Novel Alzheimer Disease Risk Loci and Pathways in African American Individuals Using the African Genome Resources Panel: A Meta-analysis | Dementia and Cognitive Impairment | JAMA Neurology | JAMA Network
[Skip to Navigation]
Sign In
Figure.  Association Plots From Single-Variant Meta-analysis
Association Plots From Single-Variant Meta-analysis

Manhattan plots showing negative log10–transformed P values from the single-variant meta-analysis adjusted for age, sex, and population stratification (A, model 1) and age, sex, population stratification, and APOE (B, model 2). The horizontal lines represent the genome-wide significance threshold (P = 5 × 10−8; red) and suggestive threshold (P = 1 × 10−6; orange). Loci are labeled with the closest gene(s) to the sentinel variant. Known loci are in blue and novel loci are in red. The y-axis is truncated, and the lowest P value on chromosome 19 was 1.8 × 10−25.

Table 1.  Results of SV Meta-analysis
Results of SV Meta-analysis
Table 2.  Novel Top Loci Identified in Gene-Based Analyses
Novel Top Loci Identified in Gene-Based Analyses
Table 3.  Association of Gene Expression at Suggestive Loci With Neuropathological Measures of Alzheimer Disease in the ROS/MAP Data Set31
Association of Gene Expression at Suggestive Loci With Neuropathological Measures of Alzheimer Disease in the ROS/MAP Data Set
Table 4.  Top Associated Pathways Derived From MAGMA Pathway Analysis
Top Associated Pathways Derived From MAGMA Pathway Analysis
Supplement 1.

eAppendix. Description of cohorts

eMethods.

eTable 1. Genotyping Platforms used in the individual data sets

eTable 2. Comparison of imputation quality of 1000G Phase 2 and AGR reference vs whole-exome sequencing data in 800 subjects

eTable 3. Demographic characteristics of data sets

eTable 4. APOEe4-stratified results for top loci

eTable 5. Sample sizes for APOEe4-stratified analyses

eTable 6. Single-marker meta-analysis results for previously reported variants

eTable 7. Gene-based results for AD genes previously identified in non-Hispanic Whites or African Americans

eTable 8. Results of top African-American (A) single variant associations, (B) gene-based associations and (C) pathways in the IGAP non-Hispanic white data set

eFigure 1. Regional association plots for the (A) three novel common and (B) seven rare loci identified in single-variant meta-analysis

eFigure 2. Forest Plots of Odds Ratios (ORs) for the (A) three novel common and (B) seven rare loci identified in single-variant meta-analysis

eFigure 3. Quantile-quantile plots for single marker association analyses based (A) on the model adjusted for age, sex and population stratification and (B) age, sex, population stratification and APOE showing the deviation of observed from expected P values

eFigure 4. Linkage disequilibrium analyses between the top associated variant in 19q13.33 (rs3745495) and three variants in APOE: A) The top associated AA variant within APOE (rs147491), and B) The two variants that define the APOE genotype (rs429358 and rs7412). Analyses were done using LDLink

eFigure 5. Manhattan plot of gene-based analysis results. Model 1 (a) is adjusted for age, sex and population stratification; Model 2 (b) is adjusted for age, sex, population stratification and APOE

eReferences.

1.
Seshadri  S, Fitzpatrick  AL, Ikram  MA,  et al; CHARGE Consortium; GERAD1 Consortium; EADI1 Consortium.  Genome-wide analysis of genetic loci associated with Alzheimer disease.   JAMA. 2010;303(18):1832-1840. doi:10.1001/jama.2010.574PubMedGoogle ScholarCrossref
2.
Naj  AC, Jun  G, Beecham  GW,  et al.  Common variants at MS4A4/MS4A6E, CD2AP, CD33 and EPHA1 are associated with late-onset Alzheimer’s disease.   Nat Genet. 2011;43(5):436-441. doi:10.1038/ng.801PubMedGoogle ScholarCrossref
3.
Harold  D, Abraham  R, Hollingworth  P,  et al.  Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer’s disease.   Nat Genet. 2009;41(10):1088-1093. doi:10.1038/ng.440PubMedGoogle ScholarCrossref
4.
Hollingworth  P, Harold  D, Sims  R,  et al; Alzheimer’s Disease Neuroimaging Initiative; CHARGE consortium; EADI1 consortium.  Common variants at ABCA7, MS4A6A/MS4A4E, EPHA1, CD33 and CD2AP are associated with Alzheimer’s disease.   Nat Genet. 2011;43(5):429-435. doi:10.1038/ng.803PubMedGoogle ScholarCrossref
5.
Jonsson  T, Stefansson  H, Steinberg  S,  et al.  Variant of TREM2 associated with the risk of Alzheimer’s disease.   N Engl J Med. 2013;368(2):107-116. doi:10.1056/NEJMoa1211103PubMedGoogle ScholarCrossref
6.
Lambert  JC, Heath  S, Even  G,  et al; European Alzheimer’s Disease Initiative Investigators.  Genome-wide association study identifies variants at CLU and CR1 associated with Alzheimer’s disease.   Nat Genet. 2009;41(10):1094-1099. doi:10.1038/ng.439PubMedGoogle ScholarCrossref
7.
Kunkle  BW, Grenier-Boley  B, Sims  R,  et al  Genetic meta-analysis of diagnosed Alzheimer's disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing.   Nat Genet. 2019;51(3):414-430. doi:10.1038/s41588-019-0358-2Google ScholarCrossref
8.
Sims  R, van der Lee  SJSJ, Naj  ACACAC,  et al; ARUK Consortium; GERAD/PERADES, CHARGE, ADGC, EADI.  Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer’s disease.   Nat Genet. 2017;49(9):1373-1384. doi:10.1038/ng.3916PubMedGoogle ScholarCrossref
9.
Ridge  PG, Mukherjee  S, Crane  PK, Kauwe  JS; Alzheimer’s Disease Genetics Consortium.  Alzheimer’s disease: analyzing the missing heritability.   PLoS One. 2013;8(11):e79771. doi:10.1371/journal.pone.0079771PubMedGoogle Scholar
10.
So  HC, Gui  AH, Cherny  SS, Sham  PC.  Evaluating the heritability explained by known susceptibility variants: a survey of ten complex diseases.   Genet Epidemiol. 2011;35(5):310-317. doi:10.1002/gepi.20579PubMedGoogle ScholarCrossref
11.
Gatz  M, Reynolds  CA, Fratiglioni  L,  et al.  Role of genes and environments for explaining Alzheimer disease.   Arch Gen Psychiatry. 2006;63(2):168-174. doi:10.1001/archpsyc.63.2.168PubMedGoogle ScholarCrossref
12.
Tang  M-X, Cross  P, Andrews  H,  et al.  Incidence of AD in African-Americans, Caribbean Hispanics, and Caucasians in northern Manhattan.   Neurology. 2001;56(1):49-56. doi:10.1212/WNL.56.1.49PubMedGoogle ScholarCrossref
13.
Hohman  TJ, Cooke-Bailey  JN, Reitz  C,  et al; Alzheimer Disease Genetics Consortium.  Global and local ancestry in African-Americans: implications for Alzheimer’s disease risk.   Alzheimers Dement. 2016;12(3):233-243. doi:10.1016/j.jalz.2015.02.012PubMedGoogle ScholarCrossref
14.
Reitz  C, Jun  G, Naj  A,  et al; Alzheimer Disease Genetics Consortium.  Variants in the ATP-binding cassette transporter (ABCA7), apolipoprotein E ϵ4,and the risk of late-onset Alzheimer disease in African Americans.   JAMA. 2013;309(14):1483-1492. doi:10.1001/jama.2013.2973PubMedGoogle ScholarCrossref
15.
Reitz  C, Mayeux  R; Alzheimer’s Disease Genetics Consortium.  TREM2 and neurodegenerative disease.   N Engl J Med. 2013;369(16):1564-1565. doi:10.1056/NEJMc1306509PubMedGoogle ScholarCrossref
16.
Rajabli  F, Feliciano  BE, Celis  K,  et al.  Ancestral origin of ApoE ε4 Alzheimer disease risk in Puerto Rican and African American populations.   PLoS Genet. 2018;14(12):e1007791. doi:10.1371/journal.pgen.1007791PubMedGoogle Scholar
17.
Jin  SC, Carrasquillo  MM, Benitez  BA,  et al.  TREM2 is associated with increased risk for Alzheimer’s disease in African Americans.   Mol Neurodegener. 2015;10(1):19. doi:10.1186/s13024-015-0016-9PubMedGoogle ScholarCrossref
18.
Cukier  HN, Kunkle  BW, Vardarajan  BN,  et al; Alzheimer’s Disease Genetics Consortium.  ABCA7 frameshift deletion associated with Alzheimer disease in African Americans.   Neurol Genet. 2016;2(3):e79. doi:10.1212/NXG.0000000000000079PubMedGoogle Scholar
19.
N’Songo  A, Carrasquillo  MM, Wang  X,  et al.  African American exome sequencing identifies potential risk variants at Alzheimer disease loci.   Neurol Genet. 2017;3(2):e141. doi:10.1212/NXG.0000000000000141PubMedGoogle Scholar
20.
Logue  MW, Schu  M, Vardarajan  BN,  et al; Alzheimer Disease Genetics Consortium; Alzheimer Disease Genetics Consortium.  Two rare AKAP9 variants are associated with Alzheimer’s disease in African Americans.   Alzheimers Dement. 2014;10(6):609-618.e11. doi:10.1016/j.jalz.2014.06.010PubMedGoogle ScholarCrossref
21.
Mez  J, Chung  J, Jun  G,  et al; Alzheimer’s Disease Genetics Consortium.  Two novel loci, COBL and SLC10A2, for Alzheimer’s disease in African Americans.   Alzheimers Dement. 2017;13(2):119-129. doi:10.1016/j.jalz.2016.09.002PubMedGoogle ScholarCrossref
22.
Santos  OA, Pedraza  O, Lucas  JA,  et al.  Ethnoracial differences in Alzheimer’s disease from the FLorida Autopsied Multi-Ethnic (FLAME) cohort.   Alzheimers Dement. 2019;15(5):635-643. doi:10.1016/j.jalz.2018.12.013PubMedGoogle ScholarCrossref
23.
Barnes  LL, Leurgans  S, Aggarwal  NT,  et al.  Mixed pathology is more likely in black than white decedents with Alzheimer dementia.   Neurology. 2015;85(6):528-534. doi:10.1212/WNL.0000000000001834PubMedGoogle ScholarCrossref
24.
Morris  JC, Schindler  SE, McCue  LM,  et al.  Assessment of racial disparities in biomarkers for Alzheimer disease.   JAMA Neurol. 2019;76(3):264-273. doi:10.1001/jamaneurol.2018.4249PubMedGoogle ScholarCrossref
25.
Filshtein  TJ, Dugger  BN, Jin  LW,  et al.  Neuropathological diagnoses of demented Hispanic, Black, and non-Hispanic white decedents seen at an Alzheimer’s disease center.   J Alzheimers Dis. 2019;68(1):145-158. doi:10.3233/JAD-180992PubMedGoogle ScholarCrossref
26.
Graff-Radford  NR, Besser  LM, Crook  JE, Kukull  WA, Dickson  DW.  Neuropathologic differences by race from the National Alzheimer’s Coordinating Center.   Alzheimers Dement. 2016;12(6):669-677. doi:10.1016/j.jalz.2016.03.004PubMedGoogle ScholarCrossref
27.
The African Partnership for Chronic Disease Research. Data. Accessed September 4, 2020. https://www.apcdr.org/data/
28.
Blacker  D, Bertram  L, Saunders  AJ,  et al; NIMH Genetics Initiative Alzheimer’s Disease Study Group.  Results of a high-resolution genome screen of 437 Alzheimer’s disease families.   Hum Mol Genet. 2003;12(1):23-32. doi:10.1093/hmg/ddg007PubMedGoogle ScholarCrossref
29.
Guerreiro  R, Wojtas  A, Bras  J,  et al; Alzheimer Genetic Analysis Group.  TREM2 variants in Alzheimer’s disease.   N Engl J Med. 2013;368(2):117-127. doi:10.1056/NEJMoa1211851PubMedGoogle ScholarCrossref
30.
NIAGADS. Explore genetics and genomics of Alzheimer’s Disease. Accessed September 4, 2020. https://www.niagads.org/
31.
Yu  L, Chibnik  LB, Srivastava  GP,  et al.  Association of Brain DNA methylation in SORL1, ABCA7, HLA-DRB5, SLC24A4, and BIN1 with pathological diagnosis of Alzheimer disease.   JAMA Neurol. 2015;72(1):15-24. doi:10.1001/jamaneurol.2014.3049PubMedGoogle ScholarCrossref
32.
de Leeuw  CA, Mooij  JM, Heskes  T, Posthuma  D.  MAGMA: generalized gene-set analysis of GWAS data.   PLoS Comput Biol. 2015;11(4):e1004219. doi:10.1371/journal.pcbi.1004219PubMedGoogle Scholar
33.
International Genomics of Alzheimer's Disease Consortium (IGAP); Jones  L, Lambert  J-C, Wang  L-S,  et al.  Convergent genetic and expression data implicate immunity in Alzheimer’s disease.   Alzheimers Dement. 2015;11(6). doi:10.1016/j.jalz.2014.05.1757Google Scholar
34.
Lambert  J-C, Ibrahim-Verbaas  CA, Harold  D,  et al; European Alzheimer’s Disease Initiative (EADI); Genetic and Environmental Risk in Alzheimer’s Disease; Alzheimer’s Disease Genetic Consortium; Cohorts for Heart and Aging Research in Genomic Epidemiology.  Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease.   Nat Genet. 2013;45(12):1452-1458. doi:10.1038/ng.2802PubMedGoogle ScholarCrossref
35.
Cummings  AC, Jiang  L, Velez Edwards  DR,  et al.  Genome-wide association and linkage study in the Amish detects a novel candidate late-onset Alzheimer disease gene.   Ann Hum Genet. 2012;76(5):342-351. doi:10.1111/j.1469-1809.2012.00721.xPubMedGoogle ScholarCrossref
36.
Lamriben  L, Oster  ME, Tamura  T,  et al.  EDEM1's mannosidase-like domain binds ERAD client proteins in a redox-sensitive manner and possesses catalytic activity.   J Biol Chem. 2018;293(36):13932-13945. doi:10.1074/jbc.RA118.004183PubMedGoogle ScholarCrossref
37.
Molinari  M, Calanca  V, Galli  C, Lucca  P, Paganetti  P.  Role of EDEM in the release of misfolded glycoproteins from the calnexin cycle.   Science. 2003;299(5611):1397-1400. doi:10.1126/science.1079474Google ScholarCrossref
38.
Cormier  JH, Tamura  T, Sunryd  JC, Hebert  DN.  EDEM1 recognition and delivery of misfolded proteins to the SEL1L-containing ERAD complex.   Mol Cell. 2009;34(5):627-633. doi:10.1016/j.molcel.2009.05.018PubMedGoogle ScholarCrossref
39.
Kaneko  M, Koike  H, Saito  R, Kitamura  Y, Okuma  Y, Nomura  Y.  Loss of HRD1-mediated protein degradation causes amyloid precursor protein accumulation and amyloid-beta generation.   J Neurosci. 2010;30(11):3924-3932. doi:10.1523/JNEUROSCI.2422-09.2010PubMedGoogle ScholarCrossref
40.
Kamboh  MI, Fan  KH, Yan  Q,  et al.  Population-based genome-wide association study of cognitive decline in older adults free of dementia: identification of a novel locus for the attention domain.   Neurobiol Aging. 2019;84:239.e15-239.e24. doi:10.1016/j.neurobiolaging.2019.02.024PubMedGoogle ScholarCrossref
41.
Allen  NJ, Bennett  ML, Foo  LC,  et al.  Astrocyte glypicans 4 and 6 promote formation of excitatory synapses via GluA1 AMPA receptors.   Nature. 2012;486(7403):410-414. doi:10.1038/nature11059PubMedGoogle ScholarCrossref
42.
Kang  TH, Kim  KT.  Negative regulation of ERK activity by VRK3-mediated activation of VHR phosphatase.   Nat Cell Biol. 2006;8(8):863-869. doi:10.1038/ncb1447PubMedGoogle ScholarCrossref
43.
Wu  GY, Deisseroth  K, Tsien  RW.  Spaced stimuli stabilize MAPK pathway activation and its effects on dendritic morphology.   Nat Neurosci. 2001;4(2):151-158. doi:10.1038/83976PubMedGoogle ScholarCrossref
44.
Thomas  GM, Huganir  RL.  MAPK cascade signalling and synaptic plasticity.   Nat Rev Neurosci. 2004;5(3):173-183. doi:10.1038/nrn1346PubMedGoogle ScholarCrossref
45.
Song  H, Kim  W, Kim  SH, Kim  KT.  VRK3-mediated nuclear localization of HSP70 prevents glutamate excitotoxicity-induced apoptosis and Aβ accumulation via enhancement of ERK phosphatase VHR activity.   Sci Rep. 2016;6:38452. doi:10.1038/srep38452PubMedGoogle ScholarCrossref
46.
van Horssen  J, Wesseling  P, van den Heuvel  LP, de Waal  RM, Verbeek  MM.  Heparan sulphate proteoglycans in Alzheimer’s disease and amyloid-related disorders.   Lancet Neurol. 2003;2(8):482-492. doi:10.1016/S1474-4422(03)00484-8PubMedGoogle ScholarCrossref
47.
van Horssen  J, Otte-Höller  I, David  G,  et al.  Heparan sulfate proteoglycan expression in cerebrovascular amyloid beta deposits in Alzheimer’s disease and hereditary cerebral hemorrhage with amyloidosis (Dutch) brains.   Acta Neuropathol. 2001;102(6):604-614. doi:10.1007/s004010100414PubMedGoogle ScholarCrossref
48.
van Horssen  J, Kleinnijenhuis  J, Maass  CN,  et al.  Accumulation of heparan sulfate proteoglycans in cerebellar senile plaques.   Neurobiol Aging. 2002;23(4):537-545. doi:10.1016/S0197-4580(02)00010-6PubMedGoogle ScholarCrossref
49.
Beckman  M, Holsinger  RM, Small  DH.  Heparin activates beta-secretase (BACE1) of Alzheimer’s disease and increases autocatalysis of the enzyme.   Biochemistry. 2006;45(21):6703-6714. doi:10.1021/bi052498tPubMedGoogle ScholarCrossref
50.
Leveugle  B, Ding  W, Durkin  JT,  et al.  Heparin promotes beta-secretase cleavage of the Alzheimer’s amyloid precursor protein.   Neurochem Int. 1997;30(6):543-548. doi:10.1016/S0197-0186(96)00103-9PubMedGoogle ScholarCrossref
51.
Castillo  GM, Ngo  C, Cummings  J, Wight  TN, Snow  AD.  Perlecan binds to the beta-amyloid proteins (A beta) of Alzheimer’s disease, accelerates A beta fibril formation, and maintains A beta fibril stability.   J Neurochem. 1997;69(6):2452-2465. doi:10.1046/j.1471-4159.1997.69062452.xPubMedGoogle ScholarCrossref
52.
Karczewski  KJ, Francioli  LC, Tiao  G,  et al.  Variation across 141,456 human exomes and genomes reveals the spectrum of loss-of-function intolerance across human protein-coding genes.  bioRxiv. Preprint posted online August 13, 2019. doi:10.1101/531210
53.
Tatsuoka  C, Tseng  H, Jaeger  J,  et al; Alzheimer’s Disease Neuroimaging Initiative.  Modeling the heterogeneity in risk of progression to Alzheimer’s disease across cognitive profiles in mild cognitive impairment.   Alzheimers Res Ther. 2013;5(2):14. doi:10.1186/alzrt168PubMedGoogle ScholarCrossref
54.
Zhang  H, Zhou  H, Lencz  T, Farrer  LA, Kranzler  HR, Gelernter  J.  Genome-wide association study of cognitive flexibility assessed by the Wisconsin Card Sorting Test.   Am J Med Genet B Neuropsychiatr Genet. 2018;177(5):511-519. doi:10.1002/ajmg.b.32642PubMedGoogle ScholarCrossref
55.
Holzenberger  M, Dupont  J, Ducos  B,  et al.  IGF-1 receptor regulates lifespan and resistance to oxidative stress in mice.   Nature. 2003;421(6919):182-187. doi:10.1038/nature01298PubMedGoogle ScholarCrossref
56.
Talbot  K, Wang  H-Y, Kazi  H,  et al.  Demonstrated brain insulin resistance in Alzheimer’s disease patients is associated with IGF-1 resistance, IRS-1 dysregulation, and cognitive decline.   J Clin Invest. 2012;122(4):1316-1338. doi:10.1172/JCI59903PubMedGoogle ScholarCrossref
57.
Freude  S, Hettich  MM, Schumann  C,  et al.  Neuronal IGF-1 resistance reduces A beta accumulation and protects against premature death in a model of Alzheimer’s disease.   FASEB J. 2009;23(10):3315-3324. doi:10.1096/fj.09-132043PubMedGoogle ScholarCrossref
58.
De Magalhaes Filho  CD, Kappeler  L, Dupont  J,  et al.  Deleting IGF-1 receptor from forebrain neurons confers neuroprotection during stroke and upregulates endocrine somatotropin.   J Cereb Blood Flow Metab. 2017;37(2):396-412. doi:10.1177/0271678X15626718PubMedGoogle ScholarCrossref
59.
Cohen  E, Paulsson  JF, Blinder  P,  et al.  Reduced IGF-1 signaling delays age-associated proteotoxicity in mice.   Cell. 2009;139(6):1157-1169. doi:10.1016/j.cell.2009.11.014PubMedGoogle ScholarCrossref
60.
Gontier  G, George  C, Chaker  Z, Holzenberger  M, Aïd  S.  Blocking IGF signaling in adult neurons alleviates Alzheimer’s Disease pathology through amyloid-β clearance.   J Neurosci. 2015;35(33):11500-11513. doi:10.1523/JNEUROSCI.0343-15.2015PubMedGoogle ScholarCrossref
61.
Garcia-Jove Navarro  M, Basset  C, Arcondéguy  T,  et al.  Api5 contributes to E2F1 control of the G1/S cell cycle phase transition.   PLoS One. 2013;8(8):e71443. doi:10.1371/journal.pone.0071443PubMedGoogle Scholar
62.
Auweter  SD, Fasan  R, Reymond  L,  et al.  Molecular basis of RNA recognition by the human alternative splicing factor Fox-1.   EMBO J. 2006;25(1):163-173. doi:10.1038/sj.emboj.7600918PubMedGoogle ScholarCrossref
63.
Hamada  N, Ito  H, Iwamoto  I, Morishita  R, Tabata  H, Nagata  K.  Role of the cytoplasmic isoform of RBFOX1/A2BP1 in establishing the architecture of the developing cerebral cortex.   Mol Autism. 2015;6:56. doi:10.1186/s13229-015-0049-5PubMedGoogle ScholarCrossref
64.
Gandal  MJ, Zhang  P, Hadjimichael  E,  et al.  Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder.   Science. 2018;362(6420):eaat8127. doi:10.1126/science.aat8127Google Scholar
65.
Alkallas  R, Fish  L, Goodarzi  H, Najafabadi  HS.  Inference of RNA decay rate from transcriptional profiling highlights the regulatory programs of Alzheimer’s disease.   Nat Commun. 2017;8(1):909. doi:10.1038/s41467-017-00867-zPubMedGoogle ScholarCrossref
66.
Lee  JA, Damianov  A, Lin  CH,  et al.  Cytoplasmic Rbfox1 regulates the expression of synaptic and autism-related genes.   Neuron. 2016;89(1):113-128. doi:10.1016/j.neuron.2015.11.025PubMedGoogle ScholarCrossref
67.
Alam  S, Suzuki  H, Tsukahara  T.  Alternative splicing regulation of APP exon 7 by RBFox proteins.   Neurochem Int. 2014;78:7-17. doi:10.1016/j.neuint.2014.08.001PubMedGoogle ScholarCrossref
68.
Raghavan  NS, Dumitrescu  L, Mormino  E,  et al; Alzheimer’s Disease Neuroimaging Initiative.  Association between common variants in RBFOX1, an RNA-binding protein, and brain amyloidosis in early and preclinical Alzheimer disease.   JAMA Neurol. Published online June 22, 2020. doi:10.1001/jamaneurol.2020.1760Google Scholar
69.
Forstner  AJ, Hecker  J, Hofmann  A,  et al.  Identification of shared risk loci and pathways for bipolar disorder and schizophrenia.   PLoS One. 2017;12(2):e0171595. doi:10.1371/journal.pone.0171595PubMedGoogle Scholar
70.
Chen  DT, Jiang  X, Akula  N,  et al; BiGS.  Genome-wide association study meta-analysis of European and Asian-ancestry samples identifies three novel loci associated with bipolar disorder.   Mol Psychiatry. 2013;18(2):195-205. doi:10.1038/mp.2011.157PubMedGoogle ScholarCrossref
71.
Ruderfer  DM, Fanous  AH, Ripke  S,  et al; Schizophrenia Working Group of the Psychiatric Genomics Consortium; Bipolar Disorder Working Group of the Psychiatric Genomics Consortium; Cross-Disorder Working Group of the Psychiatric Genomics Consortium.  Polygenic dissection of diagnosis and clinical dimensions of bipolar disorder and schizophrenia.   Mol Psychiatry. 2014;19(9):1017-1024. doi:10.1038/mp.2013.138PubMedGoogle ScholarCrossref
72.
Goes  FS, Hamshere  ML, Seifuddin  F,  et al; Bipolar Genome Study (BiGS).  Genome-wide association of mood-incongruent psychotic bipolar disorder.   Transl Psychiatry. 2012;2:e180. doi:10.1038/tp.2012.106PubMedGoogle Scholar
73.
Jiang  X, Detera-Wadleigh  SD, Akula  N,  et al.  Sodium valproate rescues expression of TRANK1 in iPSC-derived neural cells that carry a genetic variant associated with serious mental illness.   Mol Psychiatry. 2019;24(4):613-624. doi:10.1038/s41380-018-0207-1PubMedGoogle ScholarCrossref
74.
Laster  M, Shen  JI, Norris  KC.  Kidney disease among African Americans: a population perspective.   Am J Kidney Dis. 2018;72(5)(suppl 1):S3-S7. doi:10.1053/j.ajkd.2018.06.021PubMedGoogle ScholarCrossref
75.
McAdams-DeMarco  MA, Daubresse  M, Bae  S, Gross  AL, Carlson  MC, Segev  DL.  Dementia, Alzheimer’s disease, and mortality after hemodialysis initiation.   Clin J Am Soc Nephrol. 2018;13(9):1339-1347. doi:10.2215/CJN.10150917PubMedGoogle ScholarCrossref
76.
Pirici  D, Stanaszek  L, Garz  C,  et al.  Common impact of chronic kidney disease and brain microhemorrhages on cerebral Aβ pathology in SHRSP.   Brain Pathol. 2017;27(2):169-180. doi:10.1111/bpa.12384PubMedGoogle ScholarCrossref
77.
Beach  TG, Monsell  SE, Phillips  LE, Kukull  W.  Accuracy of the clinical diagnosis of Alzheimer disease at National Institute on Aging Alzheimer Disease Centers, 2005-2010.   J Neuropathol Exp Neurol. 2012;71(4):266-273. doi:10.1097/NEN.0b013e31824b211bPubMedGoogle ScholarCrossref
78.
Bennett  DA, Schneider  JA, Arvanitakis  Z, Wilson  RS.  Overview and findings from the religious orders study.   Curr Alzheimer Res. 2012;9(6):628-645. doi:10.2174/156720512801322573PubMedGoogle ScholarCrossref
Limit 200 characters
Limit 25 characters
Conflicts of Interest Disclosure

Identify all potential conflicts of interest that might be relevant to your comment.

Conflicts of interest comprise financial interests, activities, and relationships within the past 3 years including but not limited to employment, affiliation, grants or funding, consultancies, honoraria or payment, speaker's bureaus, stock ownership or options, expert testimony, royalties, donation of medical equipment, or patents planned, pending, or issued.

Err on the side of full disclosure.

If you have no conflicts of interest, check "No potential conflicts of interest" in the box below. The information will be posted with your response.

Not all submitted comments are published. Please see our commenting policy for details.

Limit 140 characters
Limit 3600 characters or approximately 600 words
    Original Investigation
    October 19, 2020

    Novel Alzheimer Disease Risk Loci and Pathways in African American Individuals Using the African Genome Resources Panel: A Meta-analysis

    Author Affiliations
    • 1The John P. Hussman Institute for Human Genomics, University of Miami, Miami, Florida
    • 2Dr. John T. MacDonald Foundation, Department of Human Genetics, University of Miami, Miami, Florida
    • 3Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, New York, New York
    • 4Gertrude H. Sergievsky Center, Columbia University, New York, New York
    • 5Department of Neurology, Columbia University, New York, New York
    • 6Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia
    • 7Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia
    • 8Department of Medicine, University of Washington, Seattle
    • 9Group Health Research Institute, Group Health, Seattle, Washington
    • 10Rush Institute for Healthy Aging, Rush University Medical Center, Chicago, Illinois
    • 11Department of Internal Medicine, Rush University Medical Center, Chicago, Illinois
    • 12Department of Psychiatry, Mount Sinai School of Medicine, New York, New York
    • 13Department of Genetics and Genomics Sciences, Mount Sinai School of Medicine, New York, New York
    • 14Department of Neuroscience, Mount Sinai School of Medicine, New York, New York
    • 15Friedman Brain Institute, Mount Sinai School of Medicine, New York, New York
    • 16Department of Neuroscience, Mayo Clinic, Jacksonville, Florida
    • 17Department of Neurology, Mayo Clinic, Jacksonville, Florida
    • 18Department of Neurological Sciences, Rush University Medical Center, Chicago, Illinois
    • 19Department of Behavioral Sciences, Rush University Medical Center, Chicago, Illinois
    • 20Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois
    • 21Department of Epidemiology, Johns Hopkins University School of Public Health, Baltimore, Maryland
    • 22Department of Epidemiology, University of Alabama at Birmingham, Birmingham
    • 23Howard University, Howard University Hospital, Washington, DC
    • 24Department of Human Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania
    • 25Alzheimer’s Disease Research Center, University of Pittsburgh, Pittsburgh, Pennsylvania
    • 26Department of Neurological Sciences, Rush University Medical Center, Chicago, Illinois
    • 27Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois
    • 28Department of Psychiatry, Indiana University School of Medicine, Indianapolis
    • 29Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, New York
    • 30Department of Medical and Molecular Genetics, Indiana University, Indianapolis
    • 31Maya Angelou Center for Health Equity, Wake Forest School of Medicine, Winston-Salem, North Carolina
    • 32Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, Massachusetts
    • 33Department of Neurology, Boston University School of Medicine, Boston, Massachusetts
    • 34Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
    • 35Department of Ophthalmology, Boston University School of Medicine, Boston, Massachusetts
    • 36Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts
    • 37Department of Population and Quantitative Health Sciences, Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio
    • 38Department of Psychiatry, Columbia University, New York, New York
    • 39Epidemiology, College of Physicians and Surgeons, Columbia University, New York, New York
    JAMA Neurol. 2021;78(1):102-113. doi:10.1001/jamaneurol.2020.3536
    Key Points

    Question  What genetic variants, genes, and pathways increase or decrease risk of Alzheimer disease in African American individuals?

    Findings  In this genome-wide association meta-analysis of 2748 individuals with Alzheimer disease and 5222 controls, several novel genetic loci and pathways associated with Alzheimer disease in African American individuals were identified.

    Meaning  While the major pathways involved in Alzheimer disease etiology in African American individuals are largely similar to those in non-Hispanic White individuals, many of the disease-associated loci within these pathways differ.

    Abstract

    Importance  Compared with non-Hispanic White individuals, African American individuals from the same community are approximately twice as likely to develop Alzheimer disease. Despite this disparity, the largest Alzheimer disease genome-wide association studies to date have been conducted in non-Hispanic White individuals. In the largest association analyses of Alzheimer disease in African American individuals, ABCA7, TREM2, and an intergenic locus at 5q35 were previously implicated.

    Objective  To identify additional risk loci in African American individuals by increasing the sample size and using the African Genome Resource panel.

    Design, Setting, and Participants  This genome-wide association meta-analysis used case-control and family-based data sets from the Alzheimer Disease Genetics Consortium. There were multiple recruitment sites throughout the United States that included individuals with Alzheimer disease and controls of African American ancestry. Analysis began October 2018 and ended September 2019.

    Main Outcomes and Measures  Diagnosis of Alzheimer disease.

    Results  A total of 2784 individuals with Alzheimer disease (1944 female [69.8%]) and 5222 controls (3743 female [71.7%]) were analyzed (mean [SD] age at last evaluation, 74.2 [13.6] years). Associations with 4 novel common loci centered near the intracellular glycoprotein trafficking gene EDEM1 (3p26; P = 8.9 × 10−7), near the immune response gene ALCAM (3q13; P = 9.3 × 10−7), within GPC6 (13q31; P = 4.1 × 10−7), a gene critical for recruitment of glutamatergic receptors to the neuronal membrane, and within VRK3 (19q13.33; P = 3.5 × 10−7), a gene involved in glutamate neurotoxicity, were identified. In addition, several loci associated with rare variants, including a genome-wide significant intergenic locus near IGF1R at 15q26 (P = 1.7 × 10−9) and 6 additional loci with suggestive significance (P ≤ 5 × 10−7) such as API5 at 11p12 (P = 8.8 × 10−8) and RBFOX1 at 16p13 (P = 5.4 × 10−7) were identified. Gene expression data from brain tissue demonstrate association of ALCAM, ARAP1, GPC6, and RBFOX1 with brain β-amyloid load. Of 25 known loci associated with Alzheimer disease in non-Hispanic White individuals, only APOE, ABCA7, TREM2, BIN1, CD2AP, FERMT2, and WWOX were implicated at a nominal significance level or stronger in African American individuals. Pathway analyses strongly support the notion that immunity, lipid processing, and intracellular trafficking pathways underlying Alzheimer disease in African American individuals overlap with those observed in non-Hispanic White individuals. A new pathway emerging from these analyses is the kidney system, suggesting a novel mechanism for Alzheimer disease that needs further exploration.

    Conclusions and Relevance  While the major pathways involved in Alzheimer disease etiology in African American individuals are similar to those in non-Hispanic White individuals, the disease-associated loci within these pathways differ.

    Introduction

    Large-scale genomic studies identified more than 20 modest-effect Alzheimer disease (AD) risk loci besides the APOE gene.1-8 However, these studies were predominantly conducted in individuals of non-Hispanic White ancestry and, taken together, the identified loci explain only 30% to 40% of the genetic contribution to AD,9,10 substantially less than the heritability estimates from twin studies ranging from 60% to 80%.11 Compared with non-Hispanic White individuals, African American individuals from the same community are twice as likely to develop AD.12 Supporting the notion that there are many genetic loci with small effect sizes contributing to these observed ancestral differences in disease risk, we recently demonstrated that AD cases in African American individuals show higher levels of African ancestry than unaffected individuals, both globally and locally at AD-relevant loci.13

    Various additional observations provide support for the genetic architecture of AD being partially ancestry-specific. In the largest genome-wide association study conducted to date and to our knowledge in African American individuals comprising 5896 participants from the Alzheimer Disease Genetics Consortium, we previously confirmed ABCA7 and APOE, notably with substantial differences in odds ratios compared with non-Hispanic White individuals, and identified a novel intergenic locus at 5q35.14 Of the additional common loci originally discovered in data sets of non-Hispanic White individuals, only a subset (CR1, BIN1, EPHA1, CD33, TREM2) replicated with nominal significance.14,15 Importantly, the population differences in the effect of APOE ɛ4 appear to be explained by the ancestral background on which the allele lies, as we have recently shown that APOE ɛ4 alleles on an African background confer lower risk than those on a non-Hispanic White background.16 Population-specific associations with AD in rare or low-frequency variants also have been identified. Several rare risk variants found in non-Hispanic White individuals do not show association with risk in African American individuals, possibly because they are extremely rare in African American individuals.15,17 In a recent targeted sequencing study of ABCA7, we identified a novel 44 base pair frameshift deletion (rs142076058) in ABCA7 in the same linkage disequilibrium block as the African American individuals’ variant (rs115550680) identified by our previous genome-wide association study (GWAS)18 that is common and associated with disease in African American individuals but present in very few non-Hispanic White individuals (minor allele frequency [MAF] = 0.12%). Two additional missense variants in ABCA7 have been associated with AD in a separate African American population.19 Finally, separate association studies of AD in African American individuals identified novel rare and low-frequency associations in AKAP9,20 COBL, and SLC10A221 that appear specific to African American individuals. Neuropathologic differences in AD between populations22-26 may also point toward population-specific risk loci for AD. The aim of the present analyses is to identify additional loci modulating risk in African American individuals.

    Methods

    To identify additional AD risk loci in African American individuals, we conducted a GWAS meta-analysis with a 37% increased sample size including individuals with AD and controls recruited from several case-control and family-based studies of African American individuals. A detailed description of the original cohorts and summary demographics of all samples included in this analysis are provided in the eAppendix and eTables 1-3 in Supplement 1. Written informed consent was obtained from all participants, and all study protocols were approved by the respective institutional review boards. Imputation was performed with the African Genome Resources panel,27 which contains all African and non-African populations from 1000 Genomes phase 3 and more than 2000 individuals from various African regions, providing better coverage of ancestral haplotypes than the 1000 Genomes–based reference panels used in previous studies. In line with this notion, comparison of imputation quality of 1000 Genomes and African Genome Resources vs available whole-exome sequencing data in 800 participants demonstrated higher accuracy in the African Genome Resources (eTable 2 in Supplement 1). The final single-nucleotide variant set for analysis included 29 610 185 genotyped and imputed variants, more than doubling the number of variants from our previous analysis.14 Genotype dosages were analyzed within each data set and subsequently meta-analyzed, adjusting for age, sex, and PCs for population substructure (model 1), and subsequently in addition for APOE genotype (model 2). Additional details on these analyses and the methods for gene, pathway, and expression association analyses can be found in the eMethods in Supplement 1. P values were 2-sided, and the standard GWAS threshold of 5 × 10−8 was used to define genome-wide significance. Analysis started October 2018 and ended September 2019.

    Results

    A total of 2784 individuals (1944 female [69.8%]) with AD and 5222 (3743 female [71.7%]) were analyzed (mean [SD] age at last evaluation, 74.2 [13.6] years). Single-variant meta-analyses replicated the APOE locus and both African American individuals’ risk loci (rs115550680 [ABCA7] and rs145848414 [5q35]) from our previous analyses at P < 5 × 10−6 (Table 1 and Figure).14 In addition, single-marker meta-analyses yielded 1 novel genome-wide significant (P ≤ 5 × 10−8) disease locus associated with rare variants and 10 novel disease-associated loci (4 common variant loci, 6 rare variant loci) associated at P ≤ 5 × 10−7 (Table 1; eFigures 1-2 in Supplement 1). There was no evidence for genomic inflation (model 1: λ = 0.94; model 2: λ = 0.96); see eFigure 3 in Supplement 1 for QQ plots). The 4 common loci were centered at (1) EDEM1 on chromosome 3p26 (rs168193; MAF = 0.25; P = 8.9 × 10−7), a known linkage region for AD,28 (2) ALCAM on chromosome 3q13 (rs2633682; MAF = 0.33; P = 9.3 × 10−7), (3) within GPC6 on chromosome 13q31 (rs9516245; MAF = 0.04; P = 4.1 × 10−7), and (4) within VRK3 on chromosome 19q13.33 (rs3745495; MAF = 0.10; P = 3.5 × 10−7). Three of 4 loci have strong regional support by variants in linkage disequilibrium (eFigure 1A in Supplement 1), and all 4 have consistent directions of effect across most individual data sets (eFigure 2A in Supplement 1). While VRK3 is located approximately 5 megabases downstream of APOE, the APOE-adjusted model and analyses showing that rs3745495 is not in linkage disequilibrium with variants within APOE (eFigure 4 in Supplement 1) suggest that it represents an independent AD-associated signal in African American individuals. The identified rare variants include a genome-wide significant intergenic locus at 15q26 close to ARRDC4 and IGF1R (rs570487962; MAF = 0.01; P = 1.69 × 10−9) and 6 loci with associations of P < 5 × 10−7 close to SIPA1L2, WDR70, API5, ACER3, PIK3C2G, and RBFOX1 (Table 1 and eFigures 1 and 2 in Supplement 1). Repeating the analyses stratified by APOEe4 carrier status revealed the association with RBFOX1 was only present in cases without an APOEe4 allele, while the intergenic association at ARRDC4/IGF1R was only found in carriers of the APOEe4 allele (eTables 4-5 in Supplement 1). These results should be interpreted with caution because of the small sample sizes obtained after stratification on APOEe4 status (eTable 5 in Supplement 1).

    Of the variants previously implicated in AD in African American individuals by other studies,15,17,20,21 rs112404845 in COBL showed association (P = 5.4 × 10−6), and 2 variants each in TREM2 (rs7748513; P = 3.6 × 10−5 and rs2234256; P = .001) and AKAP9 (rs149979685; P = .005 and rs914662445; P = .01) were replicated with at P < .05 (eTable 6 in Supplement 1). A low-frequency TREM2 stop-gain variant previously reported at P = .08 in a sample of 906 load cases and 2487 controls, was associated at P = 1.4 × 10−3 (rs2234258).17 Of the GWAS loci implicated in non-Hispanic White individuals besides APOE and ABCA7,7 only the variants in BIN1 (P = 9 × 10−4), CD2AP (P = .02), FERMT2 (P = .01), and WWOX (P = .04) showed nominal association in this African American sample (eTable 6 in Supplement 1).

    Gene-Based Analyses

    Gene-based analyses confirmed at gene-wide significance the TREM2 gene, originally identified in non-Hispanic White populations,5,29 as an AD risk locus in African American individuals (P = 9.89 × 10−6) and identified 8 loci (TRANK1, FABP2, LARP1B, TSRM, ARAP1, STARD10, SPHK1, and SERPINB13) with associations of P ≤ 1 × 10−4 (Table 2 and eFigure 2 in Supplement 1). Of the other risk loci previously reported in African American or non-Hispanic White individuals besides TREM2, only C2DAP (P = .03) was significant at P ≤ .05 (eTable 7 in Supplement 1). Full summary statistics for the complete set of single-marker and gene-based analyses are available through the National Institute on Aging Genetics of Alzheimer Disease Data Storage Site.30

    Validation and Prioritization of Identified Loci

    To validate the identified loci and evaluate their biological significance, we examined differential expression of amyloid and tau pathology in AD vs control brains and conducted pathway analyses.

    To explore differential expression, we capitalized on postmortem brain pathology quantified by immunohistochemistry and expression data from 478 individuals of European ancestry from the ROS/MAP study.31 Covarying for sex, age at death (age at last visit for clinical AD diagnosis), postmortem interval, RNA integrity, APOE ε4 status, and first 3 genomic principal components, higher expression of ALCAM (β = 0.038; P = .003) and ARAP1 (β = 0.058; P = 2.0 × 10−4) and lower expression of GPC6 (β = −0.035; P = .001) and RBFOX1 (β = −0.055; P = .001) were associated with brain amyloid load after correction for multiple testing (Table 3; Bonferroni P value threshold for significance: P = .05/19 tested genes = .003). Higher expression of STARD10 was associated with higher tau pathology burden (β = 0.050; P = 8.46 × 10−5). When covarying in addition for differences in cell type composition across samples, associations for ALCAM (β = 0.033; P = .004), ARAP1 (β = 0.06; P = 9.3 × 10−6), GPC6 (β = −0.034; P = .002), and RBFOX1 (β = −0.050; P = .001) with brain amyloid load remained unchanged; association of STARD10 with tau pathology burden was slightly attenuated (β = 0.03; P = .01).

    Pathway analyses conducted using Multi-marker Analysis of GenoMic Annotation32 identified 8 main functional groups at P < 1 × 10−3 (Table 4): (1) intracellular trafficking, (2) lipid and phospholipid metabolism, (3) transcription/DNA repair, (4) nervous system development/synaptic plasticity, (5) cell division, (6) immune response, (7) cellular signaling, and (8) kidney system development. With the exception of kidney system development, these pathways overlap with the key molecular mechanisms identified in the large-scale genomic studies in non-Hispanic White individuals.7,33 However, enrichment of amyloid precursor protein/amyloid (A)-β and tau pathways, which recently emerged as top molecular pathways in the large-scale rare variant meta-analysis in non-Hispanic White individuals conducted by the International Genomics of Alzheimer Project (IGAP),7 are notably absent among the top disease-associated pathways observed in this data set of African American individuals.

    Examination of Identified Single-Variant, Gene-Based, and Pathway Associations in the IGAP Data Set of Non-Hispanic White Individuals

    Comparison of the top single-variant associations in African American individuals with the results of the latest GWAS of non-Hispanic White individuals from the IGAP consortium (n = 94 437)7 revealed nominal replication of the single-variant association in the WDR70 gene (P = .05) and nearly nominal replication of the RBFOX1 locus (P = .07) (eTable 8 in Supplement 1). Gene-based testing of loci resulting from the single-variant analysis of African American individuals revealed PIK3C2G and GPC6 to have significance at P < .05. For the gene-based loci, the only result with nominal replication in IGAP was STARD10 (P = .02), although ARAP1 also approached significance at P < .05. Of the 21 pathways associated at P < 10−4 in African American individuals, only 2 replicated at a nominal level: inositol tetrakisphosphate phosphatase activity (P = .02) and positive regulation of nuclear division (P = .05), suggesting that while the major pathways are similar between African American and non-Hispanic White individuals, the subpathways defining these functions may differ slightly because of the specific genes involved.

    Discussion

    In the largest AD GWAS study on African American individuals conducted to date and to our knowledge, we confirmed ABCA7, the intergenic locus on chromosome 5q35, and several variants in or near COBL, TREM2, and AKAP9 as associated with AD and identified 1 novel genome-wide significant disease locus and 10 novel disease-associated loci associated at P ≤ 5 × 10−7. Gene-based analyses also confirmed TREM2 as a risk locus in this population and nominated 8 additional loci with associations of P ≤ 1 × 10−4. For 4 of these 8 novel loci, gene expression analysis from brain tissue demonstrated significant association with burden of brain amyloid (ALCAM, ARAP1, GPC6, RBFOX1), a key pathological hallmark of AD. Of 25 known loci in non-Hispanic White individuals,7,34 only APOE, ABCA7, TREM2, BIN1, CD2AP, FERMT2, and WWOX were implicated at a nominal significance level or stronger in this African American sample.

    Notably, the majority of novel loci identified in this study cluster in pathways was also implicated in non-Hispanic White individuals. The 4 common loci that were, because of their high allele frequencies, robustly present in all contributing data sets, cluster near or in genes involved in intracellular trafficking, immune response, and glutamatergic synaptic transmission. EDEM1, located in a known AD linkage region (3q26),35 encodes a protein that sequesters misfolded proteins, including the amyloid precursor protein, away from productive folding cycles and redirects them to endoplasmic reticulum–associated degradation.36-38 There is evidence that upregulation of endoplasmic reticulum–associated degradation leads to amyloid precursor protein degradation and reduced Aβ production.39 ALCAM encodes CD166 antigen promoting T-cell activation, maturation of the immunological synapse, and axon growth. A recent GWAS on cognitive decline in older adults free of dementia identified a suggestive signal in the ALCAM gene region (rs34476301; P = 6.5 × 10−6) associated with longitudinal changes in the memory domain.40 GPC6 encodes glypican 6 belonging to a conserved family of heparan sulfate proteoglycans. Secreted by astrocytes, GPC6 regulates recruitment of glutamate (GluA1 AMPA) receptors to the neuronal surface and promotes formation of excitatory synapses in neurons.41 The locus at 19q13 shows different local ancestry with regard to AD status in African American individuals,13 providing significant support for the importance of this region in AD etiology in this ethnic group. The signal falls within VRK3 encoding a serine/threonine kinase modulating the activity of extracellular signal-regulated kinases42 involved in the regulation of synaptic protein synthesis, dendritic morphology, and synaptic plasticity.43,44 Dysregulation of glutamate-induced extracellular signal-regulated kinase signaling via VRK3 is associated with Aβ accumulation,45 and VRK3 itself has been suggested as a potential therapeutic target for AD.45 In expression data from the ROS/MAP study,31 ALCAM and GPC6 expression was associated with amount of brain amyloid pathology. Codeposition and association of various heparan sulfate proteoglycans with Aβ has long been described,46-48 and in vitro studies have shown that heparan sulfate proteoglycans can regulate Aβ production49,50 and aggregation.49,51

    The 7 identified rare variant loci include the 2 top loci identified in this study, centered in a noncoding RNA (LINC02254) near the IGF1R gene on chromosome 15q26 (P = 1.69 × 10−9) and API5 on chromosome 11p12 (P = 8.81 × 10−8). The associated variants near IGF1R are all African-specific according to the Genome Aggregation Database.52 Interestingly, a GWAS of cognitive flexibility, an AD-linked phenotype,53 identified a genome-wide significant association approximately 80 kilobases upstream of this rare variant signal in African American individuals but not in non-Hispanic White individuals,54 lending support to this locus as an AD locus specific to African American individuals. IGF1R is a receptor for insulinlike growth factor I (IGF-I) controlling stress resistance, aging, and lifespan.55 Brains of individuals with AD show abnormalities in IGF1R expression and downstream signaling molecules, insulin and IGF1 resistance,56,57 and long-term inhibition of IGF signaling supports neuronal function and neuroprotection.57,58 Lifespan-extending heterozygous IGF1R knockout alleviates AD pathology through Aβ clearance,56 confers neuroprotection against Aβ proteotoxicity, and improves behavior in mice with AD.59,60 In this study, association of IGF1R expression with amyloid load was close to Bonferroni-corrected significance (P = .005). Apoptosis inhibitor-5 (API5) is a nuclear protein highly expressed in the brain whose expression prevents apoptotic cell death.61

    While the top disease-associated variant at the chromosome 16p13 locus is located approximately 500 kilobases downstream of RBFOX1, analysis of expression data and findings from epidemiologic, animal, and experimental studies nominate RBFOX1 as a potential candidate gene at this locus that warrants further scrutiny. RBFOX1 is a critical regulator of splicing and cytoplasmic mRNA stability in neurons62,63 that has been implicated across a series of neurodevelopmental and psychiatric disorders.64 There is evidence from experimental studies that downregulation of RBFOX1 leads to destabilization of messenger RNAs encoding for proteins involved in synaptic transmission and diminished synaptic function in AD65,66 and that RBFOX1 might regulate splicing of amyloid precursor protein.67 Notably, a GWAS of positron emission tomography amyloid levels in individuals without dementia reported by Raghavan et al68 nominates RBFOX1 as a locus for brain amyloidosis, in line with this notion and our GWAS and brain amyloid pathology analyses.

    Gene-based analyses confirmed TREM2 as an AD risk gene in African American individuals and identified an additional 8 novel loci with associations of P ≤ 1 × 10−4 (eFigure 5 in Supplement 1). Notably, also these genes largely cluster in AD pathways implicated by genomic studies in non-Hispanic White populations. While TRANK1 at 3p22.2 is a known GWAS risk locus for bipolar disorder and schizophrenia69-72 and potentially modulates expression of genes involved in neural development and differentiation,73 FABP2 and STARD10 are involved in lipid metabolism, SPHK1 and SERPINB13 in immune response, LARP1B in RNA transcription, and ARAP1 in endocytosis and intracellular trafficking. Finally, the results of our pathway analyses also support the notion that the principal molecular pathways (eg, immunity, lipid processing, intracellular trafficking) underlying AD in African American individuals overlap with those observed in non-Hispanic White individuals, albeit largely with different disease-associated genes within these pathways. A novel AD pathway emerging from this pathway analyses is kidney system development. This finding is particularly interesting given the observation that African American individuals are 3 times more likely to experience kidney failure compared with the non-Hispanic White population,74 and along with Hispanic populations, have a higher rate of comorbidity for dementia and kidney disease.75 Impaired kidney clearance of peripherally circulating Aβ results in elevated cerebral Aβ retention.76 Determining the contribution of this comorbid condition to AD risk, and whether misdiagnosis of AD plays a role in this association,77 could have important implications for the prevention and treatment of AD in African American individuals.

    Compared with our previous analyses based on the 1000 Genomes panel (June 2011), the African Genome Resources reference panel used in the current analysis allowed us to include both a higher number of common variants and a significant set of low-frequency variants previously not included. While some of the newly identified common variants were assessed in the previous analyses but now reached genome-wide significance because of increased statistical power, most of the newly identified variants with rarer minor allele frequencies were previously not assessed. For all novel identified disease-associated variants, imputation quality was excellent. There was also no evidence of inflation in our study when including low-frequency variants, minimizing the likelihood that the observed associations are spurious.

    Limitations

    This study has limitations. First, given the paucity of available African American samples for genomic research on AD and the need to maximize sample size to reach sufficient statistical power to identify variants with low frequency or effect sizes, we combined all samples into 1 discovery set and relied on the IGAP data on non-Hispanic White individuals and ROS/MAP brain expression data sets for replication.7,78 Additional validation will likely need to be derived from experimental studies. Second, while this is the largest GWAS data set on African American individuals to date and to our knowledge, our sample size was underpowered to detect associations with very rare single variants or rare variants exerting very small effects. Consequently, it is possible that there remain unidentified disease-associated variants.

    Conclusions

    Our study strongly suggests that the principal molecular pathways implicated in AD etiology in African American individuals largely overlap with those in non-Hispanic White individuals but that the disease-associated loci within these pathways differ. These observations are critical for several reasons. First, they provide significant support for the importance of native immune response, intracellular trafficking, lipid metabolism, nervous system development, and synaptic plasticity in AD etiology and suggest that these pathways are not ethnicity-specific but critical in disease etiology across ethnic groups. Second, this study suggests that there might also be pathways whose contributions to disease differ between ethnic groups. While amyloid and tau pathology did not emerge as top pathways in this data set on African American individuals, kidney system development was identified as a novel, plausible disease mechanism. Interestingly, cerebrospinal fluid concentrations of tau have been observed to be lower in African American individuals affected with AD compared with non-Hispanic White individuals with AD.24 Finally, these observations strongly suggest that polygenic risk scores developed for non-Hispanic White populations will likely not be applicable to this ethnic group and vice versa but that polygenic risk scores need to be developed and applied as ethnic group–specific. While additional validation is needed, the identified genomic loci and pathways significantly help to disentangle AD etiology in African American individuals, aid to clarify the molecular mechanisms underlying observed health disparities, and help to pinpoint molecular targets for therapeutic intervention in this ethnic group.

    Back to top
    Article Information

    Corresponding Authors: Brian W. Kunkle, PhD, MPH, John P. Hussman Institute for Human Genomics and Dr. John T. MacDonald Foundation, Department of Human Genetics, Miller School of Medicine, University of Miami, 1501 NW 10th Ave, Miami, FL 33136 (bkunkle@miami.edu); Christiane Reitz, MD, PhD, Departments of Neurology and Epidemiology, Sergievsky Center, Taub Institute for Research on the Aging Brain, Columbia University, 630 W 168th St, New York, NY 10032 (cr2101@cumc.columbia.edu).

    Accepted for Publication: April 9, 2020.

    Published Online: October 19, 2020. doi:10.1001/jamaneurol.2020.3536

    Correction: This article was corrected on March 22, 2021, to fix a spelling error in the byline.

    Writing Committee for the Alzheimer’s Disease Genetics Consortium (ADGC): Neill R. Graff-Radford, MD; Izri Martinez, MD; Temitope Ayodele, MS; Mark W. Logue, PhD; Laura B. Cantwell, MPH; Melissa Jean-Francois, MPH; Amanda B. Kuzma, MS; L.D. Adams, BA; Jeffery M. Vance, MD, PhD; Michael L. Cuccaro, PhD; Jaeyoon Chung, PhD; Jesse Mez, MD; Kathryn L. Lunetta, PhD; Gyungah R. Jun, PhD; Oscar L. Lopez, MD; Hugh C. Hendrie, MD; Eric M. Reiman, MD; Neil W. Kowall, MD; James B. Leverenz, MD; Scott A. Small, MD; Allan I. Levey, MD, PhD; Todd E. Golde, MD, PhD; Andrew J. Saykin, PsyD; Takiyah D. Starks, MS; Marilyn S. Albert, PhD; Bradley T. Hyman, MD, PhD; Ronald C. Petersen, MD, PhD; Mary Sano, PhD; Thomas Wisniewski, MD; Robert Vassar, PhD; Jeffrey A. Kaye, MD; Victor W. Henderson, MD, MS; Charles DeCarli, MD; Frank M. LaFerla, PhD; James B. Brewer, MD, PhD; Bruce L. Miller, MD; Russell H. Swerdlow, MD; Linda J. Van Eldik, PhD; Henry L. Paulson, MD, PhD; John Q. Trojanowski, MD, PhD; Helena C. Chui, MD; Roger N. Rosenberg, MD; Suzanne Craft, PhD; Thomas J. Grabowski, MD; Sanjay Asthana, MD; John C. Morris, MD; Stephen M. Strittmatter, MD, PhD; Walter A. Kukull, PhD.

    Affiliations of Writing Committee for the Alzheimer’s Disease Genetics Consortium (ADGC): The John P. Hussman Institute for Human Genomics, University of Miami, Miami, Florida (Jean-Francois, Adams, Vance, Cuccaro); Dr. John T. MacDonald Foundation, Department of Human Genetics, University of Miami, Miami, Florida (Vance, Cuccaro); Gertrude H. Sergievsky Center, Columbia University, New York, New York (Martinez, Ayodele); Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia (Cantwell, Kuzma); Department of Psychiatry, Mount Sinai School of Medicine, New York, New York (Sano); Department of Neuroscience, Mayo Clinic, Jacksonville, Florida (Graff-Radford); Department of Neurology, Mayo Clinic, Jacksonville, Florida (Graff-Radford); Alzheimer’s Disease Research Center, University of Pittsburgh, Pittsburgh, Pennsylvania (Lopez); Department of Psychiatry, Indiana University School of Medicine, Indianapolis (Hendrie); Department of Medical and Molecular Genetics, Indiana University, Indianapolis (Saykin); Maya Angelou Center for Health Equity, Wake Forest School of Medicine, Winston-Salem, North Carolina (Starks); Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, Massachusetts (Logue, Chung); Department of Neurology, Boston University School of Medicine, Boston, Massachusetts (Mez, Jun); Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts (Lunetta, Jun); Department of Ophthalmology, Boston University School of Medicine, Boston, Massachusetts (Jun); National Center for PTSD, VA Boston Healthcare System, Boston, Massachusetts (Logue); Department of Psychiatry, Boston University School of Medicine, Boston, Massachusetts (Logue); Arizona Alzheimer’s Center, Banner Alzheimer’s Institute, Phoenix (Reiman); Boston University, Boston VA Medical Center, Jamaica Plain, Massachusetts (Kowall); Cleveland Clinic, Cleveland, Ohio (Leverenz); Columbia University Alzheimer’s Disease Research Center, New York, New York (Small); Department of Neurology, Emory University, Atlanta, Georgia (Levey); Center for Translational Research in Neurodegenerative Disease (CTRND), University of Florida, Gainesville (Golde); Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis (Saykin); Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis (Saykin); Johns Hopkins University School of Medicine, Baltimore, Maryland (Albert); Massachusetts Alzheimer’s Disease Research Center, Department of Neurology, Massachusetts General Hospital, Charlestown (Hyman); Department of Neurology, Mayo Clinic, Rochester, Minnesota (Petersen); Center for Cognitive Neurology, New York University, New York (Wisniewski); Department of Neurology, Northwestern University, Chicago, Illinois (Vassar); Aging & Alzheimer Disease Center, Oregon Health & Science University, Portland (Kaye); Department of Epidemiology & Population Health, Stanford University, Stanford, California (Henderson); Department of Neurology & Neurological Sciences, Stanford University, Stanford, California (Henderson); University of California Davis at Medical Center, Sacramento (DeCarli); University of California Irvine, Irvine, (LaFerla); Shiley-Marcos Alzheimer’s Disease Center, UC San Diego, La Jolla, California (Brewer); University of California San Francisco, San Francisco (Miller); Alzheimer’s Disease Research Center, University of Kansas, Kansas City (Swerdlow); Sanders-Brown Center on Aging, University of Kentucky, Lexington (Van Eldik); Alzheimer Disease Center, University of Michigan, Ann Arbor (Paulson); Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia (Trojanowski); University of Southern California, Los Angeles (Chui); University of Texas Southwestern Medical Center, Dallas (Rosenberg); Wake Forest School of Medicine, Winston-Salem, North Carolina (Craft); Department of Radiology, University of Washington, Seattle (Grabowski); Department of Neurology, University of Washington, Seattle (Grabowski); University of Wisconsin, Madison (Asthana); Department of Neurology, Washington University School of Medicine, St Louis, Missouri (Morris); Department of Neurology, Yale University School of Medicine, New Haven, Connecticut (Strittmatter); National Alzheimer’s Coordinating Center, University of Washington, Seattle (Kukull); Department of Epidemiology, University of Washington, Seattle (Kukull).

    Author Contributions: Drs Kunkle and Reitz had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Kunkle, Lunetta, Goate, Haines, Pericak-Vance, Reitz.

    Acquisition, analysis, or interpretation of data: Kunkle, Schmidt, Klein, Naj, Hamilton-Nelson, Larson, Graff-Radford, Evans, De Jager, Martinez, Ayodele, Crane, Buxbaum, Ertekin-Taner, Logue, Barnes, Cantwell, Jean-Francois, Kuzma, Adams, Fallin, Vance, Cuccaro, Manly, Chung, Mez, Go, Obisesan, Jun, Kamboh, Lopez, Bennett, Hall, Hendrie, Reiman, Kowall, Leverenz, Small, Levey, Golde, Saykin, Starks, Albert, Hyman, Petersen, Sano, Wisniewski, Vassar, Kaye, Henderson, DeCarli, LaFerla, Brewer, Miller, Swerdlow, Van Eldik, Paulson, Trojanowski, Chui, Rosenberg, Craft, Grabowski, Asthana, Morris, Strittmatter, Kukull, Foroud, Martin, Wang, Byrd, Farrer, Haines, Schellenberg, Mayeux, Pericak-Vance, Reitz.

    Drafting of the manuscript: Kunkle, Klein, Hamilton-Nelson, Adams, Chung, Small, LaFerla, Miller, Goate, Schellenberg, Reitz.

    Critical revision of the manuscript for important intellectual content: Kunkle, Schmidt, Klein, Naj, Larson, Graff-Radford, Evans, De Jager, Martinez, Ayodele, Crane, Buxbaum, Ertekin-Taner, Logue, Barnes, Cantwell, Jean-Francois, Kuzma, Fallin, Vance, Cuccaro, Manly, Mez, Go, Obisesan, Lunetta, Jun, Kamboh, Lopez, Bennett, Hall, Hendrie, Reiman, Kowall, Leverenz, Levey, Golde, Saykin, Starks, Albert, Hyman, Petersen, Sano, Wisniewski, Vassar, Kaye, Henderson, DeCarli, Brewer, Swerdlow, Van Eldik, Paulson, Trojanowski, Chui, Rosenberg, Craft, Grabowski, Asthana, Morris, Strittmatter, Kukull, Foroud, Martin, Wang, Byrd, Farrer, Haines, Mayeux, Pericak-Vance, Reitz.

    Statistical analysis: Kunkle, Schmidt, Klein, Naj, Hamilton-Nelson, De Jager, Logue, Jean-Francois, Jun, Trojanowski, Haines, Reitz.

    Obtained funding: Larson, Evans, Crane, Buxbaum, Ertekin-Taner, Fallin, Manly, Go, Kamboh, Lopez, Bennett, Hall, Reiman, Leverenz, Small, Hyman, Petersen, Wisniewski, Rosenberg, Grabowski, Morris, Strittmatter, Wang, Farrer, Haines, Mayeux, Pericak-Vance, Reitz.

    Administrative, technical, or material support: Naj, Larson, Evans, Martinez, Ayodele, Crane, Ertekin-Taner, Cantwell, Kuzma, Go, Obisesan, Kamboh, Lopez, Hall, Hendrie, Reiman, Kowall, Levey, Golde, Starks, Wisniewski, Vassar, Kaye, DeCarli, Swerdlow, Van Eldik, Chui, Kukull, Foroud, Martin, Wang, Byrd, Schellenberg, Mayeux.

    Supervision: Kunkle, Vance, Obisesan, Lunetta, Kamboh, Bennett, Sano, Miller, Trojanowski, Rosenberg, Morris, Strittmatter, Mayeux, Pericak-Vance, Reitz.

    Conflict of Interest Disclosures: Dr Kunkle reported grants from the National Institute of Aging (NIA) during the conduct of the study. Dr Schmidt reported grants from the National Institutes of Health (NIH) during the conduct of the study. Dr Larson reported grants from NIA during the conduct of the study. Dr Graff-Radford reported grants from NIH during the conduct of the study; grants from AbbVie, Biogen, Eli Lilly and Company, and Novartis outside the submitted work. Dr Evans reported grants from NIH during the conduct of the study. Dr Vance reported grants from NIH/NIA during the conduct of the study. Dr Cuccaro reported grants from NIH during the conduct of the study and other support from John P. Hussman Foundation outside the submitted work. Dr Manly reported grants from NIA/NIH during the conduct of the study. Dr Mez reported grants from NIH during the conduct of the study. Dr Obisesan reported grants from NIH during the conduct of the study. Dr Bennett reported grants from NIH during the conduct of the study. Dr Hall reported grants from NIA during the conduct of the study. Dr Reiman reported grants from Banner Alzheimer’s Institute during the conduct of the study other support from Roche/Roche Diagnostics outside the submitted work. Dr Leverenz reported grants from NIA during the conduct of the study and grants from Alzheimer’s Drug Discovery Foundation, Avid Biopharmaceuticals, Biogen, GE Healthcare, Lewy Body Dementia Association, Michael J. Fox Foundation, National Institute of Neurologic Disorders, and Stroke, Sanofi; and consulting fees from Acadia, Aptinyx, Biogen, Eisai, Genzyme, Sanofi, and Takeda. Dr Levey reported personal fees from Karuna Pharmaceuticals and GENUV and grants from vTv Therapeutics, AbbVie, Biogen, Cognito, Esai, Genentech, and Novartis outside the submitted work. Dr Golde reported personal fees from Biogen, Eli Lilly and Company, and AbbVie; nonfinancial support from Lacerta Therapeutics outside the submitted work; and served on safety advisory boards for AbbVie, Promis Therapeutics, Biogen, and Eli Lilly and company. Dr Saykin reported grants from NIH grants during the conduct of the study and other support from Springer-Nature and grants from Eli Lilly and Company; multiprincipal investigator of NIA Small Business Innovation Research to Arkley BioTek, consultant to Bayer Oncology, and served on the advisory board of Neurovision outside the submitted work. Dr Albert reported grants from NIA during the conduct of the study and personal fees from Eli Lilly and Company outside the submitted work. Dr Hyman reported grants from NIH during the conduct of the study. Dr Petersen reported grants from NIH during the conduct of the study and personal fees from Hoffman-La Roche, Merck, Genentech, Biogen, GE Healthcare, and Eisai outside the submitted work. Dr Wisniewski reported grants from NIH during the conduct of the study and outside the submitted work. Dr Kaye reported grants from NIH during the conduct of the study. Dr Henderson reported grants from NIH during the conduct of the study. Dr DeCarli reported consulting for Novartis Pharmaceutical on a safety study in heart failure. Dr Brewer reported holds stock options in CorTechs Labs and Human Longevity and has served on advisory boards for Human Longevity and Eli Lilly and Company outside the submitted work. Dr Van Eldik reported grants from NIH during the conduct of the study. Dr Chui reported grants from NIA during the conduct of the study. Dr Rosenberg reported other support from Vitruvian during the conduct of the study; grants from NIH/NIA, Zale Foundation, AWARE, Triumph over Alzheimer’s Disease, and Its Their Time outside the submitted work; has a patent to amyloid beta gene vaccines issued; and served as a former Editor of JAMA Neurology, editorial board of The Journal of the Neurological Sciences, and former editorial board member of JAMA. Dr Grabowski reported grants from NIH during the conduct of the study. Dr Asthana reported grants from NIA during the conduct of the study and grants from Genentech and Lundbeck outside the submitted work. Dr Morris reported grants from NIH during the conduct of the study. Dr Strittmatter reported being a founder and equity holder in ReNetX Bio and in Allyx Therapeutics, entities seeking to develop therapeutics for Neural Repair and Neurodegeneration, respectively. Dr Goate served on the scientific advisory board for Denali Therapeutics from 2015-2018 and has served as a consultant for Biogen, AbbVie, Pfizer, GlaxoSmithKline, Eisai, and Illumina. Dr Kukull reported grants from NIH/NIA during the conduct of the study. Dr Foroud reported grants from NIH during the conduct of the study. Dr Haines reported grants from NIH during the conduct of the study. Dr Schellenberg reported grants from University of Pennsylvania during the conduct of the study. Dr Pericak-Vance reported grants from NIH/NIA during the conduct of the study. Dr Reitz reported grants from NIH during the conduct of the study. No other disclosures were reported.

    Funding/Support: This study was supported by the National Institutes of Health (NIH) (grants RF1 AG054023, U24 AG056270), Alzheimer’s Disease Genetics Consortium (grant U01 AG032984), National Institute on Aging (NIA) Genetics Initiative for Late-Onset Alzheimer’s Disease (grants U24 AG026395, U24 AG026390 [PI, Richard Mayeux, MD]), NIA Genetics of Alzheimer’s Disease Data Storage Site (grant U24 AG041689 [PI, Li-San Wang, PhD]), Washington Heights-Inwood Community Aging Project (grant R01 AG037212, R37 AG015473 [PI, Richard Mayeux, MD]), National Centralized Repository for Alzheimer’s Disease and Related Dementias (grant U24 AG021886 [PI, Tatiana Foroud, PhD]), Indianapolis AA (grants R01 AG009956, RC2 AG036650 [PI, Kathleen Hall, PhD]), ACT (grants U01 AG06781, U01 HG004610 [PI, Eric Larson, MD, MPH]), MIRAGE (grant R01 AG009029 [PI, Lindsay Farrer, PhD]), GenerAAtions (grant 5R01 AG20688 [PI, M. Daniele Fallin, PhD]), Pittsburg (grants P50 AG005133 [PI, O. Lopez], AG030653, AG041718, AG064877 [PI, M. Ilyas Kamboh, PhD]), Case Western Reserve University (grant R01 AG019085 [PI, Jonathan Haines, PhD]), CHAP (grants R01 AG11101, R01 AG030146, RC2 AG036650 [PI, Denis Evans, MD]), ROS/MAP (grants P30 AG10161, R01 AG15819, R01 AG30146, R01 AG17917, R01 AG15819 [PI, David Bennett, MD]), African-American AD Genetics Study (grant R01 AG028786 [PI, Jennifer Manly, PhD]), MARS/CORE (grants R01 AG22018, P30 AG10161 [PI, Lisa Barnes, PhD]), Mayo Clinic (grants P50 AG0016574, R01 032990, KL2 RR024151 [PIs, Ronald C. Petersen, MD, PhD, Nilufer Ertekin-Taner, MD, PhD, and Neill Graff-Radford, MD]), Miami (grants R01 AG027944, R01 AG028786 [PI, Margaret Pericak-Vance, PhD]), Wake Forest (PI, Goldie Byrd, PhD), MSSM (PI, Joseph Buxbaum, PhD), and MSSM (grants P50 AG05681, P01 AG03991, P01 AG026276 [PI, Alison Goate]). Dr Reitz was further supported by the NIH (grants RF1AG054080, U01AG052410, AG0087202). The NACC database is funded by NIA/NIH (grant U01 AG016976). National Alzheimer’s Coordinating Center data are contributed by the NIA-funded Alzheimer Disease Centers (grants P30 AG019610 [PI, Eric Reiman, MD], P30 AG013846 [PI, Neil Kowall, MD], P30 AG062428-01 [PI, James Leverenz, MD], P50 AG008702 [PI, Scott Small, MD], P50 AG025688 [PI, Allan Levey, MD, PhD], P50 AG047266 [PI, Todd Golde, MD, PhD], AG045058 [PI, Thomas Obisesan, MD, MPH], P30 AG010133 [PI, Andrew Saykin, PsyD], P50 AG005146 [PI, Marilyn Albert, PhD], P30 AG062421-01 [PI, Bradley Hyman, MD, PhD], P50 AG005138 [PI, Mary Sano, PhD], P30 AG008051 [PI, Thomas Wisniewski, MD], P30 AG013854 [PI, Robert Vassar, PhD], P30 AG008017 [PI, Jeffrey Kaye, MD], P30 AG010161 [PI, David Bennett, MD], P50 AG047366 [PI, Victor Henderson, MD, MS], P30 AG010129 [PI, Charles DeCarli, MD], P50 AG016573 [PI, Frank LaFerla, PhD], P30 AG062429-01 [PI, James Brewer, MD, PhD], P50 AG023501 [PI, Bruce Miller, MD], P30 AG035982 [PI, Russell Swerdlow, MD], P30 AG028383 [PI, Linda Van Eldik, PhD], P30 AG053760 [PI, Henry Paulson, MD, PhD], P30 AG010124 [PI, John Trojanowski, MD, PhD], P50 AG005133 [PI, Oscar Lopez, MD], P50 AG005142 [PI, Helena Chui, MD], P30 AG012300 [PI, Roger Rosenberg, MD], P30 AG049638 [PI, Suzanne Craft, PhD], P50 AG005136 [PI, Thomas Grabowski, MD], P30 AG062715-01 [PI, Sanjay Asthana, MD, FRCP], P50 AG005681 [PI, John C. Morris, MD], P50 AG047270 [PI, Stephen Strittmatter, MD, PhD]).

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

    Group Information: The Alzheimer’s Disease Genetics Consortium (ADGC) collaborators are listed in Supplement 2.

    References
    1.
    Seshadri  S, Fitzpatrick  AL, Ikram  MA,  et al; CHARGE Consortium; GERAD1 Consortium; EADI1 Consortium.  Genome-wide analysis of genetic loci associated with Alzheimer disease.   JAMA. 2010;303(18):1832-1840. doi:10.1001/jama.2010.574PubMedGoogle ScholarCrossref
    2.
    Naj  AC, Jun  G, Beecham  GW,  et al.  Common variants at MS4A4/MS4A6E, CD2AP, CD33 and EPHA1 are associated with late-onset Alzheimer’s disease.   Nat Genet. 2011;43(5):436-441. doi:10.1038/ng.801PubMedGoogle ScholarCrossref
    3.
    Harold  D, Abraham  R, Hollingworth  P,  et al.  Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer’s disease.   Nat Genet. 2009;41(10):1088-1093. doi:10.1038/ng.440PubMedGoogle ScholarCrossref
    4.
    Hollingworth  P, Harold  D, Sims  R,  et al; Alzheimer’s Disease Neuroimaging Initiative; CHARGE consortium; EADI1 consortium.  Common variants at ABCA7, MS4A6A/MS4A4E, EPHA1, CD33 and CD2AP are associated with Alzheimer’s disease.   Nat Genet. 2011;43(5):429-435. doi:10.1038/ng.803PubMedGoogle ScholarCrossref
    5.
    Jonsson  T, Stefansson  H, Steinberg  S,  et al.  Variant of TREM2 associated with the risk of Alzheimer’s disease.   N Engl J Med. 2013;368(2):107-116. doi:10.1056/NEJMoa1211103PubMedGoogle ScholarCrossref
    6.
    Lambert  JC, Heath  S, Even  G,  et al; European Alzheimer’s Disease Initiative Investigators.  Genome-wide association study identifies variants at CLU and CR1 associated with Alzheimer’s disease.   Nat Genet. 2009;41(10):1094-1099. doi:10.1038/ng.439PubMedGoogle ScholarCrossref
    7.
    Kunkle  BW, Grenier-Boley  B, Sims  R,  et al  Genetic meta-analysis of diagnosed Alzheimer's disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing.   Nat Genet. 2019;51(3):414-430. doi:10.1038/s41588-019-0358-2Google ScholarCrossref
    8.
    Sims  R, van der Lee  SJSJ, Naj  ACACAC,  et al; ARUK Consortium; GERAD/PERADES, CHARGE, ADGC, EADI.  Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer’s disease.   Nat Genet. 2017;49(9):1373-1384. doi:10.1038/ng.3916PubMedGoogle ScholarCrossref
    9.
    Ridge  PG, Mukherjee  S, Crane  PK, Kauwe  JS; Alzheimer’s Disease Genetics Consortium.  Alzheimer’s disease: analyzing the missing heritability.   PLoS One. 2013;8(11):e79771. doi:10.1371/journal.pone.0079771PubMedGoogle Scholar
    10.
    So  HC, Gui  AH, Cherny  SS, Sham  PC.  Evaluating the heritability explained by known susceptibility variants: a survey of ten complex diseases.   Genet Epidemiol. 2011;35(5):310-317. doi:10.1002/gepi.20579PubMedGoogle ScholarCrossref
    11.
    Gatz  M, Reynolds  CA, Fratiglioni  L,  et al.  Role of genes and environments for explaining Alzheimer disease.   Arch Gen Psychiatry. 2006;63(2):168-174. doi:10.1001/archpsyc.63.2.168PubMedGoogle ScholarCrossref
    12.
    Tang  M-X, Cross  P, Andrews  H,  et al.  Incidence of AD in African-Americans, Caribbean Hispanics, and Caucasians in northern Manhattan.   Neurology. 2001;56(1):49-56. doi:10.1212/WNL.56.1.49PubMedGoogle ScholarCrossref
    13.
    Hohman  TJ, Cooke-Bailey  JN, Reitz  C,  et al; Alzheimer Disease Genetics Consortium.  Global and local ancestry in African-Americans: implications for Alzheimer’s disease risk.   Alzheimers Dement. 2016;12(3):233-243. doi:10.1016/j.jalz.2015.02.012PubMedGoogle ScholarCrossref
    14.
    Reitz  C, Jun  G, Naj  A,  et al; Alzheimer Disease Genetics Consortium.  Variants in the ATP-binding cassette transporter (ABCA7), apolipoprotein E ϵ4,and the risk of late-onset Alzheimer disease in African Americans.   JAMA. 2013;309(14):1483-1492. doi:10.1001/jama.2013.2973PubMedGoogle ScholarCrossref
    15.
    Reitz  C, Mayeux  R; Alzheimer’s Disease Genetics Consortium.  TREM2 and neurodegenerative disease.   N Engl J Med. 2013;369(16):1564-1565. doi:10.1056/NEJMc1306509PubMedGoogle ScholarCrossref
    16.
    Rajabli  F, Feliciano  BE, Celis  K,  et al.  Ancestral origin of ApoE ε4 Alzheimer disease risk in Puerto Rican and African American populations.   PLoS Genet. 2018;14(12):e1007791. doi:10.1371/journal.pgen.1007791PubMedGoogle Scholar
    17.
    Jin  SC, Carrasquillo  MM, Benitez  BA,  et al.  TREM2 is associated with increased risk for Alzheimer’s disease in African Americans.   Mol Neurodegener. 2015;10(1):19. doi:10.1186/s13024-015-0016-9PubMedGoogle ScholarCrossref
    18.
    Cukier  HN, Kunkle  BW, Vardarajan  BN,  et al; Alzheimer’s Disease Genetics Consortium.  ABCA7 frameshift deletion associated with Alzheimer disease in African Americans.   Neurol Genet. 2016;2(3):e79. doi:10.1212/NXG.0000000000000079PubMedGoogle Scholar
    19.
    N’Songo  A, Carrasquillo  MM, Wang  X,  et al.  African American exome sequencing identifies potential risk variants at Alzheimer disease loci.   Neurol Genet. 2017;3(2):e141. doi:10.1212/NXG.0000000000000141PubMedGoogle Scholar
    20.
    Logue  MW, Schu  M, Vardarajan  BN,  et al; Alzheimer Disease Genetics Consortium; Alzheimer Disease Genetics Consortium.  Two rare AKAP9 variants are associated with Alzheimer’s disease in African Americans.   Alzheimers Dement. 2014;10(6):609-618.e11. doi:10.1016/j.jalz.2014.06.010PubMedGoogle ScholarCrossref
    21.
    Mez  J, Chung  J, Jun  G,  et al; Alzheimer’s Disease Genetics Consortium.  Two novel loci, COBL and SLC10A2, for Alzheimer’s disease in African Americans.   Alzheimers Dement. 2017;13(2):119-129. doi:10.1016/j.jalz.2016.09.002PubMedGoogle ScholarCrossref
    22.
    Santos  OA, Pedraza  O, Lucas  JA,  et al.  Ethnoracial differences in Alzheimer’s disease from the FLorida Autopsied Multi-Ethnic (FLAME) cohort.   Alzheimers Dement. 2019;15(5):635-643. doi:10.1016/j.jalz.2018.12.013PubMedGoogle ScholarCrossref
    23.
    Barnes  LL, Leurgans  S, Aggarwal  NT,  et al.  Mixed pathology is more likely in black than white decedents with Alzheimer dementia.   Neurology. 2015;85(6):528-534. doi:10.1212/WNL.0000000000001834PubMedGoogle ScholarCrossref
    24.
    Morris  JC, Schindler  SE, McCue  LM,  et al.  Assessment of racial disparities in biomarkers for Alzheimer disease.   JAMA Neurol. 2019;76(3):264-273. doi:10.1001/jamaneurol.2018.4249PubMedGoogle ScholarCrossref
    25.
    Filshtein  TJ, Dugger  BN, Jin  LW,  et al.  Neuropathological diagnoses of demented Hispanic, Black, and non-Hispanic white decedents seen at an Alzheimer’s disease center.   J Alzheimers Dis. 2019;68(1):145-158. doi:10.3233/JAD-180992PubMedGoogle ScholarCrossref
    26.
    Graff-Radford  NR, Besser  LM, Crook  JE, Kukull  WA, Dickson  DW.  Neuropathologic differences by race from the National Alzheimer’s Coordinating Center.   Alzheimers Dement. 2016;12(6):669-677. doi:10.1016/j.jalz.2016.03.004PubMedGoogle ScholarCrossref
    27.
    The African Partnership for Chronic Disease Research. Data. Accessed September 4, 2020. https://www.apcdr.org/data/
    28.
    Blacker  D, Bertram  L, Saunders  AJ,  et al; NIMH Genetics Initiative Alzheimer’s Disease Study Group.  Results of a high-resolution genome screen of 437 Alzheimer’s disease families.   Hum Mol Genet. 2003;12(1):23-32. doi:10.1093/hmg/ddg007PubMedGoogle ScholarCrossref
    29.
    Guerreiro  R, Wojtas  A, Bras  J,  et al; Alzheimer Genetic Analysis Group.  TREM2 variants in Alzheimer’s disease.   N Engl J Med. 2013;368(2):117-127. doi:10.1056/NEJMoa1211851PubMedGoogle ScholarCrossref
    30.
    NIAGADS. Explore genetics and genomics of Alzheimer’s Disease. Accessed September 4, 2020. https://www.niagads.org/
    31.
    Yu  L, Chibnik  LB, Srivastava  GP,  et al.  Association of Brain DNA methylation in SORL1, ABCA7, HLA-DRB5, SLC24A4, and BIN1 with pathological diagnosis of Alzheimer disease.   JAMA Neurol. 2015;72(1):15-24. doi:10.1001/jamaneurol.2014.3049PubMedGoogle ScholarCrossref
    32.
    de Leeuw  CA, Mooij  JM, Heskes  T, Posthuma  D.  MAGMA: generalized gene-set analysis of GWAS data.   PLoS Comput Biol. 2015;11(4):e1004219. doi:10.1371/journal.pcbi.1004219PubMedGoogle Scholar
    33.
    International Genomics of Alzheimer's Disease Consortium (IGAP); Jones  L, Lambert  J-C, Wang  L-S,  et al.  Convergent genetic and expression data implicate immunity in Alzheimer’s disease.   Alzheimers Dement. 2015;11(6). doi:10.1016/j.jalz.2014.05.1757Google Scholar
    34.
    Lambert  J-C, Ibrahim-Verbaas  CA, Harold  D,  et al; European Alzheimer’s Disease Initiative (EADI); Genetic and Environmental Risk in Alzheimer’s Disease; Alzheimer’s Disease Genetic Consortium; Cohorts for Heart and Aging Research in Genomic Epidemiology.  Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease.   Nat Genet. 2013;45(12):1452-1458. doi:10.1038/ng.2802PubMedGoogle ScholarCrossref
    35.
    Cummings  AC, Jiang  L, Velez Edwards  DR,  et al.  Genome-wide association and linkage study in the Amish detects a novel candidate late-onset Alzheimer disease gene.   Ann Hum Genet. 2012;76(5):342-351. doi:10.1111/j.1469-1809.2012.00721.xPubMedGoogle ScholarCrossref
    36.
    Lamriben  L, Oster  ME, Tamura  T,  et al.  EDEM1's mannosidase-like domain binds ERAD client proteins in a redox-sensitive manner and possesses catalytic activity.   J Biol Chem. 2018;293(36):13932-13945. doi:10.1074/jbc.RA118.004183PubMedGoogle ScholarCrossref
    37.
    Molinari  M, Calanca  V, Galli  C, Lucca  P, Paganetti  P.  Role of EDEM in the release of misfolded glycoproteins from the calnexin cycle.   Science. 2003;299(5611):1397-1400. doi:10.1126/science.1079474Google ScholarCrossref
    38.
    Cormier  JH, Tamura  T, Sunryd  JC, Hebert  DN.  EDEM1 recognition and delivery of misfolded proteins to the SEL1L-containing ERAD complex.   Mol Cell. 2009;34(5):627-633. doi:10.1016/j.molcel.2009.05.018PubMedGoogle ScholarCrossref
    39.
    Kaneko  M, Koike  H, Saito  R, Kitamura  Y, Okuma  Y, Nomura  Y.  Loss of HRD1-mediated protein degradation causes amyloid precursor protein accumulation and amyloid-beta generation.   J Neurosci. 2010;30(11):3924-3932. doi:10.1523/JNEUROSCI.2422-09.2010PubMedGoogle ScholarCrossref
    40.
    Kamboh  MI, Fan  KH, Yan  Q,  et al.  Population-based genome-wide association study of cognitive decline in older adults free of dementia: identification of a novel locus for the attention domain.   Neurobiol Aging. 2019;84:239.e15-239.e24. doi:10.1016/j.neurobiolaging.2019.02.024PubMedGoogle ScholarCrossref
    41.
    Allen  NJ, Bennett  ML, Foo  LC,  et al.  Astrocyte glypicans 4 and 6 promote formation of excitatory synapses via GluA1 AMPA receptors.   Nature. 2012;486(7403):410-414. doi:10.1038/nature11059PubMedGoogle ScholarCrossref
    42.
    Kang  TH, Kim  KT.  Negative regulation of ERK activity by VRK3-mediated activation of VHR phosphatase.   Nat Cell Biol. 2006;8(8):863-869. doi:10.1038/ncb1447PubMedGoogle ScholarCrossref
    43.
    Wu  GY, Deisseroth  K, Tsien  RW.  Spaced stimuli stabilize MAPK pathway activation and its effects on dendritic morphology.   Nat Neurosci. 2001;4(2):151-158. doi:10.1038/83976PubMedGoogle ScholarCrossref
    44.
    Thomas  GM, Huganir  RL.  MAPK cascade signalling and synaptic plasticity.   Nat Rev Neurosci. 2004;5(3):173-183. doi:10.1038/nrn1346PubMedGoogle ScholarCrossref
    45.
    Song  H, Kim  W, Kim  SH, Kim  KT.  VRK3-mediated nuclear localization of HSP70 prevents glutamate excitotoxicity-induced apoptosis and Aβ accumulation via enhancement of ERK phosphatase VHR activity.   Sci Rep. 2016;6:38452. doi:10.1038/srep38452PubMedGoogle ScholarCrossref
    46.
    van Horssen  J, Wesseling  P, van den Heuvel  LP, de Waal  RM, Verbeek  MM.  Heparan sulphate proteoglycans in Alzheimer’s disease and amyloid-related disorders.   Lancet Neurol. 2003;2(8):482-492. doi:10.1016/S1474-4422(03)00484-8PubMedGoogle ScholarCrossref
    47.
    van Horssen  J, Otte-Höller  I, David  G,  et al.  Heparan sulfate proteoglycan expression in cerebrovascular amyloid beta deposits in Alzheimer’s disease and hereditary cerebral hemorrhage with amyloidosis (Dutch) brains.   Acta Neuropathol. 2001;102(6):604-614. doi:10.1007/s004010100414PubMedGoogle ScholarCrossref
    48.
    van Horssen  J, Kleinnijenhuis  J, Maass  CN,  et al.  Accumulation of heparan sulfate proteoglycans in cerebellar senile plaques.   Neurobiol Aging. 2002;23(4):537-545. doi:10.1016/S0197-4580(02)00010-6PubMedGoogle ScholarCrossref
    49.
    Beckman  M, Holsinger  RM, Small  DH.  Heparin activates beta-secretase (BACE1) of Alzheimer’s disease and increases autocatalysis of the enzyme.   Biochemistry. 2006;45(21):6703-6714. doi:10.1021/bi052498tPubMedGoogle ScholarCrossref
    50.
    Leveugle  B, Ding  W, Durkin  JT,  et al.  Heparin promotes beta-secretase cleavage of the Alzheimer’s amyloid precursor protein.   Neurochem Int. 1997;30(6):543-548. doi:10.1016/S0197-0186(96)00103-9PubMedGoogle ScholarCrossref
    51.
    Castillo  GM, Ngo  C, Cummings  J, Wight  TN, Snow  AD.  Perlecan binds to the beta-amyloid proteins (A beta) of Alzheimer’s disease, accelerates A beta fibril formation, and maintains A beta fibril stability.   J Neurochem. 1997;69(6):2452-2465. doi:10.1046/j.1471-4159.1997.69062452.xPubMedGoogle ScholarCrossref
    52.
    Karczewski  KJ, Francioli  LC, Tiao  G,  et al.  Variation across 141,456 human exomes and genomes reveals the spectrum of loss-of-function intolerance across human protein-coding genes.  bioRxiv. Preprint posted online August 13, 2019. doi:10.1101/531210
    53.
    Tatsuoka  C, Tseng  H, Jaeger  J,  et al; Alzheimer’s Disease Neuroimaging Initiative.  Modeling the heterogeneity in risk of progression to Alzheimer’s disease across cognitive profiles in mild cognitive impairment.   Alzheimers Res Ther. 2013;5(2):14. doi:10.1186/alzrt168PubMedGoogle ScholarCrossref
    54.
    Zhang  H, Zhou  H, Lencz  T, Farrer  LA, Kranzler  HR, Gelernter  J.  Genome-wide association study of cognitive flexibility assessed by the Wisconsin Card Sorting Test.   Am J Med Genet B Neuropsychiatr Genet. 2018;177(5):511-519. doi:10.1002/ajmg.b.32642PubMedGoogle ScholarCrossref
    55.
    Holzenberger  M, Dupont  J, Ducos  B,  et al.  IGF-1 receptor regulates lifespan and resistance to oxidative stress in mice.   Nature. 2003;421(6919):182-187. doi:10.1038/nature01298PubMedGoogle ScholarCrossref
    56.
    Talbot  K, Wang  H-Y, Kazi  H,  et al.  Demonstrated brain insulin resistance in Alzheimer’s disease patients is associated with IGF-1 resistance, IRS-1 dysregulation, and cognitive decline.   J Clin Invest. 2012;122(4):1316-1338. doi:10.1172/JCI59903PubMedGoogle ScholarCrossref
    57.
    Freude  S, Hettich  MM, Schumann  C,  et al.  Neuronal IGF-1 resistance reduces A beta accumulation and protects against premature death in a model of Alzheimer’s disease.   FASEB J. 2009;23(10):3315-3324. doi:10.1096/fj.09-132043PubMedGoogle ScholarCrossref
    58.
    De Magalhaes Filho  CD, Kappeler  L, Dupont  J,  et al.  Deleting IGF-1 receptor from forebrain neurons confers neuroprotection during stroke and upregulates endocrine somatotropin.   J Cereb Blood Flow Metab. 2017;37(2):396-412. doi:10.1177/0271678X15626718PubMedGoogle ScholarCrossref
    59.
    Cohen  E, Paulsson  JF, Blinder  P,  et al.  Reduced IGF-1 signaling delays age-associated proteotoxicity in mice.   Cell. 2009;139(6):1157-1169. doi:10.1016/j.cell.2009.11.014PubMedGoogle ScholarCrossref
    60.
    Gontier  G, George  C, Chaker  Z, Holzenberger  M, Aïd  S.  Blocking IGF signaling in adult neurons alleviates Alzheimer’s Disease pathology through amyloid-β clearance.   J Neurosci. 2015;35(33):11500-11513. doi:10.1523/JNEUROSCI.0343-15.2015PubMedGoogle ScholarCrossref
    61.
    Garcia-Jove Navarro  M, Basset  C, Arcondéguy  T,  et al.  Api5 contributes to E2F1 control of the G1/S cell cycle phase transition.   PLoS One. 2013;8(8):e71443. doi:10.1371/journal.pone.0071443PubMedGoogle Scholar
    62.
    Auweter  SD, Fasan  R, Reymond  L,  et al.  Molecular basis of RNA recognition by the human alternative splicing factor Fox-1.   EMBO J. 2006;25(1):163-173. doi:10.1038/sj.emboj.7600918PubMedGoogle ScholarCrossref
    63.
    Hamada  N, Ito  H, Iwamoto  I, Morishita  R, Tabata  H, Nagata  K.  Role of the cytoplasmic isoform of RBFOX1/A2BP1 in establishing the architecture of the developing cerebral cortex.   Mol Autism. 2015;6:56. doi:10.1186/s13229-015-0049-5PubMedGoogle ScholarCrossref
    64.
    Gandal  MJ, Zhang  P, Hadjimichael  E,  et al.  Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder.   Science. 2018;362(6420):eaat8127. doi:10.1126/science.aat8127Google Scholar
    65.
    Alkallas  R, Fish  L, Goodarzi  H, Najafabadi  HS.  Inference of RNA decay rate from transcriptional profiling highlights the regulatory programs of Alzheimer’s disease.   Nat Commun. 2017;8(1):909. doi:10.1038/s41467-017-00867-zPubMedGoogle ScholarCrossref
    66.
    Lee  JA, Damianov  A, Lin  CH,  et al.  Cytoplasmic Rbfox1 regulates the expression of synaptic and autism-related genes.   Neuron. 2016;89(1):113-128. doi:10.1016/j.neuron.2015.11.025PubMedGoogle ScholarCrossref
    67.
    Alam  S, Suzuki  H, Tsukahara  T.  Alternative splicing regulation of APP exon 7 by RBFox proteins.   Neurochem Int. 2014;78:7-17. doi:10.1016/j.neuint.2014.08.001PubMedGoogle ScholarCrossref
    68.
    Raghavan  NS, Dumitrescu  L, Mormino  E,  et al; Alzheimer’s Disease Neuroimaging Initiative.  Association between common variants in RBFOX1, an RNA-binding protein, and brain amyloidosis in early and preclinical Alzheimer disease.   JAMA Neurol. Published online June 22, 2020. doi:10.1001/jamaneurol.2020.1760Google Scholar
    69.
    Forstner  AJ, Hecker  J, Hofmann  A,  et al.  Identification of shared risk loci and pathways for bipolar disorder and schizophrenia.   PLoS One. 2017;12(2):e0171595. doi:10.1371/journal.pone.0171595PubMedGoogle Scholar
    70.
    Chen  DT, Jiang  X, Akula  N,  et al; BiGS.  Genome-wide association study meta-analysis of European and Asian-ancestry samples identifies three novel loci associated with bipolar disorder.   Mol Psychiatry. 2013;18(2):195-205. doi:10.1038/mp.2011.157PubMedGoogle ScholarCrossref
    71.
    Ruderfer  DM, Fanous  AH, Ripke  S,  et al; Schizophrenia Working Group of the Psychiatric Genomics Consortium; Bipolar Disorder Working Group of the Psychiatric Genomics Consortium; Cross-Disorder Working Group of the Psychiatric Genomics Consortium.  Polygenic dissection of diagnosis and clinical dimensions of bipolar disorder and schizophrenia.   Mol Psychiatry. 2014;19(9):1017-1024. doi:10.1038/mp.2013.138PubMedGoogle ScholarCrossref
    72.
    Goes  FS, Hamshere  ML, Seifuddin  F,  et al; Bipolar Genome Study (BiGS).  Genome-wide association of mood-incongruent psychotic bipolar disorder.   Transl Psychiatry. 2012;2:e180. doi:10.1038/tp.2012.106PubMedGoogle Scholar
    73.
    Jiang  X, Detera-Wadleigh  SD, Akula  N,  et al.  Sodium valproate rescues expression of TRANK1 in iPSC-derived neural cells that carry a genetic variant associated with serious mental illness.   Mol Psychiatry. 2019;24(4):613-624. doi:10.1038/s41380-018-0207-1PubMedGoogle ScholarCrossref
    74.
    Laster  M, Shen  JI, Norris  KC.  Kidney disease among African Americans: a population perspective.   Am J Kidney Dis. 2018;72(5)(suppl 1):S3-S7. doi:10.1053/j.ajkd.2018.06.021PubMedGoogle ScholarCrossref
    75.
    McAdams-DeMarco  MA, Daubresse  M, Bae  S, Gross  AL, Carlson  MC, Segev  DL.  Dementia, Alzheimer’s disease, and mortality after hemodialysis initiation.   Clin J Am Soc Nephrol. 2018;13(9):1339-1347. doi:10.2215/CJN.10150917PubMedGoogle ScholarCrossref
    76.
    Pirici  D, Stanaszek  L, Garz  C,  et al.  Common impact of chronic kidney disease and brain microhemorrhages on cerebral Aβ pathology in SHRSP.   Brain Pathol. 2017;27(2):169-180. doi:10.1111/bpa.12384PubMedGoogle ScholarCrossref
    77.
    Beach  TG, Monsell  SE, Phillips  LE, Kukull  W.  Accuracy of the clinical diagnosis of Alzheimer disease at National Institute on Aging Alzheimer Disease Centers, 2005-2010.   J Neuropathol Exp Neurol. 2012;71(4):266-273. doi:10.1097/NEN.0b013e31824b211bPubMedGoogle ScholarCrossref
    78.
    Bennett  DA, Schneider  JA, Arvanitakis  Z, Wilson  RS.  Overview and findings from the religious orders study.   Curr Alzheimer Res. 2012;9(6):628-645. doi:10.2174/156720512801322573PubMedGoogle ScholarCrossref
    ×