, Shi Y, Bertelsen S, Kapoor M, Marcora E, Sikora E, Holtzman DM, Maxfield FR, Zhang B, Wang M, Poon WW, Goate AM. Human glia‐specific functional dysregulations affected by
APOE
ε4 risk of Alzheimer's disease. Alzheimers Dement 2020. [DOI: 10.1002/alz.040543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Ewers M, Frontzkowski L, Neitzel J, Rubinski A, Habeck C, Gordon BA, Benzinger TLS, Levin J, Cruchaga C, Goate AM, Fagan AM, Karch CM, Morris JC, Holtzman DM, Bateman RJ, Stern Y, Franzmeier N. Global system segregation enhances reserve in normal aging and Alzheimer’s disease. Alzheimers Dement 2020. [DOI: 10.1002/alz.037930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Arranz AM, Preman P, T.C.W. J, Snellinx A, Calafate S, Thal DR, Goate AM, De Strooper B. Chimeric models to analyze human neuron and astroglia responses in Alzheimer's disease. Alzheimers Dement 2020. [DOI: 10.1002/alz.042678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Pimenova AA, Herbinet M, Gupta I, Machlovi S, Marcora E, Goate AM. Protective low expression of PU.1 reduces microglial inflammatory and phagocytic response. Alzheimers Dement 2020. [DOI: 10.1002/alz.041201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Vermunt L, Sutphen CL, Dicks E, Cruchaga C, Ewers M, Goate AM, Hassenstab J, Jucker M, Karch CM, Kuhle J, McDade E, Morris JC, Perrin RJ, Preische O, Suárez‐Calvet M, Xiong C, Scheltens P, Visser PJ, Bateman RJ, Benzinger TL, Fagan AM, Gordon BA, Tijms BM. Cross‐modal associations between traditional and emerging CSF biomarkers and grey matter network disruption in autosomal dominant Alzheimer disease. Alzheimers Dement 2020. [DOI: 10.1002/alz.045905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Salloway SP, Bateman RJ, Aschenbrenner AJ, Benzinger TL, Clifford D, Coalier K, Cruchaga C, Fagan AM, Farlow MR, Goate AM, Gordon BA, Hassenstab J, Jack CR, Koeppe RA, McDade E, Mills S, Morris JC, Santacruz A, Snyder PJ, Wang G, Xiong C, Snider BJ, Mummery CJ, Surti GM, Hannequin D, Wallon D, Berman S, Lah JJ, Jiménez‐Velazquez IZ, Roberson ED, van Dyck CH, Honig LS, Sanchez‐Valle R, Brooks WS, Gauthier S, Masters CL, Galasko DR, Brosch JR, Hsiung GR, Jayadev S, Formaglio M, Masellis M, Clarnette R, Pariente J, Dubois B, Pasquier F, Baudler M, Delmar P, Doody R, Fontoura P, Kerchner GA. Gantenerumab in‐depth outcomes. Alzheimers Dement 2020. [DOI: 10.1002/alz.038049] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Podleśny-Drabiniok A, Marcora E, Goate AM. Microglial Phagocytosis: A Disease-Associated Process Emerging from Alzheimer’s Disease Genetics. Trends Neurosci 2020; 43:965-979. [DOI: 10.1016/j.tins.2020.10.002] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 09/02/2020] [Accepted: 10/05/2020] [Indexed: 01/02/2023]
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Johnson EC, Demontis D, Thorgeirsson TE, Walters RK, Polimanti R, Hatoum AS, Sanchez-Roige S, Paul SE, Wendt FR, Clarke TK, Lai D, Reginsson GW, Zhou H, He J, Baranger DAA, Gudbjartsson DF, Wedow R, Adkins DE, Adkins AE, Alexander J, Bacanu SA, Bigdeli TB, Boden J, Brown SA, Bucholz KK, Bybjerg-Grauholm J, Corley RP, Degenhardt L, Dick DM, Domingue BW, Fox L, Goate AM, Gordon SD, Hack LM, Hancock DB, Hartz SM, Hickie IB, Hougaard DM, Krauter K, Lind PA, McClintick JN, McQueen MB, Meyers JL, Montgomery GW, Mors O, Mortensen PB, Nordentoft M, Pearson JF, Peterson RE, Reynolds MD, Rice JP, Runarsdottir V, Saccone NL, Sherva R, Silberg JL, Tarter RE, Tyrfingsson T, Wall TL, Webb BT, Werge T, Wetherill L, Wright MJ, Zellers S, Adams MJ, Bierut LJ, Boardman JD, Copeland WE, Farrer LA, Foroud TM, Gillespie NA, Grucza RA, Harris KM, Heath AC, Hesselbrock V, Hewitt JK, Hopfer CJ, Horwood J, Iacono WG, Johnson EO, Kendler KS, Kennedy MA, Kranzler HR, Madden PAF, Maes HH, Maher BS, Martin NG, McGue M, McIntosh AM, Medland SE, Nelson EC, Porjesz B, Riley BP, Stallings MC, Vanyukov MM, Vrieze S, Davis LK, Bogdan R, Gelernter J, Edenberg HJ, Stefansson K, Børglum AD, Agrawal A. A large-scale genome-wide association study meta-analysis of cannabis use disorder. Lancet Psychiatry 2020; 7:1032-1045. [PMID: 33096046 PMCID: PMC7674631 DOI: 10.1016/s2215-0366(20)30339-4] [Citation(s) in RCA: 158] [Impact Index Per Article: 39.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 07/15/2020] [Accepted: 07/16/2020] [Indexed: 12/12/2022]
Abstract
BACKGROUND Variation in liability to cannabis use disorder has a strong genetic component (estimated twin and family heritability about 50-70%) and is associated with negative outcomes, including increased risk of psychopathology. The aim of the study was to conduct a large genome-wide association study (GWAS) to identify novel genetic variants associated with cannabis use disorder. METHODS To conduct this GWAS meta-analysis of cannabis use disorder and identify associations with genetic loci, we used samples from the Psychiatric Genomics Consortium Substance Use Disorders working group, iPSYCH, and deCODE (20 916 case samples, 363 116 control samples in total), contrasting cannabis use disorder cases with controls. To examine the genetic overlap between cannabis use disorder and 22 traits of interest (chosen because of previously published phenotypic correlations [eg, psychiatric disorders] or hypothesised associations [eg, chronotype] with cannabis use disorder), we used linkage disequilibrium score regression to calculate genetic correlations. FINDINGS We identified two genome-wide significant loci: a novel chromosome 7 locus (FOXP2, lead single-nucleotide polymorphism [SNP] rs7783012; odds ratio [OR] 1·11, 95% CI 1·07-1·15, p=1·84 × 10-9) and the previously identified chromosome 8 locus (near CHRNA2 and EPHX2, lead SNP rs4732724; OR 0·89, 95% CI 0·86-0·93, p=6·46 × 10-9). Cannabis use disorder and cannabis use were genetically correlated (rg 0·50, p=1·50 × 10-21), but they showed significantly different genetic correlations with 12 of the 22 traits we tested, suggesting at least partially different genetic underpinnings of cannabis use and cannabis use disorder. Cannabis use disorder was positively genetically correlated with other psychopathology, including ADHD, major depression, and schizophrenia. INTERPRETATION These findings support the theory that cannabis use disorder has shared genetic liability with other psychopathology, and there is a distinction between genetic liability to cannabis use and cannabis use disorder. FUNDING National Institute of Mental Health; National Institute on Alcohol Abuse and Alcoholism; National Institute on Drug Abuse; Center for Genomics and Personalized Medicine and the Centre for Integrative Sequencing; The European Commission, Horizon 2020; National Institute of Child Health and Human Development; Health Research Council of New Zealand; National Institute on Aging; Wellcome Trust Case Control Consortium; UK Research and Innovation Medical Research Council (UKRI MRC); The Brain & Behavior Research Foundation; National Institute on Deafness and Other Communication Disorders; Substance Abuse and Mental Health Services Administration (SAMHSA); National Institute of Biomedical Imaging and Bioengineering; National Health and Medical Research Council (NHMRC) Australia; Tobacco-Related Disease Research Program of the University of California; Families for Borderline Personality Disorder Research (Beth and Rob Elliott) 2018 NARSAD Young Investigator Grant; The National Child Health Research Foundation (Cure Kids); The Canterbury Medical Research Foundation; The New Zealand Lottery Grants Board; The University of Otago; The Carney Centre for Pharmacogenomics; The James Hume Bequest Fund; National Institutes of Health: Genes, Environment and Health Initiative; National Institutes of Health; National Cancer Institute; The William T Grant Foundation; Australian Research Council; The Virginia Tobacco Settlement Foundation; The VISN 1 and VISN 4 Mental Illness Research, Education, and Clinical Centers of the US Department of Veterans Affairs; The 5th Framework Programme (FP-5) GenomEUtwin Project; The Lundbeck Foundation; NIH-funded Shared Instrumentation Grant S10RR025141; Clinical Translational Sciences Award grants; National Institute of Neurological Disorders and Stroke; National Heart, Lung, and Blood Institute; National Institute of General Medical Sciences.
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Novikova G, Marcora E, Kapoor M, TCW J, Renton AE, Efthymiou AM, Abud EM, Bendl JM, Cheng HM, Fullard JF, Roussos P, Poon WW, Hao K, Goate AM. Integration of Alzheimer’s disease genetics and myeloid genomics reveals novel disease risk mechanisms. Alzheimers Dement 2020. [DOI: 10.1002/alz.043897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Zhao L, Zhang Z, Rodriguez SMB, Vardarajan BN, Renton AE, Goate AM, Mayeux R, Wang GT, Leal SM. A quantitative trait rare variant nonparametric linkage method with application to age-at-onset of Alzheimer's disease. Eur J Hum Genet 2020; 28:1734-1742. [PMID: 32740652 PMCID: PMC7785016 DOI: 10.1038/s41431-020-0703-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 07/09/2020] [Accepted: 07/22/2020] [Indexed: 12/18/2022] Open
Abstract
To analyze pedigrees with quantitative trait (QT) and sequence data, we developed a rare variant (RV) quantitative nonparametric linkage (QNPL) method, which evaluates sharing of minor alleles. RV-QNPL has greater power than the traditional QNPL that tests for excess sharing of minor and major alleles. RV-QNPL is robust to population substructure and admixture, locus heterogeneity, and inclusion of nonpathogenic variants and can be readily applied outside of coding regions. When QNPL was used to analyze common variants, it often led to loci mapping to large intervals, e.g., >40 Mb. In contrast, when RVs are analyzed, regions are well defined, e.g., a gene. Using simulation studies, we demonstrate that RV-QNPL is substantially more powerful than applying traditional QNPL methods to analyze RVs. RV-QNPL was also applied to analyze age-at-onset (AAO) data for 107 late-onset Alzheimer's disease (LOAD) pedigrees of Caribbean Hispanic and European ancestry with whole-genome sequence data. When AAO of AD was analyzed regardless of APOE ε4 status, suggestive linkage (LOD = 2.4) was observed with RVs in KNDC1 and nominally significant linkage (p < 0.05) was observed with RVs in LOAD genes ABCA7 and IQCK. When AAO of AD was analyzed for APOE ε4 positive family members, nominally significant linkage was observed with RVs in APOE, while when AAO of AD was analyzed for APOE ε4 negative family members, nominal significance was observed for IQCK and ADAMTS1. RV-QNPL provides a powerful resource to analyze QTs in families to elucidate their genetic etiology.
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Cai D, Huang M, Cao J, Hou J, Zhu L, Zhang L, Pero A, Guo L, T.C.W. J, Zhong M, Goate AM, Zhang B, Yan R. A novel interaction between AD risk genes synaptojanin 1 and reticulon‐3 potentially impacts synaptic function and endo‐lysosomal trafficking during disease development and progression. Alzheimers Dement 2020. [DOI: 10.1002/alz.043247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Zhang Q, Sidorenko J, Couvy-Duchesne B, Marioni RE, Wright MJ, Goate AM, Marcora E, Huang KL, Porter T, Laws SM, Sachdev PS, Mather KA, Armstrong NJ, Thalamuthu A, Brodaty H, Yengo L, Yang J, Wray NR, McRae AF, Visscher PM. Risk prediction of late-onset Alzheimer's disease implies an oligogenic architecture. Nat Commun 2020; 11:4799. [PMID: 32968074 PMCID: PMC7511365 DOI: 10.1038/s41467-020-18534-1] [Citation(s) in RCA: 83] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Accepted: 08/25/2020] [Indexed: 01/09/2023] Open
Abstract
Genetic association studies have identified 44 common genome-wide significant risk loci for late-onset Alzheimer's disease (LOAD). However, LOAD genetic architecture and prediction are unclear. Here we estimate the optimal P-threshold (Poptimal) of a genetic risk score (GRS) for prediction of LOAD in three independent datasets comprising 676 cases and 35,675 family history proxy cases. We show that the discriminative ability of GRS in LOAD prediction is maximised when selecting a small number of SNPs. Both simulation results and direct estimation indicate that the number of causal common SNPs for LOAD may be less than 100, suggesting LOAD is more oligogenic than polygenic. The best GRS explains approximately 75% of SNP-heritability, and individuals in the top decile of GRS have ten-fold increased odds when compared to those in the bottom decile. In addition, 14 variants are identified that contribute to both LOAD risk and age at onset of LOAD.
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Neuner SM, Tcw J, Goate AM. Genetic architecture of Alzheimer's disease. Neurobiol Dis 2020; 143:104976. [PMID: 32565066 PMCID: PMC7409822 DOI: 10.1016/j.nbd.2020.104976] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 05/30/2020] [Accepted: 06/13/2020] [Indexed: 02/06/2023] Open
Abstract
Advances in genetic and genomic technologies over the last thirty years have greatly enhanced our knowledge concerning the genetic architecture of Alzheimer's disease (AD). Several genes including APP, PSEN1, PSEN2, and APOE have been shown to exhibit large effects on disease susceptibility, with the remaining risk loci having much smaller effects on AD risk. Notably, common genetic variants impacting AD are not randomly distributed across the genome. Instead, these variants are enriched within regulatory elements active in human myeloid cells, and to a lesser extent liver cells, implicating these cell and tissue types as critical to disease etiology. Integrative approaches are emerging as highly effective for identifying the specific target genes through which AD risk variants act and will likely yield important insights related to potential therapeutic targets in the coming years. In the future, additional consideration of sex- and ethnicity-specific contributions to risk as well as the contribution of complex gene-gene and gene-environment interactions will likely be necessary to further improve our understanding of AD genetic architecture.
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Polimanti R, Walters RK, Johnson EC, McClintick JN, Adkins AE, Adkins DE, Bacanu SA, Bierut LJ, Bigdeli TB, Brown S, Bucholz KK, Copeland WE, Costello EJ, Degenhardt L, Farrer LA, Foroud TM, Fox L, Goate AM, Grucza R, Hack LM, Hancock DB, Hartz SM, Heath AC, Hewitt JK, Hopfer CJ, Johnson EO, Kendler KS, Kranzler HR, Krauter K, Lai D, Madden PAF, Martin NG, Maes HH, Nelson EC, Peterson RE, Porjesz B, Riley BP, Saccone N, Stallings M, Wall TL, Webb BT, Wetherill L, Edenberg HJ, Agrawal A, Gelernter J. Leveraging genome-wide data to investigate differences between opioid use vs. opioid dependence in 41,176 individuals from the Psychiatric Genomics Consortium. Mol Psychiatry 2020; 25:1673-1687. [PMID: 32099098 PMCID: PMC7392789 DOI: 10.1038/s41380-020-0677-9] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 01/15/2020] [Accepted: 01/30/2020] [Indexed: 01/17/2023]
Abstract
To provide insights into the biology of opioid dependence (OD) and opioid use (i.e., exposure, OE), we completed a genome-wide analysis comparing 4503 OD cases, 4173 opioid-exposed controls, and 32,500 opioid-unexposed controls, including participants of European and African descent (EUR and AFR, respectively). Among the variants identified, rs9291211 was associated with OE (exposed vs. unexposed controls; EUR z = -5.39, p = 7.2 × 10-8). This variant regulates the transcriptomic profiles of SLC30A9 and BEND4 in multiple brain tissues and was previously associated with depression, alcohol consumption, and neuroticism. A phenome-wide scan of rs9291211 in the UK Biobank (N > 360,000) found association of this variant with propensity to use dietary supplements (p = 1.68 × 10-8). With respect to the same OE phenotype in the gene-based analysis, we identified SDCCAG8 (EUR + AFR z = 4.69, p = 10-6), which was previously associated with educational attainment, risk-taking behaviors, and schizophrenia. In addition, rs201123820 showed a genome-wide significant difference between OD cases and unexposed controls (AFR z = 5.55, p = 2.9 × 10-8) and a significant association with musculoskeletal disorders in the UK Biobank (p = 4.88 × 10-7). A polygenic risk score (PRS) based on a GWAS of risk-tolerance (n = 466,571) was positively associated with OD (OD vs. unexposed controls, p = 8.1 × 10-5; OD cases vs. exposed controls, p = 0.054) and OE (exposed vs. unexposed controls, p = 3.6 × 10-5). A PRS based on a GWAS of neuroticism (n = 390,278) was positively associated with OD (OD vs. unexposed controls, p = 3.2 × 10-5; OD vs. exposed controls, p = 0.002) but not with OE (p = 0.67). Our analyses highlight the difference between dependence and exposure and the importance of considering the definition of controls in studies of addiction.
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Pimenova AA, Goate AM. Novel presenilin 1 and 2 double knock-out cell line for in vitro validation of PSEN1 and PSEN2 mutations. Neurobiol Dis 2020; 138:104785. [PMID: 32032730 PMCID: PMC7515654 DOI: 10.1016/j.nbd.2020.104785] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 01/05/2020] [Accepted: 01/31/2020] [Indexed: 12/23/2022] Open
Abstract
Mutations in APP (amyloid precursor protein), PSEN1 (presenilin 1) or PSEN2 (presenilin 2) are the main cause of early-onset familial forms of Alzheimer's disease (autosomal dominant AD or ADAD). These genes affect γ-secretase-dependent generation of Amyloid β (Aβ) peptides, the main constituent of amyloid plaques and one of the pathological hallmarks of AD. Evaluation of patients with ADAD includes assessment of family history, clinical presentation, biomarkers, neuropathology when available and DNA sequencing data. These analyses frequently uncover novel variants of unknown significance in ADAD genes. This presents a barrier to recruitment of such individuals into clinical trials, unless a biochemical test can demonstrate that a novel mutation results in altered APP processing in a manner consistent with pathogenicity. Here we describe generation and characterization of a novel presenilin 1 and 2 double knock-out in N2A mouse neuroblastoma cells using CRISPR/Cas9, which results in complete ablation of Aβ production, decreased Pen-2 expression and Nicastrin glycosylation. Because of the absence of background Aβ secretion from endogenous γ-secretases, these cells can be used for validation of PSEN1 and PSEN2 variant effects on production of Aβ or other γ-secretase substrates and for biochemical studies of γ-secretase function using novel variants. We examined several PSEN1 and PSEN2 mutations of known and unknown pathogenicity. Known mutants increased Aβ42/Aβ40 ratio with varying effect on Aβ40, Aβ42, total Aβ levels and Pen-2 expression, which aligns with previous work on these mutants. Our data on novel PSEN1 V142F, G206V and G206D mutations suggest that these mutations underlie the reported clinical observations in ADAD patients. We believe our novel cell line will be valuable for the scientific community for reliable validation of presenilin mutations and helpful in defining their pathogenicity to improve and facilitate evaluation of ADAD patients, particularly in the context of enrollment in clinical trials.
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Barthélemy NR, Li Y, Joseph-Mathurin N, Gordon BA, Hassenstab J, Benzinger TLS, Buckles V, Fagan AM, Perrin RJ, Goate AM, Morris JC, Karch CM, Xiong C, Allegri R, Mendez PC, Berman SB, Ikeuchi T, Mori H, Shimada H, Shoji M, Suzuki K, Noble J, Farlow M, Chhatwal J, Graff-Radford NR, Salloway S, Schofield PR, Masters CL, Martins RN, O'Connor A, Fox NC, Levin J, Jucker M, Gabelle A, Lehmann S, Sato C, Bateman RJ, McDade E. A soluble phosphorylated tau signature links tau, amyloid and the evolution of stages of dominantly inherited Alzheimer's disease. Nat Med 2020; 26:398-407. [PMID: 32161412 PMCID: PMC7309367 DOI: 10.1038/s41591-020-0781-z] [Citation(s) in RCA: 310] [Impact Index Per Article: 77.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2019] [Accepted: 01/30/2020] [Indexed: 12/31/2022]
Abstract
Development of tau-based therapies for Alzheimer's disease requires an understanding of the timing of disease-related changes in tau. We quantified the phosphorylation state at multiple sites of the tau protein in cerebrospinal fluid markers across four decades of disease progression in dominantly inherited Alzheimer's disease. We identified a pattern of tau staging where site-specific phosphorylation changes occur at different periods of disease progression and follow distinct trajectories over time. These tau phosphorylation state changes are uniquely associated with structural, metabolic, neurodegenerative and clinical markers of disease, and some (p-tau217 and p-tau181) begin with the initial increases in aggregate amyloid-β as early as two decades before the development of aggregated tau pathology. Others (p-tau205 and t-tau) increase with atrophy and hypometabolism closer to symptom onset. These findings provide insights into the pathways linking tau, amyloid-β and neurodegeneration, and may facilitate clinical trials of tau-based treatments.
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Hsu S, Pimenova AA, Hayes K, Villa JA, Rosene MJ, Jere M, Goate AM, Karch CM. Systematic validation of variants of unknown significance in APP, PSEN1 and PSEN2. Neurobiol Dis 2020; 139:104817. [PMID: 32087291 PMCID: PMC7236786 DOI: 10.1016/j.nbd.2020.104817] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2019] [Revised: 02/06/2020] [Accepted: 02/18/2020] [Indexed: 12/19/2022] Open
Abstract
Alzheimer’s disease (AD) is a neurodegenerative disease that is clinically characterized by progressive cognitive decline. More than 200 pathogenic mutations have been identified in amyloid-β precursor protein (APP), presenilin 1 (PSEN1) and presenilin 2 (PSEN2). Additionally, common and rare variants occur within APP, PSEN1, and PSEN2 that may be risk factors, protective factors, or benign, non-pathogenic polymorphisms. Yet, to date, no single study has carefully examined the effect of all of the variants of unknown significance reported in APP, PSEN1 and PSEN2 on Aβ isoform levels in vitro. In this study, we analyzed Aβ isoform levels by ELISA in a cell-based system in which each reported pathogenic and risk variant in APP, PSEN1, and PSEN2 was expressed individually. In order to classify variants for which limited family history data is available, we have implemented an algorithm for determining pathogenicity using available information from multiple domains, including genetic, bioinformatic, and in vitro analyses. We identified 90 variants of unknown significance and classified 19 as likely pathogenic mutations. We also propose that five variants are possibly protective. In defining a subset of these variants as pathogenic, individuals from these families may eligible to enroll in observational studies and clinical trials.
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Wetherill L, Lai D, Johnson EC, Anokhin A, Bauer L, Bucholz KK, Dick DM, Hariri AR, Hesselbrock V, Kamarajan C, Kramer J, Kuperman S, Meyers JL, Nurnberger JI, Schuckit M, Scott DM, Taylor RE, Tischfield J, Porjesz B, Goate AM, Edenberg HJ, Foroud T, Bogdan R, Agrawal A. ERRATUM: Genome-wide association study identifies loci associated with liability to alcohol and drug dependence that is associated with variability in reward-related ventral striatum activity in African- and European-Americans. GENES, BRAIN, AND BEHAVIOR 2019; 18:e12608. [PMID: 31667958 DOI: 10.1111/gbb.12608] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Karch CM, Kao AW, Karydas A, Onanuga K, Martinez R, Argouarch A, Wang C, Huang C, Sohn PD, Bowles KR, Spina S, Silva MC, Marsh JA, Hsu S, Pugh DA, Ghoshal N, Norton J, Huang Y, Lee SE, Seeley WW, Theofilas P, Grinberg LT, Moreno F, McIlroy K, Boeve BF, Cairns NJ, Crary JF, Haggarty SJ, Ichida JK, Kosik KS, Miller BL, Gan L, Goate AM, Temple S. A Comprehensive Resource for Induced Pluripotent Stem Cells from Patients with Primary Tauopathies. Stem Cell Reports 2019; 13:939-955. [PMID: 31631020 PMCID: PMC6895712 DOI: 10.1016/j.stemcr.2019.09.006] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Revised: 09/16/2019] [Accepted: 09/17/2019] [Indexed: 12/14/2022] Open
Abstract
Primary tauopathies are characterized neuropathologically by inclusions containing abnormal forms of the microtubule-associated protein tau (MAPT) and clinically by diverse neuropsychiatric, cognitive, and motor impairments. Autosomal dominant mutations in the MAPT gene cause heterogeneous forms of frontotemporal lobar degeneration with tauopathy (FTLD-Tau). Common and rare variants in the MAPT gene increase the risk for sporadic FTLD-Tau, including progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD). We generated a collection of fibroblasts from 140 MAPT mutation/risk variant carriers, PSP, CBD, and cognitively normal controls; 31 induced pluripotent stem cell (iPSC) lines from MAPT mutation carriers, non-carrier family members, and autopsy-confirmed PSP patients; 33 genome engineered iPSCs that were corrected or mutagenized; and forebrain neural progenitor cells (NPCs). Here, we present a resource of fibroblasts, iPSCs, and NPCs with comprehensive clinical histories that can be accessed by the scientific community for disease modeling and development of novel therapeutics for tauopathies. A collection of fibroblasts from 140 MAPT mutation carriers, PSP, CBD, and controls 31 iPSC lines reprogrammed from MAPT mutation carriers, PSP patients, and controls 33 iPSC lines engineered with CRISPR/Cas9 or TALENs Comprehensive resource for tauopathy modeling and discovery of novel therapeutics
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