458 results on '"Whitehead, Patrice"'
Search Results
2. An Alzheimer’s disease risk variant in TTC3 modifies the actin cytoskeleton organization and the PI3K-Akt signaling pathway in iPSC-derived forebrain neurons
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Cukier, Holly N., Duarte, Carolina L., Laverde-Paz, Mayra J., Simon, Shaina A., Van Booven, Derek J., Miyares, Amanda T., Whitehead, Patrice L., Hamilton-Nelson, Kara L., Adams, Larry D., Carney, Regina M., Cuccaro, Michael L., Vance, Jeffery M., Pericak-Vance, Margaret A., Griswold, Anthony J., and Dykxhoorn, Derek M.
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- 2023
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3. Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer's disease.
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Sims, Rebecca, van der Lee, Sven J, Naj, Adam C, Bellenguez, Céline, Badarinarayan, Nandini, Jakobsdottir, Johanna, Kunkle, Brian W, Boland, Anne, Raybould, Rachel, Bis, Joshua C, Martin, Eden R, Grenier-Boley, Benjamin, Heilmann-Heimbach, Stefanie, Chouraki, Vincent, Kuzma, Amanda B, Sleegers, Kristel, Vronskaya, Maria, Ruiz, Agustin, Graham, Robert R, Olaso, Robert, Hoffmann, Per, Grove, Megan L, Vardarajan, Badri N, Hiltunen, Mikko, Nöthen, Markus M, White, Charles C, Hamilton-Nelson, Kara L, Epelbaum, Jacques, Maier, Wolfgang, Choi, Seung-Hoan, Beecham, Gary W, Dulary, Cécile, Herms, Stefan, Smith, Albert V, Funk, Cory C, Derbois, Céline, Forstner, Andreas J, Ahmad, Shahzad, Li, Hongdong, Bacq, Delphine, Harold, Denise, Satizabal, Claudia L, Valladares, Otto, Squassina, Alessio, Thomas, Rhodri, Brody, Jennifer A, Qu, Liming, Sánchez-Juan, Pascual, Morgan, Taniesha, Wolters, Frank J, Zhao, Yi, Garcia, Florentino Sanchez, Denning, Nicola, Fornage, Myriam, Malamon, John, Naranjo, Maria Candida Deniz, Majounie, Elisa, Mosley, Thomas H, Dombroski, Beth, Wallon, David, Lupton, Michelle K, Dupuis, Josée, Whitehead, Patrice, Fratiglioni, Laura, Medway, Christopher, Jian, Xueqiu, Mukherjee, Shubhabrata, Keller, Lina, Brown, Kristelle, Lin, Honghuang, Cantwell, Laura B, Panza, Francesco, McGuinness, Bernadette, Moreno-Grau, Sonia, Burgess, Jeremy D, Solfrizzi, Vincenzo, Proitsi, Petra, Adams, Hieab H, Allen, Mariet, Seripa, Davide, Pastor, Pau, Cupples, L Adrienne, Price, Nathan D, Hannequin, Didier, Frank-García, Ana, Levy, Daniel, Chakrabarty, Paramita, Caffarra, Paolo, Giegling, Ina, Beiser, Alexa S, Giedraitis, Vilmantas, Hampel, Harald, Garcia, Melissa E, Wang, Xue, Lannfelt, Lars, Mecocci, Patrizia, Eiriksdottir, Gudny, Crane, Paul K, Pasquier, Florence, and Boccardi, Virginia
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ARUK Consortium ,GERAD/PERADES ,CHARGE ,ADGC ,EADI ,Microglia ,Humans ,Alzheimer Disease ,Genetic Predisposition to Disease ,Adaptor Proteins ,Signal Transducing ,Membrane Glycoproteins ,Receptors ,Immunologic ,Odds Ratio ,Case-Control Studies ,Gene Expression Profiling ,Amino Acid Sequence ,Sequence Homology ,Amino Acid ,Gene Frequency ,Genotype ,Linkage Disequilibrium ,Polymorphism ,Single Nucleotide ,Phospholipase C gamma ,Immunity ,Innate ,Protein Interaction Maps ,Exome ,Neurodegenerative ,Brain Disorders ,Neurosciences ,Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD) ,Dementia ,Alzheimer's Disease ,Aging ,Acquired Cognitive Impairment ,2.1 Biological and endogenous factors ,Developmental Biology ,Biological Sciences ,Medical and Health Sciences - Abstract
We identified rare coding variants associated with Alzheimer's disease in a three-stage case-control study of 85,133 subjects. In stage 1, we genotyped 34,174 samples using a whole-exome microarray. In stage 2, we tested associated variants (P < 1 × 10-4) in 35,962 independent samples using de novo genotyping and imputed genotypes. In stage 3, we used an additional 14,997 samples to test the most significant stage 2 associations (P < 5 × 10-8) using imputed genotypes. We observed three new genome-wide significant nonsynonymous variants associated with Alzheimer's disease: a protective variant in PLCG2 (rs72824905: p.Pro522Arg, P = 5.38 × 10-10, odds ratio (OR) = 0.68, minor allele frequency (MAF)cases = 0.0059, MAFcontrols = 0.0093), a risk variant in ABI3 (rs616338: p.Ser209Phe, P = 4.56 × 10-10, OR = 1.43, MAFcases = 0.011, MAFcontrols = 0.008), and a new genome-wide significant variant in TREM2 (rs143332484: p.Arg62His, P = 1.55 × 10-14, OR = 1.67, MAFcases = 0.0143, MAFcontrols = 0.0089), a known susceptibility gene for Alzheimer's disease. These protein-altering changes are in genes highly expressed in microglia and highlight an immune-related protein-protein interaction network enriched for previously identified risk genes in Alzheimer's disease. These genetic findings provide additional evidence that the microglia-mediated innate immune response contributes directly to the development of Alzheimer's disease.
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- 2017
4. Genome-wide association analysis and admixture mapping in a Puerto Rican cohort supports an Alzheimer disease risk locus on chromosome 12.
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Akgun, Bilcag, Feliciano-Astacio, Briseida E., Hamilton-Nelson, Kara L., Scott, Kyle, Rivero, Joe, Adams, Larry D., Sanchez, Jose J., Valladares, Glenies S., Tejada, Sergio, Bussies, Parker L., Silva-Vergara, Concepcion, Rodriguez, Vanessa C., Mena, Pedro R., Celis, Katrina, Whitehead, Patrice G., Prough, Michael, Kosanovic, Christina, Van Booven, Derek J., Schmidt, Michael A., and Acosta, Heriberto
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ALZHEIMER'S disease risk factors ,CHROMOSOME analysis ,RISK assessment ,GENOME-wide association studies ,RESEARCH funding ,GENOMICS ,DESCRIPTIVE statistics ,PATH analysis (Statistics) ,GENE mapping ,GENE expression ,LONGITUDINAL method ,GENETIC risk score ,STATISTICS ,PUERTO Ricans ,DATA analysis software ,SEQUENCE analysis - Abstract
Introduction: Hispanic/Latino populations are underrepresented in Alzheimer Disease (AD) genetic studies. Puerto Ricans (PR), a three-way admixed (European, African, and Amerindian) population is the second-largest Hispanic group in the continental US. We aimed to conduct a genome-wide association study (GWAS) and comprehensive analyses to identify novel AD susceptibility loci and characterize known AD genetic risk loci in the PR population. Materials and methods: Our study included Whole Genome Sequencing (WGS) and phenotype data from 648 PR individuals (345 AD, 303 cognitively unimpaired). We used a generalized linear-mixed model adjusting for sex, age, population substructure, and genetic relationship matrix. To infer local ancestry, we merged the dataset with the HGDP/1000G reference panel. Subsequently, we conducted univariate admixture mapping (AM) analysis. Results: We identified suggestive signals within the SLC38A1 and SCN8A genes on chromosome 12q13. This region overlaps with an area of linkage of AD in previous studies (12q13) in independent data sets further supporting. Univariate African AM analysis identified one suggestive ancestral block (p = 7.2x10
-6 ) located in the same region. The ancestry-aware approach showed that this region has both European and African ancestral backgrounds and both contributing to the risk in this region. We also replicated 11 different known AD loci -including APOE- identified in mostly European studies, which is likely due to the high European background of the PR population. Conclusion: PR GWAS and AM analysis identified a suggestive AD risk locus on chromosome 12, which includes the SLC38A1 and SCN8A genes. Our findings demonstrate the importance of designing GWAS and ancestry-aware approaches and including underrepresented populations in genetic studies of AD. [ABSTRACT FROM AUTHOR]- Published
- 2024
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5. Generalizability of Tau and Amyloid Plasma Biomarkers in Alzheimer's Disease Cohorts of Diverse Genetic Ancestries
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Griswold, Anthony J, primary, Rajabli, Farid, additional, Gu, Tianjie, additional, Arvizu, Jamie, additional, Golightly, Charles, additional, Whitehead, Patrice G, additional, Hamilton-Nelson, Kara L, additional, Adams, Larry D, additional, Sanchez, Jose Javier, additional, Mena, Pedro, additional, Starks, Takiyah D, additional, Illanes-Manrique, Maryenela, additional, Silva, Concepcion, additional, Bush, William, additional, Cuccaro, Michael L, additional, Vance, Jeffery M, additional, Cornejo-Olivas, Mario, additional, Feliciano, Briseida, additional, Byrd, Goldie, additional, Beecham, Gary, additional, Haines, Jonathan, additional, and Pericak-Vance, Margaret A, additional
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- 2024
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6. Novel Stop-gain SORL1 Mutation in a Peruvian Family with Alzheimer’s Disease of the PeADI Study (P6-9.017)
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Olivas, Mario Cornejo, primary, Griswold, Anthony J., additional, Saldarriaga-Mayo, Ana, additional, Mena, Pedro, additional, Rodriguez, Richard, additional, Adams, Larry, additional, Whitehead, Patrice, additional, Isasi, Rosario, additional, Illanes-Manrique, Maryenela, additional, Sarapura-Castro, Elison, additional, Rajabli, Farid, additional, McInerney, Katalina, additional, Milla-Neyra, Karina, additional, Enciso, Carla Manrique, additional, Beecham, Gary, additional, Castro-Suarez, Sheila, additional, St. George-Hyslop, Peter, additional, Araujo-Aliaga, Ismael, additional, Cuccaro, Michael L., additional, Vance, Jeffery, additional, and Pericak-Vance, Margaret, additional
- Published
- 2024
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7. ABCA7 frameshift deletion associated with Alzheimer disease in African Americans
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Cukier, Holly N, Kunkle, Brian W, Vardarajan, Badri N, Rolati, Sophie, Hamilton-Nelson, Kara L, Kohli, Martin A, Whitehead, Patrice L, Dombroski, Beth A, Van Booven, Derek, Lang, Rosalyn, Dykxhoorn, Derek M, Farrer, Lindsay A, Cuccaro, Michael L, Vance, Jeffery M, Gilbert, John R, Beecham, Gary W, Martin, Eden R, Carney, Regina M, Mayeux, Richard, Schellenberg, Gerard D, Byrd, Goldie S, Haines, Jonathan L, Pericak-Vance, Margaret A, Albert, Marilyn S, Albin, Roger L, Apostolova, Liana G, Arnold, Steven E, Asthana, Sanjay, Atwood, Craig S, Baldwin, Clinton T, Barmada, M Michael, Barnes, Lisa L, Barral, Sandra, Beach, Thomas G, Becker, James T, Beekly, Duane, Bennett, David A, Bigio, Eileen H, Bird, Thomas D, Blacker, Deborah, Boeve, Bradley F, Boxer, Adam, Burke, James R, Burns, Jeffrey M, Buxbaum, Joseph D, Cai, Guiqing, Cairns, Nigel J, Cantwell, Laura B, Cao, Chuanhai, Carlsson, Cynthia M, Carrasquillo, Minerva M, Carroll, Steven L, Chui, Helena C, Clark, David G, Cribbs, David H, Crocco, Elizabeth A, Cruchaga, Carlos, De Jager, Philip L, DeCarli, Charles, Demirci, F Yesim, Dick, Malcolm, Dickson, Dennis W, Duara, Ranjan, Ertekin-Taner, Nilufer, Evans, Denis A, Faber, Kelley M, Fallin, M Daniele, Fallon, Kenneth B, Fardo, David W, Farlow, Martin R, Ferris, Steven, Foroud, Tatiana M, Frosch, Matthew P, Galasko, Douglas R, Gearing, Marla, Geschwind, Daniel H, Ghetti, Bernardino, Go, Rodney CP, Goate, Alison M, Graff-Radford, Neill R, Green, Robert C, Griffith, Patrick, Growdon, John H, Hakonarson, Hakon, Hamilton, Ronald L, Haroutunian, Vahram, Harrell, Lindy E, Honig, Lawrence S, Huebinger, Ryan M, Hulette, Christine M, Hyman, Bradley T, Jicha, Gregory A, and Jin, Lee-Way
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Biological Sciences ,Genetics ,Human Genome ,Neurodegenerative ,Neurosciences ,Dementia ,Aging ,Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD) ,Acquired Cognitive Impairment ,Alzheimer's Disease ,Clinical Research ,Brain Disorders ,2.1 Biological and endogenous factors ,Aetiology ,Alzheimer's Disease Genetics Consortium ,Clinical sciences - Abstract
ObjectiveTo identify a causative variant(s) that may contribute to Alzheimer disease (AD) in African Americans (AA) in the ATP-binding cassette, subfamily A (ABC1), member 7 (ABCA7) gene, a known risk factor for late-onset AD.MethodsCustom capture sequencing was performed on ∼150 kb encompassing ABCA7 in 40 AA cases and 37 AA controls carrying the AA risk allele (rs115550680). Association testing was performed for an ABCA7 deletion identified in large AA data sets (discovery n = 1,068; replication n = 1,749) and whole exome sequencing of Caribbean Hispanic (CH) AD families.ResultsA 44-base pair deletion (rs142076058) was identified in all 77 risk genotype carriers, which shows that the deletion is in high linkage disequilibrium with the risk allele. The deletion was assessed in a large data set (531 cases and 527 controls) and, after adjustments for age, sex, and APOE status, was significantly associated with disease (p = 0.0002, odds ratio [OR] = 2.13 [95% confidence interval (CI): 1.42-3.20]). An independent data set replicated the association (447 cases and 880 controls, p = 0.0117, OR = 1.65 [95% CI: 1.12-2.44]), and joint analysis increased the significance (p = 1.414 × 10(-5), OR = 1.81 [95% CI: 1.38-2.37]). The deletion is common in AA cases (15.2%) and AA controls (9.74%), but in only 0.12% of our non-Hispanic white cohort. Whole exome sequencing of multiplex, CH families identified the deletion cosegregating with disease in a large sibship. The deleted allele produces a stable, detectable RNA strand and is predicted to result in a frameshift mutation (p.Arg578Alafs) that could interfere with protein function.ConclusionsThis common ABCA7 deletion could represent an ethnic-specific pathogenic alteration in AD.
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- 2016
8. Tau and Amyloid Plasma Biomarker Analysis in Alzheimer’s Disease Cohorts from Diverse Genetic Ancestries
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Griswold, Anthony J., primary, Rajabli, Farid, additional, Gu, Tianjie, additional, Garcia‐Serje, Catherine, additional, Arvizu, Jamie, additional, Golightly, Charles G., additional, Whitehead, Patrice, additional, Hamilton‐Nelson, Kara L., additional, Adams, Larry D., additional, Celis, Katrina, additional, Sanchez, Jose Javier, additional, Tejada, Sergio J., additional, Mena, Pedro R., additional, Starks, Takiyah D., additional, Cornejo‐Olivas, Mario, additional, Illanes‐Manrique, Maryenela Z, additional, Silva‐Vergara, Concepcion, additional, Cuccaro, Michael L., additional, Vance, Jeffery M., additional, Feliciano‐Astacio, Briseida E., additional, Byrd, Goldie S., additional, Beecham, Gary W., additional, Haines, Jonathan L., additional, and Pericak‐Vance, Margaret A., additional
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- 2023
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9. APOE genotype vs Local ancestry: what contributes the most to Alzheimer’s disease risk?
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Moura, Sofia, primary, Celis, Katrina, additional, Muniz, Maria, additional, Rajabli, Farid, additional, Rivero, Joe, additional, Hamilton‐Nelson, Kara L., additional, Whitehead, Patrice, additional, Gearing, Marla, additional, Bennett, David A. A, additional, Flanagan, Margaret E, additional, Weintraub, Sandra, additional, Geula, Changiz, additional, Scott, William K., additional, Schuck, Theresa, additional, Jin, Fulai, additional, Dykxhoorn, Derek M., additional, Pericak‐Vance, Margaret A., additional, Griswold, Anthony J., additional, Young, Juan I., additional, and Vance, Jeffery M., additional
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- 2023
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10. Assessing the functional effect of the Presenilin‐1 G206A variant on age of onset of Alzheimer Disease in the Puerto Rican population
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Celis, Katrina, primary, Rajabli, Farid, additional, Griswold, Anthony J., additional, Adams, Larry D., additional, Tejada, Sergio J., additional, Sanchez, Jose Javier, additional, Simon, Shaina A., additional, Hamilton‐Nelson, Kara L., additional, Gomez, Nicholas R, additional, Whitehead, Patrice, additional, Mena, Pedro R., additional, Arvizu, Jamie, additional, Golightly, Charles G., additional, Silva, Concepcion, additional, Acosta, Heriberto, additional, Cuccaro, Michael L., additional, Vance, Jeffery M., additional, Beecham, Gary W., additional, Feliciano‐Astacio, Briseida E., additional, Dykxhoorn, Derek M., additional, Young, Juan I., additional, and Pericak‐Vance, Margaret A., additional
- Published
- 2023
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11. Recruitment and Retention in the Puerto Rico Alzheimer’s Disease Initiative
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Feliciano‐Astacio, Briseida E., primary, Rajabli, Farid, additional, Prough, Michael B., additional, Hamilton‐Nelson, Kara L., additional, Adams, Larry D., additional, Mena, Pedro R., additional, Tejada, Sergio J., additional, Sanchez, Jose Javier, additional, Celis, Katrina, additional, Silva‐Vergara, Concepcion, additional, Whitehead, Patrice, additional, Acosta, Heriberto, additional, Feliciano, Nereida I, additional, Chinea, Angel, additional, McInerney, Katalina, additional, Vance, Jeffery M., additional, Cuccaro, Michael L., additional, Beecham, Gary W., additional, and Pericak‐Vance, Margaret A., additional
- Published
- 2023
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12. Genomic editing of APOE4/4 to APOE3/3 in iPSCs bearing African or European local ancestry surrounding APOE
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Oron, Oded, primary, DeRosa, Brooke A., additional, Rajabli, Farid, additional, Celis, Katrina, additional, Feliciano‐Astacio, Briseida E., additional, Silva‐Vergara, Concepcion, additional, Beecham, Gary W., additional, Adams, Larry D., additional, Tejada, Sergio J., additional, Mena, Pedro R., additional, Whitehead, Patrice, additional, Hamilton‐Nelson, Kara L., additional, Starks, Takiyah D., additional, Byrd, Goldie S., additional, Cuccaro, Michael L., additional, Young, Juan I., additional, Pericak‐Vance, Margaret A., additional, Vance, Jeffery M., additional, and Dykxhoorn, Derek M., additional
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- 2023
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13. Deciphering the Genomic Regulatory Architecture of iPSC Derived Oligodendrocytes from Diverse Ancestries for Alzheimer’s Disease Studies
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Ramirez, Aura M, primary, Shepherd, Jihanne J, additional, Coombs, Lauren, additional, Simon, Shaina A., additional, Moura, Sofia, additional, Rajabli, Farid, additional, DeRosa, Brooke A., additional, Muniz, Maria, additional, Whitehead, Patrice, additional, Adams, Larry D., additional, Mena, Pedro R., additional, Illanes‐Manrique, Maryenela Z, additional, Starks, Takiyah D., additional, Tejada, Sergio J., additional, Byrd, Goldie S., additional, Cornejo‐Olivas, Mario, additional, Feliciano‐Astacio, Briseida E., additional, Rissman, Robert A, additional, Wang, Liyong, additional, Xu, Wanying, additional, Jin, Fulai, additional, Griswold, Anthony J., additional, Young, Juan I., additional, Dykxhoorn, Derek M., additional, and Vance, Jeffery M., additional
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- 2023
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14. Characterization of an African ancestry‐specific protective allele of the APOE ε4 allele for Alzheimer’s disease risk
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Wang, Liyong, primary, Vasquez, Marina Lipkin, additional, Nuytemans, Karen, additional, Rajabli, Farid, additional, Whitehead, Patrice, additional, Gearing, Marla, additional, Bennett, David A. A, additional, Flanagan, Margaret E, additional, Weintraub, Sandra, additional, Geula, Changiz, additional, Scott, William K., additional, Schuck, Theresa, additional, Jin, Fulai, additional, Xu, Wanying, additional, Dykxhoorn, Derek M., additional, Pericak‐Vance, Margaret A., additional, Griswold, Anthony J., additional, Young, Juan I., additional, and Vance, Jeffery M., additional
- Published
- 2023
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15. The Alzheimer’s Disease Sequencing Project – Follow Up Study (ADSP‐FUS): APOE genotype status and demographic characteristics across datasets
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Mena, Pedro R., primary, Zaman, Andrew, additional, Faber, Kelley M., additional, Adams, Larry D., additional, Inciute, Jovita D., additional, Whitehead, Patrice, additional, Foroud, Tatiana M., additional, Reyes‐Dumeyer, Dolly, additional, Kuzma, Amanda B, additional, Nicaretta, Heather Issen, additional, Naj, Adam C., additional, Martin, Eden R., additional, Dalgard, Clifton L., additional, Schellenberg, Gerald D., additional, Wang, Li‐San, additional, Mayeux, Richard, additional, Vardarajan, Badri N, additional, Vance, Jeffery M., additional, Cuccaro, Michael L., additional, Kunkle, Brian W., additional, and Pericak‐Vance, Margaret A., additional
- Published
- 2023
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16. Evidence of novel fine-scale structural variation at autism spectrum disorder candidate loci
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Hedges, Dale J, Hamilton-Nelson, Kara L, Sacharow, Stephanie J, Nations, Laura, Beecham, Gary W, Kozhekbaeva, Zhanna M, Butler, Brittany L, Cukier, Holly N, Whitehead, Patrice L, Ma, Deqiong, Jaworski, James M, Nathanson, Lubov, Lee, Joycelyn M, Hauser, Stephen L, Oksenberg, Jorge R, Cuccaro, Michael L, Haines, Jonathan L, Gilbert, John R, and Pericak-Vance, Margaret A
- Abstract
Abstract Background Autism spectrum disorders (ASD) represent a group of neurodevelopmental disorders characterized by a core set of social-communicative and behavioral impairments. Gamma-aminobutyric acid (GABA) is the major inhibitory neurotransmitter in the brain, acting primarily via the GABA receptors (GABR). Multiple lines of evidence, including altered GABA and GABA receptor expression in autistic patients, indicate that the GABAergic system may be involved in the etiology of autism. Methods As copy number variations (CNVs), particularly rare and de novo CNVs, have now been implicated in ASD risk, we examined the GABA receptors and genes in related pathways for structural variation that may be associated with autism. We further extended our candidate gene set to include 19 genes and regions that had either been directly implicated in the autism literature or were directly related (via function or ancestry) to these primary candidates. For the high resolution CNV screen we employed custom-designed 244 k comparative genomic hybridization (CGH) arrays. Collectively, our probes spanned a total of 11 Mb of GABA-related and additional candidate regions with a density of approximately one probe every 200 nucleotides, allowing a theoretical resolution for detection of CNVs of approximately 1 kb or greater on average. One hundred and sixty-eight autism cases and 149 control individuals were screened for structural variants. Prioritized CNV events were confirmed using quantitative PCR, and confirmed loci were evaluated on an additional set of 170 cases and 170 control individuals that were not included in the original discovery set. Loci that remained interesting were subsequently screened via quantitative PCR on an additional set of 755 cases and 1,809 unaffected family members. Results Results include rare deletions in autistic individuals at JAKMIP1, NRXN1, Neuroligin4Y, OXTR, and ABAT. Common insertion/deletion polymorphisms were detected at several loci, including GABBR2 and NRXN3. Overall, statistically significant enrichment in affected vs. unaffected individuals was observed for NRXN1 deletions. Conclusions These results provide additional support for the role of rare structural variation in ASD.
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- 2012
17. Variants in chondroitin sulfate metabolism genes in thrombotic storm
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Nuytemans, Karen, Ortel, Thomas L., Gomez, Lissette, Hofmann, Natalia, Alves, Natalie, Dueker, Nicole, Beecham, Ashley, Whitehead, Patrice, Hahn Estabrooks, Susan, Kitchens, Craig S., Erkan, Doruk, Brandão, Leonardo R., James, Andra H., Kulkarni, Roshni, Manco-Johnson, Marilyn J., Pericak-Vance, Margaret A., and Vance, Jeffery M.
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- 2018
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18. Use of local genetic ancestry to assess TOMM40-523′ and risk for Alzheimer disease
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Bussies, Parker L., Rajabli, Farid, Griswold, Anthony, Dorfsman, Daniel A., Whitehead, Patrice, Adams, Larry D., Mena, Pedro R., Cuccaro, Michael, Haines, Jonathan L., Byrd, Goldie S., Beecham, Gary W., Pericak-Vance, Margaret A., Young, Juan I., and Vance, Jeffery M.
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- 2020
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19. Linkage of Alzheimer disease families with Puerto Rican ancestry identifies a chromosome 9 locus
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Rajabli, Farid, Feliciano-Astacio, Briseida E., Cukier, Holly N., Wang, Liyong, Griswold, Anthony J., Hamilton-Nelson, Kara L., Adams, Larry D., Rodriguez, Vanessa C., Mena, Pedro R., Tejada, Sergio, Celis, Katrina, Whitehead, Patrice L., Van Booven, Derek J., Hofmann, Natalia K., Bussies, Parker L., Prough, Michael, Chinea, Angel, Feliciano, Nereida I., Vardarajan, Badri N., Reitz, Christiane, Lee, Joseph H., Prince, Martin J., Jimenez, Ivonne Z., Mayeux, Richard P., Acosta, Heriberto, Dalgard, Clifton L., Haines, Jonathan L., Vance, Jeffery M., Cuccaro, Michael L., Beecham, Gary W., and Pericak-Vance, Margaret A.
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- 2021
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20. Dissecting the role of Amerindian genetic ancestry and the ApoE ε4 allele on Alzheimer disease in an admixed Peruvian population
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Marca-Ysabel, Maria Victoria, Rajabli, Farid, Cornejo-Olivas, Mario, Whitehead, Patrice G., Hofmann, Natalia K., Illanes Manrique, Maryenela Zaida, Veliz Otani, Diego Martin, Milla Neyra, Ana Karina, Castro Suarez, Sheila, Meza Vega, Maria, Adams, Larry D., Mena, Pedro R., Rosario, Isasi, Cuccaro, Michael L., Vance, Jeffery M., Beecham, Gary W., Custodio, Nilton, Montesinos, Rosa, Mazzetti Soler, Pilar E., and Pericak-Vance, Margaret A.
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- 2021
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21. Multi-ancestry genome-wide meta-analysis of 56,241 individuals identifiesLRRC4C, LHX5-AS1and nominates ancestry-specific lociPTPRK,GRB14, andKIAA0825as novel risk loci for Alzheimer’s disease: the Alzheimer’s Disease Genetics Consortium
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Rajabli, Farid, primary, Benchek, Penelope, additional, Tosto, Giuseppe, additional, Kushch, Nicholas, additional, Sha, Jin, additional, Bazemore, Katrina, additional, Zhu, Congcong, additional, Lee, Wan-Ping, additional, Haut, Jacob, additional, Hamilton-Nelson, Kara L., additional, Wheeler, Nicholas R., additional, Zhao, Yi, additional, Farrell, John J., additional, Grunin, Michelle A., additional, Leung, Yuk Yee, additional, Kuksa, Pavel P., additional, Li, Donghe, additional, Lucio da Fonseca, Eder, additional, Mez, Jesse B., additional, Palmer, Ellen L., additional, Pillai, Jagan, additional, Sherva, Richard M., additional, Song, Yeunjoo E., additional, Zhang, Xiaoling, additional, Iqbal, Taha, additional, Pathak, Omkar, additional, Valladares, Otto, additional, Kuzma, Amanda B., additional, Abner, Erin, additional, Adams, Perrie M., additional, Aguirre, Alyssa, additional, Albert, Marilyn S., additional, Albin, Roger L., additional, Allen, Mariet, additional, Alvarez, Lisa, additional, Apostolova, Liana G., additional, Arnold, Steven E., additional, Asthana, Sanjay, additional, Atwood, Craig S., additional, Ayres, Gayle, additional, Baldwin, Clinton T., additional, Barber, Robert C., additional, Barnes, Lisa L., additional, Barral, Sandra, additional, Beach, Thomas G., additional, Becker, James T., additional, Beecham, Gary W., additional, Beekly, Duane, additional, Benitez, Bruno A., additional, Bennett, David, additional, Bertelson, John, additional, Bird, Thomas D., additional, Blacker, Deborah, additional, Boeve, Bradley F., additional, Bowen, James D., additional, Boxer, Adam, additional, Brewer, James, additional, Burke, James R., additional, Burns, Jeffrey M., additional, Buxbaum, Joseph D., additional, Cairns, Nigel J., additional, Cantwell, Laura B., additional, Cao, Chuanhai, additional, Carlson, Christopher S., additional, Carlsson, Cynthia M., additional, Carney, Regina M., additional, Carrasquillo, Minerva M., additional, Chasse, Scott, additional, Chesselet, Marie-Francoise, additional, Chin, Nathaniel A., additional, Chui, Helena C., additional, Chung, Jaeyoon, additional, Craft, Suzanne, additional, Crane, Paul K., additional, Cribbs, David H., additional, Crocco, Elizabeth A., additional, Cruchaga, Carlos, additional, Cuccaro, Michael L., additional, Cullum, Munro, additional, Darby, Eveleen, additional, Davis, Barbara, additional, De Jager, Philip L., additional, DeCarli, Charles, additional, DeToledo, John, additional, Dick, Malcolm, additional, Dickson, Dennis W., additional, Dombroski, Beth A., additional, Doody, Rachelle S., additional, Duara, Ranjan, additional, Ertekin-Taner, NIlüfer, additional, Evans, Denis A., additional, Faber, Kelley M., additional, Fairchild, Thomas J., additional, Fallon, Kenneth B., additional, Fardo, David W., additional, Farlow, Martin R., additional, Fernandez-Hernandez, Victoria, additional, Ferris, Steven, additional, Foroud, Tatiana M., additional, Frosch, Matthew P., additional, Fulton-Howard, Brian, additional, Galasko, Douglas R., additional, Gamboa, Adriana, additional, Gearing, Marla, additional, Geschwind, Daniel H., additional, Ghetti, Bernardino, additional, Gilbert, John R., additional, Goate, Alison M., additional, Grabowski, Thomas J., additional, Graff-Radford, Neill R., additional, Green, Robert C., additional, Growdon, John H., additional, Hakonarson, Hakon, additional, Hall, James, additional, Hamilton, Ronald L., additional, Harari, Oscar, additional, Hardy, John, additional, Harrell, Lindy E., additional, Head, Elizabeth, additional, Henderson, Victor W., additional, Hernandez, Michelle, additional, Hohman, Timothy, additional, Honig, Lawrence S., additional, Huebinger, Ryan M., additional, Huentelman, Matthew J., additional, Hulette, Christine M., additional, Hyman, Bradley T., additional, Hynan, Linda S., additional, Ibanez, Laura, additional, Jarvik, Gail P., additional, Jayadev, Suman, additional, Jin, Lee-Way, additional, Johnson, Kim, additional, Johnson, Leigh, additional, Kamboh, M. Ilyas, additional, Karydas, Anna M., additional, Katz, Mindy J., additional, Kauwe, John S., additional, Kaye, Jeffrey A., additional, Keene, C. Dirk, additional, Khaleeq, Aisha, additional, Kim, Ronald, additional, Knebl, Janice, additional, Kowall, Neil W., additional, Kramer, Joel H., additional, Kukull, Walter A., additional, LaFerla, Frank M., additional, Lah, James J., additional, Larson, Eric B., additional, Lerner, Alan, additional, Leverenz, James B., additional, Levey, Allan I., additional, Lieberman, Andrew P., additional, Lipton, Richard B., additional, Logue, Mark, additional, Lopez, Oscar L., additional, Lunetta, Kathryn L., additional, Lyketsos, Constantine G., additional, Mains, Douglas, additional, Margaret, Flanagan E., additional, Marson, Daniel C., additional, Martin, Eden R R., additional, Martiniuk, Frank, additional, Mash, Deborah C., additional, Masliah, Eliezer, additional, Massman, Paul, additional, Masurkar, Arjun, additional, McCormick, Wayne C., additional, McCurry, Susan M., additional, McDavid, Andrew N., additional, McDonough, Stefan, additional, McKee, Ann C., additional, Mesulam, Marsel, additional, Miller, Bruce L., additional, Miller, Carol A., additional, Miller, Joshua W., additional, Montine, Thomas J., additional, Monuki, Edwin S., additional, Morris, John C., additional, Mukherjee, Shubhabrata, additional, Myers, Amanda J., additional, Nguyen, Trung, additional, O’Bryant, Sid, additional, Olichney, John M., additional, Ory, Marcia, additional, Palmer, Raymond, additional, Parisi, Joseph E., additional, Paulson, Henry L., additional, Pavlik, Valory, additional, Paydarfar, David, additional, Perez, Victoria, additional, Peskind, Elaine, additional, Petersen, Ronald C., additional, Pierce, Aimee, additional, Polk, Marsha, additional, Poon, Wayne W., additional, Potter, Huntington, additional, Qu, Liming, additional, Quiceno, Mary, additional, Quinn, Joseph F., additional, Raj, Ashok, additional, Raskind, Murray, additional, Reiman, Eric M., additional, Reisberg, Barry, additional, Reisch, Joan S., additional, Ringman, John M., additional, Roberson, Erik D., additional, Rodriguear, Monica, additional, Rogaeva, Ekaterina, additional, Rosen, Howard J., additional, Rosenberg, Roger N., additional, Royall, Donald R., additional, Sager, Mark A., additional, Sano, Mary, additional, Saykin, Andrew J., additional, Schneider, Julie A., additional, Schneider, Lon S., additional, Seeley, William W., additional, Slifer, Susan H., additional, Small, Scott, additional, Smith, Amanda G., additional, Smith, Janet P., additional, Sonnen, Joshua A., additional, Spina, Salvatore, additional, St George-Hyslop, Peter, additional, Stern, Robert A., additional, Stevens, Alan B., additional, Strittmatter, Stephen M., additional, Sultzer, David, additional, Swerdlow, Russell H., additional, Tanzi, Rudolph E., additional, Tilson, Jeffrey L., additional, Trojanowski, John Q., additional, Troncoso, Juan C., additional, Tsuang, Debby W., additional, Van Deerlin, Vivianna M., additional, van Eldik, Linda J., additional, Vance, Jeffery M., additional, Vardarajan, Badri N., additional, Vassar, Robert, additional, Vinters, Harry V., additional, Vonsattel, Jean-Paul, additional, Weintraub, Sandra, additional, Welsh-Bohmer, Kathleen A., additional, Whitehead, Patrice L., additional, Wijsman, Ellen M., additional, Wilhelmsen, Kirk C., additional, Williams, Benjamin, additional, Williamson, Jennifer, additional, Wilms, Henrik, additional, Wingo, Thomas S., additional, Wisniewski, Thomas, additional, Woltjer, Randall L., additional, Woon, Martin, additional, Wright, Clinton B., additional, Wu, Chuang-Kuo, additional, Younkin, Steven G., additional, Yu, Chang-En, additional, Yu, Lei, additional, Zhu, Xiongwei, additional, Kunkle, Brian W., additional, Bush, William S., additional, Wang, Li-San, additional, Farrer, Lindsay A., additional, Haines, Jonathan L., additional, Mayeux, Richard, additional, Pericak-Vance, Margaret A., additional, Schellenberg, Gerard D., additional, Jun, Gyungah R., additional, Reitz, Christiane, additional, and Naj, Adam C., additional
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- 2023
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22. An Alzheimer′s disease risk variant inTTC3modifies the actin cytoskeleton organization and the PI3K-Akt signaling pathway in iPSC-derived forebrain neurons
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Cukier, Holly N., primary, Duarte, Carolina L., additional, Laverde-Paz, Mayra J., additional, Simon, Shaina A., additional, Van Booven, Derek J., additional, Miyares, Amanda T., additional, Whitehead, Patrice L., additional, Hamilton-Nelson, Kara L., additional, Adams, Larry D., additional, Carney, Regina M., additional, Cuccaro, Michael L., additional, Vance, Jeffery M., additional, Pericak-Vance, Margaret A., additional, Griswold, Anthony J., additional, and Dykxhoorn, Derek M., additional
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- 2023
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23. Ancestry‐related differences in chromatin accessibility and gene expression of APOE ε4 are associated with Alzheimer's disease risk
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Celis, Katrina, primary, Moreno, Maria D. M. Muniz, additional, Rajabli, Farid, additional, Whitehead, Patrice, additional, Hamilton‐Nelson, Kara, additional, Dykxhoorn, Derek M., additional, Nuytemans, Karen, additional, Wang, Liyong, additional, Flanagan, Margaret, additional, Weintraub, Sandra, additional, Geula, Changiz, additional, Gearing, Marla, additional, Dalgard, Clifton L., additional, Jin, Fulai, additional, Bennett, David A., additional, Schuck, Theresa, additional, Pericak‐Vance, Margaret A., additional, Griswold, Anthony J., additional, Young, Juan I., additional, and Vance, Jeffery M., additional
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- 2023
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24. Alzheimer disease (AD) specific transcription, DNA methylation and splicing in twenty AD associated loci
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Humphries, Crystal, Kohli, Martin A., Whitehead, Patrice, Mash, Deborah C., Pericak-Vance, Margaret A., and Gilbert, John
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- 2015
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25. Genetic architecture of Alzheimer's disease in a West African Cohort: Insights from the READD ‐ ADSP.
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Akinyemi, Rufus O., Griswold, Anthony J., Coker, Motunrayo, Whitehead, Patrice L., Rajabli, Farid, Akinwande, Kazeem S., Diala, Samuel, Ogunronbi, Mayowa, Scott, Kyle, Obiako, Reginald, Adams, Larry D., Hamilton‐Nelson, Kara L., Wahab, Kolawole, Mena, Pedro R., Akpalu, Albert Kwaku, Kunkle, Brian W., Sarfo, Fred Stephen, Vance, Jeffery M., Okubadejo, Njideka U, and Baiyewu, Olusegun
- Abstract
Background: The "Recruitment and Retention for Alzheimer's Disease Diversity Genetic Cohorts in the ADSP (READD‐ADSP)" is developing a resource to expand ancestral diversity in Alzheimer disease (AD) studies to dissect the genetic architecture of AD across different populations. In addition to US sites, READD‐ADSP includes four US sites and nine countries in sub‐Saharan Africa through the Africa Dementia Consortium (AfDC). The overall goal of READD‐ADSP is to identify genetically driven targets in diverse groups including African Americans and Hispanic/Latinos in US, and Africans. In this preliminary analysis we investigated the ancestral genetic differences and the impact of known AD risk factors within West African cohorts. Method: Genome‐wide genotyping was performed on 91 AD cases and 97 cognitive unimpaired controls from Nigeria and Ghana. APOE alleles and ABCA7 deletion (rs142076058) were sequenced using Sanger. We calculated global ancestry (principal components) using the PC‐AiR approach that is robust to known and cryptic relatedness. We investigated known AD loci from non‐Hispanic White (NHW) and AA genome wide association studies. For association analysis, we employed a mixed‐model regression approach (SAIGE) where we controlled for age, gender, population substructure (first three principal components), and relatedness. Result: Principal component analysis identified a distinction between the Ghana and Nigerian cohorts along the first principal component (PC1). Among the genetic loci examined, several showed nominal significance. Notably, the most prominent marker was found in SORL1 (rs17125523; p = 2 × 10‐3). Additionally, we discovered an exonic nonsynonymous marker in the BIN1 gene (rs112318500), which is specific to African ancestry and showed a protective effect. APOE e4 allele showed a significant association with AD risk (OR = 2.5; CI:1.5‐4.2; pv = 0.001), while the e2 indicated a protective trend but did not reach statistical significance. No statistical difference in the frequency of ABCA7 deletion was observed between AD and CU individuals. Conclusion: Our findings highlight the presence of genetic variations between West African populations that warrant further investigation, potentially offering new insights into the genetic underpinnings of AD. Data collection is ongoing across the AfDC and updated data will be presented. [ABSTRACT FROM AUTHOR]
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- 2024
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26. Multi‐Ethnic Analysis of Expression Quantitative Trait Loci (eQTL) in Alzheimer's Disease: A Focus on Health Disparity Populations.
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Wheeler, Nicholas R., Mews, Makaela, Gu, Tianjie, Gomez, Lissette, Whitehead, Patrice L., Hamilton‐Nelson, Kara L., Sanchez, Jose Javier, Adams, Larry D., Mena, Pedro R., Starks, Takiyah D., Silva, Concepcion, Illanes‐Manrique, Maryenela, Cuccaro, Michael L., Vance, Jeffery M., Cornejo‐Olivas, Mario, Byrd, Goldie S., Feliciano‐Astacio, Briseida E., Haines, Jonathan L., Beecham, Gary, and Pericak‐Vance, Margaret A.
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Background: Despite its high heritability, the genetic mechanisms influencing Alzheimer's Disease (AD), particularly in health disparity populations like African Americans (AA) and Hispanics (HI), are not fully understood. The lack of ancestral diversity in genetic datasets, notably in eQTL studies that associate genetic variation with gene expression, exacerbates these disparities. Our study seeks to address this gap by comparing the AD interactions of racially and ethnically diverse expression Quantitative Trait Loci (eQTL) effects to investigate the genetic influence on AD in underrepresented populations. Method: We investigated the impact of AD status on multi‐ethnic eQTLs across 3 diverse ancestral cohorts: AA, HI, and Non‐Hispanic White (NHW). Genotype and gene expression data were collected from samples of each genetic ancestry (AA = 224, HI = 293, NHW = 235), all with known AD status, with a focus on generating whole blood RNAseq data. We modified the TensorQTL pipeline to incorporate an interaction term for AD status to identify eQTL effects potentially induced by disease. We examined the top eQTL association per gene to assess the impact of AD status across the three ancestral cohorts. Result: Our preliminary analysis reveals striking variations in the impact of AD status on gene expression across AA, HI, and NHW cohorts. Several genes in each ancestry show statistically significant (pv<10e‐8) interaction effects (G X AD) (AA = 11, HI = 5, NHW = 9), indicating that genetic variation within those genes influences gene expression in an AD‐status dependent manner. A number of genes displayed interesting interaction patterns, where the most significant eQTL‐AD interaction was identical in two of the three ancestries studied, suggesting a potential ancestrally‐shared connection to AD risk (AA+HI = 4, AA+NHW = 3, HI+NHW = 5, AA+HI+NHW = 0), though notably there were no genes with induced eQTLs across all three ancestries. Conclusion: Our study highlights the impact the AD disease process has on genetic variants that alter whole‐blood gene expression. We also highlight differences by population, prompting the development of ethnically diverse gene expression panels that pave the way for more accurate genetic studies in health disparity populations. Our findings also underscore the potential for discovering novel genetic mechanisms influencing AD risk, which could lead to more effective and inclusive therapeutic strategies. [ABSTRACT FROM AUTHOR]
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- 2024
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27. Whole blood DNA methylation profiles of Alzheimer's disease differ across ancestrally diverse populations.
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Gu, Tianjie, Gomez, Lissette, Mews, Makaela, Whitehead, Patrice L., Hamilton‐Nelson, Kara L., Sanchez, Jose Javier, Adams, Larry D., Mena, Pedro R., Starks, Takiyah D., Silva‐Vergara, Concepcion, Illanes‐Manrique, Maryenela Z., Cuccaro, Michael L., Vance, Jeffery M., Cornejo‐Olivas, Mario, Byrd, Goldie S., Feliciano‐Astacio, Briseida E., Haines, Jonathan L., Beecham, Gary, Pericak‐Vance, Margaret A, and Bush, William S.
- Abstract
Background: Prior studies have shown differences in the genetic etiology and clinical presentation of Alzheimer's Disease across populations. For example, for multiple genetic loci associated with AD, effect sizes can vary drastically between individuals of different ancestral backgrounds. Few investigations into differences in epigenetic features like DNA methylation have been conducted in AD, particularly in diverse individuals. These studies are critical to identify and further characterize mechanisms of disease allowing for development of therapeutic interventions. Method: As part of an ongoing study of the genetics and epigenetics of AD in diverse populations, we performed a methylome analysis of 626 individuals. DNA from whole blood was analyzed using the Illumina MethylationEPICv2.0. The cohort consisted of both AD and cognitively unimpaired (CU) individuals of European (68 AD, 67 CU), African (98 AD, 106 CU), or Hispanic (Puerto Rican (85 AD, 76 CU); Peruvian (41 AD, 41 CU); Cuban (22 AD, 22 CU)) backgrounds. We analyzed data using the SeSAMe R package for quality control and statistical analysis. We performed differential methylation analysis between AD and CU in the overall dataset and within each ancestral population using linear models with covariates sex, age of exam, batch effect, global ancestry and estimated immune cell type proportions. Result: 878,853 CpG sites were tested for differences between AD status. In a preliminary analysis of these data, we identified 563 CpG sites with nominally significant differences (p‐value ≤ 0.001) between AD and CU. Within each ancestral group, the number of differentially methylated sites differed: European – 442 sites, African – 217 sites, Hispanic – 475. Notably, however, the markers within the ancestral group did not overlap, implying that the AD disease process may be quite different across populations. Conclusion: While these results are preliminary, and expansion of the dataset may reveal convergence of methylation patterns across ancestral populations, our analyses suggest the possibility of ancestry specific whole blood DNA methylation patterns as signatures of AD pathogenesis. Ultimately, combining these methylation profiles with existing genomic and transcriptomic data may reveal distinct genes but similar underlying pathological processes contributing to AD across individuals of diverse ancestries. [ABSTRACT FROM AUTHOR]
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- 2024
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28. The Alzheimer's Disease Sequencing Project – Discovery, Discovery Extension and Follow Up Study (ADSP‐FUS): APOE genotype status and demographic characteristics across datasets.
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Mena, Pedro R., Zaman, Andrew F, Faber, Kelley M., Adams, Larry D., Inciute, Jovita D., Whitehead, Patrice L., Foroud, Tatiana M., Reyes‐Dumeyer, Dolly, Kuzma, Amanda B, Nicaretta, Heather Issen, Naj, Adam C., Martin, Eden R., Dalgard, Clifton L., Schellenberg, Gerard D., Wang, Li‐San, Mayeux, Richard, Vardarajan, Badri N., Vance, Jeffery M., Cuccaro, Michael L., and Kunkle, Brain
- Abstract
Background: The ADSP is a National Institute on Aging (NIA) initiative focused on identifying genetic risk and protective variants for Alzheimer Disease (AD). Initial phases (Discovery and Discovery Extension) were predominantly non‐Hispanic Whites of European Ancestry (NHW‐EA). The ADSP expanded the population diversity in the Follow Up Study (ADSP‐FUS), and the current phase, ADSP‐FUS 2.0: The Diverse Population Initiative, focusing on whole genome sequencing (WGS) of non‐European populations including Hispanic/Latino (HL), non‐Hispanic Black with African Ancestry (NHB‐AA) and Asian populations. Support for these efforts include newly funded initiatives such as The DAWN Project, focused on recruitment of African, African‐American and Hispanic American populations, and the Asian Cohort for Alzheimer's Disease (ACAD). Methods: ADSP cohorts consist of studies of AD, dementia, and age‐related conditions. Clinical classifications are assigned based on standard criteria and derived from clinical measures and history, as well as additional neuropathologic data. In addition to production of WGS, APOE genotyping is available for all ADSP samples. Results: The ADSP currently consists of 40 cohorts comprised of ∼36,300 individuals, with plans to sequence >110,000 individuals from diverse race/ethnicity. Genotyping, sequencing, and clinical adjudication has been performed on 36,361 participants (cases N = 12,133, median age = 72; cognitively‐unimpaired(CU) individuals N = 17,116, median age = 74; ADRD N = 7,112, median age = 71). Mean ages for cases and controls vary across cohorts, 57.0+5.6 to 86.5+4.2 cases and 63.3+7.8 to 90.0+0 controls. 61% participants female, distributed as follows: cases(60.3%), CU(63.7%), and ADRD(55.8%). APOE genotype proportions differ considerably across reported race/ethnicity, for example highest for APOE ε4/ε4 carriers observed in Non‐Hispanic whites participants (7.4%) and the lowest in Asians (1.7%) Conclusion: The results provide an overview of clinical features in ADSP cohorts. The growth of the ADSP‐FUS 2.0 is central to the ADSP and expanding the size and diversity of this genomic resource available via NIAGADS. WGS data will be integrated with ADSP programs focused on phenotype harmonization, association analyses, functional genomics, and machine learning. In concert with these programs, the ADSP‐FUS 2.0 will accelerate the identification and understanding of potential genetic risk and protective variants for AD across all populations with the target of developing new treatments that are globally effective. [ABSTRACT FROM AUTHOR]
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- 2024
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29. Uncovering the Role of an African‐specific ABCA7 Frameshift Deletion on Lipid Metabolism and Alzheimer's Disease.
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Nam, Younji, DeRosa, Brooke A., Golightly, Charles G., Simon, Shaina A., Arvizu, Jamie, Ramirez, Aura M, Whitehead, Patrice L., Adams, Larry D., Starks, Takiyah D., Cukier, Holly N., Cuccaro, Michael L., Haines, Jonathan L., Byrd, Goldie S., Beecham, Gary, Dykxhoorn, Derek M., Young, Juan I, Vance, Jeffery M., and Pericak‐Vance, Margaret A.
- Abstract
Background: We previously identified a 44‐base pair deletion in (ATP‐binding cassette sub‐family A member 7) (ABCA7) that is significantly associated with Alzheimer's disease (AD) in African Americans (AA), producing a frameshift mutation resulting in a truncated protein (p.Arg578Alafs). ABCA7 is a lipid transporter across cellular membranes. While we have shown the mutant mRNA is present in neurons, whether it is translated into a stable protein is not known due to the lack of antibodies capable of recognizing the N‐terminus of endogenous ABCA7. The abrupt ABCA7 translation due to the deletion may alter the lipid metabolism, which can be investigated using isogenic iPSC and differentiated models. Methods: We analyzed single‐cell RNAseq (scRNAseq) data from spheroid cultures of 12 individuals. We generated a recombinant version of the ABCA7 gene with and without the deletion and bearing an N‐terminal flag tag which were transfected into HEK cells. We utilized CRISPR‐based genome editing using induced pluripotent stem cell (iPSC) lines from three independent AA‐cognitively unimpaired individuals (WT) to introduce the AA‐specific ABCA7 homozygous or heterozygous deletions. These were differentiated using standard protocols in the HIHG iPSC core into monocultures of induced neuron‐like cells. C‐terminal antibodies were used to assess the presence of native ABCA7 in HEK cells. Results: Examination of HEK cells revealed no detectable native ABCA7. The truncated ABCA7‐tagged protein appeared stable and localized in the plasma membrane as seen for the wild‐type protein (Figure 1). ScRNAseq confirmed that expression of ABCA7 is highest in neurons, identifying them as the iPSC differentiated cell of choice. We successfully generated both homozygous and heterozygous deletion isogenic lines in the three WT iPSC lines. Differentiated neurons from these isogenic lines have a normal phenotype, allowing for functional assays and lipidomic studies. Conclusion: The truncated protein p.Arg578Alafs appears to be expressed and stable in HEK cells, and surprisingly located in the plasma membrane despite the absence of most transmembrane domains. Our three isogenic iPSC pairs will be a great resource for studying the pathogenic effects of the ABCA7 truncation in differentiated cells. [ABSTRACT FROM AUTHOR]
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- 2024
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30. Exploring Ancestry‐dependent Regulation of Alzheimer's disease‐associated genetic risk factors in iOligodendroglia.
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Ramirez, Aura M, Shepherd, Jihanne J, Coombs, Lauren, Simon, Shaina A., Moura, Sofia, Rajabli, Farid, DeRosa, Brooke A., Whitehead, Patrice L., Adams, Larry D., Starks, Takiyah D., Illanes‐Manrique, Maryenela Z., Tejada, Sergio J., Byrd, Goldie S., Cornejo‐Olivas, Mario, Feliciano‐Astacio, Briseida E., Wang, Liyong, Jin, Fulai, Dykxhoorn, Derek M., Griswold, Anthony J., and Young, Juan I
- Abstract
Background: Genome‐wide association studies (GWAS) in Alzheimer's disease (AD) are consistently discovering genetic variants linked to the risk of developing this neurodegenerative condition. However, the effect size of the shared associated loci varies across populations as well as each population can have unique associations. This phenomenon could be explained by ancestry‐dependent changes in the genomic regulatory architecture (GRA) influencing the expression of these genes, similar to the effect of different local ancestry on the risk of AD in APOE4 carriers. Thus, understanding of GRA in the context of AD is imperative but currently most GRA data available is predominantly European, limiting our ability to comprehensively interpret the variability associated with AD risk genes across populations. For this study we focused on oligodendroglia, a cell lineage that has been historically overlooked but that is emerging as key players in AD due to their involvement in various pathological processes, including neuroinflammation, oxidative stress, and synaptic dysfunction. Here, we report ancestry‐dependent differences in the GRA of iPSC derived oligodendroglia with African, Amerindian, or European global ancestry. Method: We obtained PBMCs from individuals with Alzheimer's disease (AD) or without cognitive impairment, each with over 85% global ancestry of a specific ancestral background. These cells were then transformed into induced pluripotent stem cells (iPSC) and subsequently differentiated into oligodendroglia‐containing 3D neural cultures. On the 76th day of differentiation, we harvested and lysed the cells to isolate nuclei for Multiomic profiling including Single Cell ATAC and Single Cell RNA‐seq, we analyzed the chromatin accessibility and transcriptomes to identify ancestry‐dependent changes genome‐wide and in AD GWAS hits. Result: We found several AD GWAS hits differentially expressed between ancestries in OPCs and in the more mature oligodendrocyte population (including APP and CLU) and some differentially accessible peaks associated to some of these genes (predominantly PRDM7). Nevertheless, OPCs showed more ancestry‐specific regulation than the more mature oligodendrocytes. Conclusion: Our findings offer ancestry‐specific understanding of oligodendroglia chromatin changes and gene regulation in the context of AD. These results present a comprehensive perspective on the genetic regulatory architecture of oligodendroglia and constitute a resource for gene identification studies in the African American and Hispanic populations. [ABSTRACT FROM AUTHOR]
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- 2024
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31. Enhancing Alzheimer Disease risk prediction using machine learning for Pathway Polygenic Risk Scores (p‐PRS).
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Rajabli, Farid, Aguiar‐Pulido, Vanessa, Scott, Kyle, Feliciano‐Astacio, Briseida E., Hamilton‐Nelson, Kara L., Adams, Larry D., Mena, Pedro R., Celis, Katrina, Sanchez, Jose Javier, Whitehead, Patrice L., Prough, Michael B., Acosta, Heriberto, McInerney, Katalina F, Vance, Jeffery M., Cuccaro, Michael L., Beecham, Gary W, and Pericak‐Vance, Margaret A
- Abstract
Background: Polygenic Risk Scores (PRS) are important in predicting disease risk and are usually rely on markers selected by thresholding p‐values from genome‐wide association studies (GWAS). In traditional approaches, one single model is built to calculate risk scores, employing effect size to determine additive risk. However, this traditional method overlooks potential interactions between genetic loci resulting in reduced prediction power. To overcome these limitations, we propose an interpretable machine learning approach based on pathway‐level PRS (p‐PRS). Our approach improves prediction accuracy of PRS and generates both pathway specific and overall PRS considering possible nonlinear interactions. This advancement opens new avenues in personalized medicine, enabling more accurate disease prediction and prevention strategies. Method: We used whole genome sequencing data from 652 individuals from the Puerto Rican Alzheimer's Disease Initiative (PRADI). First, we selected Alzheimer disease (AD)‐related pathways and genes based on those reported in the largest non‐Hispanic White AD GWAS to date (Bellinguez et al. 2021). Then, we applied the Clumping and Thresholding (C+T) PRS approach using Bellinguez et al. summary statistics within each pathway (±20Kb for each gene). Finally, we used random forest with pathway‐level PRS values as input to classify AD vs. control. We assessed the performance of the p‐PRS model based on area under the receiver operating characteristic curve (AUC) using a 70%/30% split for training/testing. The outcomes were compared to a traditional C+T PRS model (AD∼PRS+APOE). Result: p‐PRS improves the performance of the traditional PRS model by 4.2% (p = 5.54E‐32). Performance is centered around AUC = 0.689(SE = 0.0022) for p‐PRS and 0.647(SE = 0.0024) for traditional PRS. PRS by pathway enables determining individual‐level important pathways for accurate classification. Top pathways obtained are related to processes implicated in cognitive deficit, neuroinflammation, neurodegeneration and vesicle mediation. Conclusion: Utilizing the innovative p‐PRS approach has improved the estimation of AD risk in the PR cohort. The application of interpretable machine learning approaches allows identifying the most relevant pathways for effective risk prediction and classification. Importantly, improved precision will provide more effective actionable risk mitigation strategies, optimize the selection process for clinical trials, and contribute to the development of more personalized treatment interventions. [ABSTRACT FROM AUTHOR]
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- 2024
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32. Local ancestry in Apoe3: Friend or Foe?
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Moura, Sofia, Celis, Katrina, Nasciben, Luciana Bertholim, Rajabli, Farid, Rivero, Joe, Hamilton‐Nelson, Kara L., Whitehead, Patrice L., Gearing, Marla, Bennett, David A., Flanagan, Margaret E, Weintraub, Sandra, Geula, Changiz, Scott, William K., Davis, David A., Vontell, Regina T, Schuck, Theresa, Dykxhoorn, Derek M., Pericak‐Vance, Margaret A., Griswold, Anthony J., and Young, Juan I
- Abstract
Background: Non‐Hispanic White APOE4 carriers have a higher risk of developing AD compared to African American APOE4 carriers. The local ancestry (LA) surrounding the APOE region was previously shown to be the primary factor in this risk difference. APOE4 carriers of European LA (ELA) have been found to have higher APOE4 expression and chromatin accessibility compared to African LA (ALA). We sought to investigate whether the LA around APOE3 has the same effect between ancestries. Differences between alleles could provide insight into ancestry‐specific regulatory areas controlling the APOE4 expression. Method: We identified AD autopsy samples by GSA, all homozygous for APOE3 and LA from 4 ADRC brain banks: Miami, Emory, Duke, and Rush. We performed single nuclei RNA sequencing (snRNA‐seq) on eight frozen frontal cortex (B9 area) samples using 10x Genomics. Result: We performed snRNA‐seq in a total of 51,462 nuclei from eight brains (4 ELA and 4 ALA). We identified 35 distinct cell clusters at a resolution of 0.6. The proportion of cells per cluster between ELA and ALA samples was similar for all clusters, except for cluster 32 (Excitatory Neurons) which had a greater than 2‐fold difference in ALA. Our data shows that APOE3 carriers with ELA have a significantly higher APOE expression in Excitatory Neurons (cluster 7) than those of ALA and contrary to our previous observations in APOE4 carriers (Griswold, A. et al, (2021)), APOE3 carriers of ALA express higher APOE in astrocytes (cluster 2) and Microglia (cluster 6). However, overall, comparison of APOE3 vs APOE4 carriers in this study demonstrated that APOE3 alleles have significantly lower gene expression than APOE4 carriers of the same LA. Conclusion: Our preliminary data suggest that the LA surrounding APOE3 is associated with different effects on APOE expression compared to APOE4 allele. Further, within the same LA, we observed that, overall, the different cell types express less APOE in APOE3 carriers compared to APOE4 carriers. Altogether, this may provide additional insight into the regulatory mechanisms affecting APOE4 expression. [ABSTRACT FROM AUTHOR]
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- 2024
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33. Genome‐wide association analysis and admixture mapping suggest an Alzheimer disease risk locus on chromosome 12 in a Puerto Rican cohort.
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Akgun, Bilcag, Feliciano‐Astacio, Briseida E., Rivero, Joe, Hamilton‐Nelson, Kara L., Scott, Kyle, Celis, Katrina, Adams, Larry D., Sanchez, Jose Javier, Valladares, Glenies S, Silva‐Vergara, Concepcion, Rodriguez, Vanessa C, Mena, Pedro R., Whitehead, Patrice L., Prough, Michael B., Acosta, Heriberto, Griswold, Anthony J., Dalgard, Clifton L., McInerney, Katalina F, Vance, Jeffery M., and Cuccaro, Michael L.
- Abstract
Background: Hispanic/Latino populations are underrepresented in Alzheimer Disease (AD) genetic studies. The Puerto Rican (PR) population, a three‐way admixed (European, African, and Amerindian) population is the second‐largest Hispanic group in the continental US. We performed a genome‐wide association study (GWAS) in the PR population to identify novel AD susceptibility loci and characterize known AD genetic risk loci. Method: 652 individuals (349 AD; 303 cognitively unimpaired), ascertained through the Puerto Rico Alzheimer Disease Initiative (PRADI), were included in the analyses. We performed GWAS on the Whole Genome Sequencing (WGS) dataset with a generalized linear‐mixed model adjusting for sex, age, and population substructure as fixed effects and genetic relationship matrix as a random effect. To infer local ancestry, we merged the target PR dataset with appropriate population samples from the HGDP and 1000G reference panels. Subsequently, we conducted univariate admixture mapping (AM) analysis. We also assessed the polygenic risk score (PRS) using the effect sizes from summary statistics from the non‐Hispanic White (NHW) study. Result: We identified a suggestive significant (p<5 × 10‐6) signal (rs11183403; P = 4 × 10‐6) within the SLC38A1 gene on chromosome 12. Univariate African AM analysis identified one suggestive (p<4 × 10‐5) ancestral block located in the same region on chromosomes 12q13.1 (p = 7.2 × 10‐6). We replicated eight known AD loci– APOE, ABCA7, CLU, FERMT2, GRN, PRDM7, SEC61G, and TREM2. Admixture analysis revealed proportions of 68% European, 20% African, and 12% Amerindian in the PR cohort. NHW‐derived PRS has a good prediction power (AUC = 0.62) in the PR dataset. Conclusion: PR GWAS and AM identified a suggestive AD risk locus in the SLC38A1 gene. This region overlaps with an area of linkage of AD in previous studies (12q13). The SLC38A1 gene is associated with ischemic brain damage and its transcription is affected by amyloid‐beta peptide. Our study replicated 8 known AD loci previously identified in European studies and showed good predictive power with NHW‐derived PRS which is likely due to the high European background of the PR population. Including underrepresented populations in genetic studies is important for identifying health disparities and developing effective treatments for AD in all groups. [ABSTRACT FROM AUTHOR]
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- 2024
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34. A novel stop‐gain SORL1 mutation from Amerindian background in a Peruvian family with Alzheimer's Disease of the PeADI Study.
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Cornejo‐Olivas, Mario, Griswold, Anthony J., Saldarriaga‐Mayo, Ana, Mena, Pedro R., Rodriguez, Richard S., Adams, Larry D., Whitehead, Patrice L., Isasi, Rosario, Illanes‐Manrique, Maryenela, Sarapura‐Castro, Elison, Rajabli, Farid, McInerney, Katalina F, Milla‐Neyra, Karina, Manrique‐Enciso, Carla, Beecham, Gary W, Castro‐Suarez, Sheila, George‐Hyslop, Peter St, Araujo‐Aliaga, Ismael, Cuccaro, Michael L., and Vance, Jeffery M.
- Abstract
Background: Common and rare variants in SORL1 have been associated with increased risk of Alzheimer's disease (AD). Since 2019, we have run an international collaborative research initiative to ascertain a Peruvian cohort for Alzheimer's disease and other related dementias for genetic studies (PeADI). Method: A Peruvian family (4 AD cases and two mild cognitive impairment (MCI) cases) was recruited through the PeADI study. All six family‐member completed a full cognitive assessment, had plasma‐based biomarkers pTau181 and Aβ42/40 measured via SIMOA chemistry on the Quanterix HD‐X, and underwent whole genome sequencing. Variants within AD risk genes as determined by the ADSP Gene Verification Committee were prioritized and variant interpretation was performed according to ACMG recommendations. Result: We identified a SORL1 c.5019G>A (p.Trp1673Ter) variant of Amerindian background in the four AD diagnosed siblings within this family. The two MCI cases did not carry the novel variant. The identified SORL1 variant corresponded to a heterozygous stop‐gain variant in exon 36 replacing tryptophan by a stop codon at position 1673 of the SORL1 protein. In‐silico analysis predicts this variant promotes nonsense‐mediated mRNA decay. This variant has not been previously reported in databases including gnomAD, LOVD and ClinVar. The 4 AD cases had on average 2.3X higher plasma pTau181 concentrations compared to the 2 MCI (2.03 ± 0.28pg/µl vs 0.88 ± 0.7pg/µl). There was no noticeable difference in the Aβ42/40 ratio. This variant is classified as likely pathogenic according to ACMG. Conclusion: We report the first Peruvian AD family carrying a likely pathogenic stop‐gain SORL1 variant within an Amerindian background region. Further cosegregation and functional assays are required to establish the risk size of this variant for AD. [ABSTRACT FROM AUTHOR]
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- 2024
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35. Ancestry‐specific studies on Alzheimer's disease using iPSC‐derived microglia.
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Moura, Sofia, Nasciben, Luciana Bertholim, Shepherd, Jihanne J, Coombs, Lauren, Ramirez, Aura M, Simon, Shaina A., Van Booven, Derek, Rivero, Joe, DeRosa, Brooke A., Whitehead, Patrice L., Adams, Larry D., Starks, Takiyah D., Mena, Pedro R., Illanes‐Manrique, Maryenela Z., Tejada, Sergio J., Byrd, Goldie S., Cornejo‐Olivas, Mario, Feliciano‐Astacio, Briseida E., Rajabli, Farid, and Wang, Liyong
- Abstract
Background: In the US, African Americans (admixed with African and European) followed by Hispanics (admixed with Amerindian, African, and European) are the most affected groups compared to non‐Hispanic Whites (NHW). While genetic diversity and admixture play crucial roles in disease risk, the ancestry‐specific mechanisms remain poorly understood with most AD‐related studies focusing on NHW. Despite the recent field efforts to include genetically admixed populations, there continues to be a lack of functional studies in AD across the different cell types in these populations. Given the importance of Microglia in AD, we here characterize the genetic regulatory architecture (GRA) on iPSC‐derived Microglia (MGL) in African and Amerindian genomes. Method: iPSC lines derived from controls and AD patients with >90% genomic content from different ancestries (Amerindian, African, and European) were differentiated into MGL. We performed bulk RNA‐seq and ATAC‐seq, followed by differential expression and accessibility analyses to study the GRA of these admixed populations and its contributions to AD. Result: We identified 1,103 differentially expressed genes (DEGs) and 267 differentially accessible genes (DAGs) across ancestries. We observed the most differences on both chromatin accessibility and gene expression levels between AI and AF. On the chromatin level and in the context of AD, we observed 2 DAGs (PRDM7 and SCIMP) between AI and AF, and 1 DAG between AI and EU (PRDM7). In addition, we identified 10 AD‐risk modifying genes that are differentially expressed between AI and AF ancestries (ABI3, CTSB, JAZF1, MS4A6A, PILRA, PLEKHA1, RASGEF1C, SORL1, TREM2, and TREML2) and 3 DEGs between AI and EU (JAZF1, MS4A6A, and SORL1). We identified several DEGs to be involved in lipid metabolism, cholesterol biosynthesis and metabolism, lysosomal activity, and immune response ‐ all highly relevant processes in AD pathology. Conclusion: We provide new insights into ancestry‐specific genetic risk factors in AD pathophysiology. Here, we report novel transcriptomic and chromatin accessibility data in microglia of AI and AF ancestries that potentially contribute to a differential genetic risk in AD in the different ancestries. Interestingly, those ancestries with greatest migratory differences revealed the largest DEG. [ABSTRACT FROM AUTHOR]
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- 2024
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36. Exploring effect of known Alzheimer disease genetic loci in the Peruvian population
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Cornejo‐Olivas, Mario, primary, Rajabli, Farid, additional, Kushch, Nicholas A., additional, Mena, Pedro Ramon, additional, Illanes‐Manrique, Maryenela, additional, Adams, Larry D., additional, Whitehead, Patrice L., additional, Hamilton‐Nelson, Kara L., additional, Milla‐Neyra, Karina, additional, Marca, Victoria, additional, Sarapura‐Castro, Elison, additional, Manrique‐Enciso, Carla, additional, Mejía, Koni K., additional, Isasi, Rosario, additional, Castro‐Suarez, Sheila, additional, Araujo‐Aliaga, Ismael, additional, Custodio, Nilton, additional, Montesinos, Rosa, additional, Griswold, Anthony J., additional, Dalgard, Clifton L., additional, Beecham, Gary W., additional, Cuccaro, Michael L., additional, Vance, Jeffery M., additional, and Pericak‐Vance, Margaret A., additional
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- 2022
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37. Admixed ancestral composition with Amerindian predominance at the Peruvian Alzheimer Disease Initiative (PeADI)
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Cubas‐Montecino, Diana, primary, Rajabli, Farid, additional, Illanes‐Manrique, Maryenela, additional, Kushch, Nicholas A., additional, Mena, Pedro Ramon, additional, Whitehead, Patrice L., additional, Hamilton‐Nelson, Kara L., additional, Milla‐Neyra, Karina, additional, Marca, Victoria, additional, Sarapura‐Castro, Elison, additional, Manrique‐Enciso, Carla, additional, Mejía, Koni K., additional, Isasi, Rosario, additional, Castro‐Suarez, Sheila, additional, Araujo‐Aliaga, Ismael, additional, Custodio, Nilton, additional, Montesinos, Rosa, additional, Griswold, Anthony J., additional, Dalgard, Clifton L., additional, Beecham, Gary W., additional, Cuccaro, Michael L., additional, Vance, Jeffery M., additional, Cornejo‐Olivas, Mario, additional, and Pericak‐Vance, Margaret A., additional
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- 2022
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38. Transcriptomic Analysis of Whole Blood in Admixed Latinx Alzheimer Disease Cohorts
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Griswold, Anthony J., primary, Gu, Tianjie, additional, Van Booven, Derek, additional, Whitehead, Patrice L., additional, Hamilton‐Nelson, Kara L., additional, Contreras, Maricarmen, additional, Sanchez, Jose Javier, additional, Tejada, Sergio, additional, Adams, Larry D., additional, Mena, Pedro Ramon, additional, Bush, William S., additional, Silva‐Vergara, Concepcion, additional, Cornejo‐Olivas, Mario, additional, Illanes‐Manrique, Maryenela, additional, Cuccaro, Michael L., additional, Vance, Jeffery M., additional, Feliciano‐Astacio, Briseida E., additional, Beecham, Gary W., additional, and Pericak‐Vance, Margaret A., additional
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- 2022
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39. Ancestral Analysis of the Presenilin‐1 G206A Variant Reveals it as a Founder Event on an African Haplotype in the Puerto Rican Population
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Hamilton‐Nelson, Kara L., primary, Griswold, Anthony J., additional, Rajabli, Farid, additional, Whitehead, Patrice L., additional, Contreras, Maricarmen, additional, Tejada, Sergio, additional, Sanchez, Jose Javier, additional, Mena, Pedro Ramon, additional, Adams, Larry D., additional, Starks, Takiyah D., additional, Silva‐Vergara, Concepcion, additional, Cuccaro, Michael L., additional, Vance, Jeffery M., additional, Byrd, Goldie S., additional, Haines, Jonathan L., additional, Beecham, Gary W., additional, Feliciano‐Astacio, Briseida E., additional, Pericak‐Vance, Margaret A., additional, and Celis, Katrina, additional
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- 2022
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40. Fine‐mapping of chromosome 9p21 linkage in Puerto Rican Alzheimer disease families.
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Celis, Katrina, primary, Rajabli, Farid, additional, Simon, Shaina A, additional, Wang, Liyong, additional, Hamilton‐Nelson, Kara L., additional, Adams, Larry D., additional, Mena, Pedro Ramon, additional, Whitehead, Patrice L., additional, Van Booven, Derek, additional, Feliciano‐Astacio, Briseida E., additional, Chinea, Angel, additional, Feliciano, Nereida I, additional, Acosta, Heriberto, additional, Dalgard, Clifton L., additional, Haines, Jonathan L., additional, Vance, Jeffery M., additional, Cuccaro, Michael L., additional, Beecham, Gary W., additional, Dykxhoorn, Derek M., additional, Griswold, Anthony J., additional, and Pericak‐Vance, Margaret A., additional
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- 2022
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41. Analysis of Alzheimer Disease Plasma Biomarker pTau‐181 in Individuals of Diverse Admixed Ancestral Backgrounds
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Grimmer, Timo, primary, Rajabli, Farid, additional, Garcia‐Serje, Catherine, additional, Arvizu, Jamie, additional, Larkin‐Gero, Emma, additional, Whitehead, Patrice L., additional, Hamilton‐Nelson, Kara L., additional, Adams, Larry D., additional, Contreras, Maricarmen, additional, Sanchez, Jose Javier, additional, Tejada, Sergio, additional, Mena, Pedro Ramon, additional, Starks, Takiyah D., additional, Cornejo‐Olivas, Mario, additional, Illanes‐Manrique, Maryenela, additional, Silva‐Vergara, Concepcion, additional, Cuccaro, Michael L., additional, Vance, Jeffery M., additional, Feliciano‐Astacio, Briseida E., additional, Byrd, Goldie S., additional, Beecham, Gary W., additional, Haines, Jonathan L., additional, and Pericak‐Vance, Margaret A., additional
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- 2022
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42. Astrocyte specific chromatin accessibility differences in European vs African Alzheimer Disease brains
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Celis, Katrina, primary, Griswold, Anthony J., additional, Rajabli, Farid, additional, Whitehead, Patrice L., additional, Hamilton‐Nelson, Kara L., additional, Dykxhoorn, Derek M., additional, Nuytemans, Karen, additional, Wang, Liyong, additional, Weintraub, Sandra, additional, Geula, Changiz, additional, Gearing, Marla, additional, Trojanowski, John Q, additional, Bennett, David A, additional, Pericak‐Vance, Margaret A., additional, Young, Juan J, additional, Vance, Jeffery M., additional, and Flanagan, Margaret E, additional
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- 2022
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43. The Alzheimer’s Disease Sequencing Project Follow Up Study (ADSP‐FUS): increasing ethnic diversity in Alzheimer’s disease (AD) genetics research.
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Mena, Pedro Ramon, primary, Kunkle, Brian W., additional, Faber, Kelley M., additional, Adams, Larry D., additional, Inciute, Jovita D., additional, Whitehead, Patrice L., additional, Foroud, Tatiana M., additional, Reyes‐Dumeyer, Dolly, additional, Kuzma, Amanda B, additional, Leung, Yuk Yee, additional, Naj, Adam C., additional, Martin, Eden R., additional, Dalgard, Clifton L., additional, Schellenberg, Gerard D., additional, Wang, Li‐San, additional, Mayeux, Richard, additional, Vardarajan, Badri N., additional, Vance, Jeffery M., additional, Cuccaro, Michael L., additional, and Pericak‐Vance, Margaret A., additional
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- 2022
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44. Author Correction: Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing
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Kunkle, Brian W., Grenier-Boley, Benjamin, Sims, Rebecca, Bis, Joshua C., Damotte, Vincent, Naj, Adam C., Boland, Anne, Vronskaya, Maria, van der Lee, Sven J., Amlie-Wolf, Alexandre, Bellenguez, Céline, Frizatti, Aura, Chouraki, Vincent, Martin, Eden R., Sleegers, Kristel, Badarinarayan, Nandini, Jakobsdottir, Johanna, Hamilton-Nelson, Kara L., Moreno-Grau, Sonia, Olaso, Robert, Raybould, Rachel, Chen, Yuning, Kuzma, Amanda B., Hiltunen, Mikko, Morgan, Taniesha, Ahmad, Shahzad, Vardarajan, Badri N., Epelbaum, Jacques, Hoffmann, Per, Boada, Merce, Beecham, Gary W., Garnier, Jean-Guillaume, Harold, Denise, Fitzpatrick, Annette L., Valladares, Otto, Moutet, Marie-Laure, Gerrish, Amy, Smith, Albert V., Qu, Liming, Bacq, Delphine, Denning, Nicola, Jian, Xueqiu, Zhao, Yi, Del Zompo, Maria, Fox, Nick C., Choi, Seung-Hoan, Mateo, Ignacio, Hughes, Joseph T., Adams, Hieab H., Malamon, John, Sanchez-Garcia, Florentino, Patel, Yogen, Brody, Jennifer A., Dombroski, Beth A., Naranjo, Maria Candida Deniz, Daniilidou, Makrina, Eiriksdottir, Gudny, Mukherjee, Shubhabrata, Wallon, David, Uphill, James, Aspelund, Thor, Cantwell, Laura B., Garzia, Fabienne, Galimberti, Daniela, Hofer, Edith, Butkiewicz, Mariusz, Fin, Bertrand, Scarpini, Elio, Sarnowski, Chloe, Bush, Will S., Meslage, Stéphane, Kornhuber, Johannes, White, Charles C., Song, Yuenjoo, Barber, Robert C., Engelborghs, Sebastiaan, Sordon, Sabrina, Voijnovic, Dina, Adams, Perrie M., Vandenberghe, Rik, Mayhaus, Manuel, Cupples, L. Adrienne, Albert, Marilyn S., De Deyn, Peter P., Gu, Wei, Himali, Jayanadra J., Beekly, Duane, Squassina, Alessio, Hartmann, Annette M., Orellana, Adelina, Blacker, Deborah, Rodriguez-Rodriguez, Eloy, Lovestone, Simon, Garcia, Melissa E., Doody, Rachelle S., Munoz-Fernadez, Carmen, Sussams, Rebecca, Lin, Honghuang, Fairchild, Thomas J., Benito, Yolanda A., Holmes, Clive, Karamujić-Čomić, Hata, Frosch, Matthew P., Thonberg, Hakan, Maier, Wolfgang, Roshchupkin, Gennady, Ghetti, Bernardino, Giedraitis, Vilmantas, Kawalia, Amit, Li, Shuo, Huebinger, Ryan M., Kilander, Lena, Moebus, Susanne, Hernández, Isabel, Kamboh, M. Ilyas, Brundin, RoseMarie, Turton, James, Yang, Qiong, Katz, Mindy J., Concari, Letizia, Lord, Jenny, Beiser, Alexa S., Keene, C. Dirk, Helisalmi, Seppo, Kloszewska, Iwona, Kukull, Walter A., Koivisto, Anne Maria, Lynch, Aoibhinn, Tarraga, Lluís, Larson, Eric B., Haapasalo, Annakaisa, Lawlor, Brian, Mosley, Thomas H., Lipton, Richard B., Solfrizzi, Vincenzo, Gill, Michael, Longstreth, Jr, W. T., Montine, Thomas J., Frisardi, Vincenza, Diez-Fairen, Monica, Rivadeneira, Fernando, Petersen, Ronald C., Deramecourt, Vincent, Alvarez, Ignacio, Salani, Francesca, Ciaramella, Antonio, Boerwinkle, Eric, Reiman, Eric M., Fievet, Nathalie, Rotter, Jerome I., Reisch, Joan S., Hanon, Olivier, Cupidi, Chiara, Uitterlinden, A. G. Andre, Royall, Donald R., Dufouil, Carole, Maletta, Raffaele Giovanni, de Rojas, Itziar, Sano, Mary, Brice, Alexis, Cecchetti, Roberta, George-Hyslop, Peter St, Ritchie, Karen, Tsolaki, Magda, Tsuang, Debby W., Dubois, Bruno, Craig, David, Wu, Chuang-Kuo, Soininen, Hilkka, Avramidou, Despoina, Albin, Roger L., Fratiglioni, Laura, Germanou, Antonia, Apostolova, Liana G., Keller, Lina, Koutroumani, Maria, Arnold, Steven E., Panza, Francesco, Gkatzima, Olymbia, Asthana, Sanjay, Hannequin, Didier, Whitehead, Patrice, Atwood, Craig S., Caffarra, Paolo, Hampel, Harald, Quintela, Inés, Carracedo, Ángel, Lannfelt, Lars, Rubinsztein, David C., Barnes, Lisa L., Pasquier, Florence, Frölich, Lutz, Barral, Sandra, McGuinness, Bernadette, Beach, Thomas G., Johnston, Janet A., Becker, James T., Passmore, Peter, Bigio, Eileen H., Schott, Jonathan M., Bird, Thomas D., Warren, Jason D., Boeve, Bradley F., Lupton, Michelle K., Bowen, James D., Proitsi, Petra, Boxer, Adam, Powell, John F., Burke, James R., Kauwe, John S. K., Burns, Jeffrey M., Mancuso, Michelangelo, Buxbaum, Joseph D., Bonuccelli, Ubaldo, Cairns, Nigel J., McQuillin, Andrew, Cao, Chuanhai, Livingston, Gill, Carlson, Chris S., Bass, Nicholas J., Carlsson, Cynthia M., Hardy, John, Carney, Regina M., Bras, Jose, Carrasquillo, Minerva M., Guerreiro, Rita, Allen, Mariet, Chui, Helena C., Fisher, Elizabeth, Masullo, Carlo, Crocco, Elizabeth A., DeCarli, Charles, Bisceglio, Gina, Dick, Malcolm, Ma, Li, Duara, Ranjan, Graff-Radford, Neill R., Evans, Denis A., Hodges, Angela, Faber, Kelley M., Scherer, Martin, Fallon, Kenneth B., Riemenschneider, Matthias, Fardo, David W., Heun, Reinhard, Farlow, Martin R., Kölsch, Heike, Ferris, Steven, Leber, Markus, Foroud, Tatiana M., Heuser, Isabella, Galasko, Douglas R., Giegling, Ina, Gearing, Marla, Hüll, Michael, Geschwind, Daniel H., Gilbert, John R., Morris, John, Green, Robert C., Mayo, Kevin, Growdon, John H., Feulner, Thomas, Hamilton, Ronald L., Harrell, Lindy E., Drichel, Dmitriy, Honig, Lawrence S., Cushion, Thomas D., Huentelman, Matthew J., Hollingworth, Paul, Hulette, Christine M., Hyman, Bradley T., Marshall, Rachel, Jarvik, Gail P., Meggy, Alun, Abner, Erin, Menzies, Georgina E., Jin, Lee-Way, Leonenko, Ganna, Real, Luis M., Jun, Gyungah R., Baldwin, Clinton T., Grozeva, Detelina, Karydas, Anna, Russo, Giancarlo, Kaye, Jeffrey A., Kim, Ronald, Jessen, Frank, Kowall, Neil W., Vellas, Bruno, Kramer, Joel H., Vardy, Emma, LaFerla, Frank M., Jöckel, Karl-Heinz, Lah, James J., Dichgans, Martin, Leverenz, James B., Mann, David, Levey, Allan I., Pickering-Brown, Stuart, Lieberman, Andrew P., Klopp, Norman, Lunetta, Kathryn L., Wichmann, H-Erich, Lyketsos, Constantine G., Morgan, Kevin, Marson, Daniel C., Brown, Kristelle, Martiniuk, Frank, Medway, Christopher, Mash, Deborah C., Nöthen, Markus M., Masliah, Eliezer, Hooper, Nigel M., McCormick, Wayne C., Daniele, Antonio, McCurry, Susan M., Bayer, Anthony, McDavid, Andrew N., Gallacher, John, McKee, Ann C., van den Bussche, Hendrik, Mesulam, Marsel, Brayne, Carol, Miller, Bruce L., Riedel-Heller, Steffi, Miller, Carol A., Miller, Joshua W., Al-Chalabi, Ammar, Morris, John C., Shaw, Christopher E., Myers, Amanda J., Wiltfang, Jens, O’Bryant, Sid, Olichney, John M., Alvarez, Victoria, Parisi, Joseph E., Singleton, Andrew B., Paulson, Henry L., Collinge, John, Perry, William R., Mead, Simon, Peskind, Elaine, Cribbs, David H., Rossor, Martin, Pierce, Aimee, Ryan, Natalie S., Poon, Wayne W., Nacmias, Benedetta, Potter, Huntington, Sorbi, Sandro, Quinn, Joseph F., Sacchinelli, Eleonora, Raj, Ashok, Spalletta, Gianfranco, Raskind, Murray, Caltagirone, Carlo, Bossù, Paola, Orfei, Maria Donata, Reisberg, Barry, Clarke, Robert, Reitz, Christiane, Smith, A David, Ringman, John M., Warden, Donald, Roberson, Erik D., Wilcock, Gordon, Rogaeva, Ekaterina, Bruni, Amalia Cecilia, Rosen, Howard J., Gallo, Maura, Rosenberg, Roger N., Ben-Shlomo, Yoav, Sager, Mark A., Mecocci, Patrizia, Saykin, Andrew J., Pastor, Pau, Cuccaro, Michael L., Vance, Jeffery M., Schneider, Julie A., Schneider, Lori S., Slifer, Susan, Seeley, William W., Smith, Amanda G., Sonnen, Joshua A., Spina, Salvatore, Stern, Robert A., Swerdlow, Russell H., Tang, Mitchell, Tanzi, Rudolph E., Trojanowski, John Q., Troncoso, Juan C., Van Deerlin, Vivianna M., Van Eldik, Linda J., Vinters, Harry V., Vonsattel, Jean Paul, Weintraub, Sandra, Welsh-Bohmer, Kathleen A., Wilhelmsen, Kirk C., Williamson, Jennifer, Wingo, Thomas S., Woltjer, Randall L., Wright, Clinton B., Yu, Chang-En, Yu, Lei, Saba, Yasaman, Alzheimer Disease Genetics Consortium (ADGC), The European Alzheimer’s Disease Initiative (EADI), Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (CHARGE), Genetic and Environmental Risk in AD/Defining Genetic, Polygenic and Environmental Risk for Alzheimer’s Disease Consortium (GERAD/PERADES), Pilotto, Alberto, Bullido, Maria J., Peters, Oliver, Crane, Paul K., Bennett, David, Bosco, Paola, Coto, Eliecer, Boccardi, Virginia, De Jager, Phil L., Lleo, Alberto, Warner, Nick, Lopez, Oscar L., Ingelsson, Martin, Deloukas, Panagiotis, Cruchaga, Carlos, Graff, Caroline, Gwilliam, Rhian, Fornage, Myriam, Goate, Alison M., Sanchez-Juan, Pascual, Kehoe, Patrick G., Amin, Najaf, Ertekin-Taner, Nilifur, Berr, Claudine, Debette, Stéphanie, Love, Seth, Launer, Lenore J., Younkin, Steven G., Dartigues, Jean-Francois, Corcoran, Chris, Ikram, M. Arfan, Dickson, Dennis W., Nicolas, Gael, Campion, Dominique, Tschanz, JoAnn, Schmidt, Helena, Hakonarson, Hakon, Clarimon, Jordi, Munger, Ron, Schmidt, Reinhold, Farrer, Lindsay A., Van Broeckhoven, Christine, O’Donovan, Michael C., DeStefano, Anita L., Jones, Lesley, Haines, Jonathan L., Deleuze, Jean-Francois, Owen, Michael J., Gudnason, Vilmundur, Mayeux, Richard, Escott-Price, Valentina, Psaty, Bruce M., Ramirez, Alfredo, Wang, Li-San, Ruiz, Agustin, van Duijn, Cornelia M., Holmans, Peter A., Seshadri, Sudha, Williams, Julie, Amouyel, Phillippe, Schellenberg, Gerard D., Lambert, Jean-Charles, and Pericak-Vance, Margaret A.
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- 2019
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45. Ancestry-related differences in chromatin accessibility and gene expression ofAPOE4are associated with Alzheimer disease risk
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Celis, Katrina, primary, Muniz Moreno, Maria DM., additional, Rajabli, Farid, additional, Whitehead, Patrice, additional, Hamilton-Nelson, Kara, additional, Dykxhoorn, Derek M., additional, Nuytemans, Karen, additional, Wang, Liyong, additional, Dalgard, Clifton L., additional, Flanagan, Margaret, additional, Weintraub, Sandra, additional, Geula, Changiz, additional, Gearing, Marla, additional, Bennett, David A., additional, Schuck, Theresa, additional, Jin, Fulai, additional, Pericak-Vance, Margaret A., additional, Griswold, Anthony J., additional, Young, Juan I., additional, and Vance, Jeffery M., additional
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- 2022
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46. Early-Onset Alzheimer Disease and Candidate Risk Genes Involved in Endolysosomal Transport
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Kunkle, Brian W., Vardarajan, Badri N., Naj, Adam C., Whitehead, Patrice L., Rolati, Sophie, Slifer, Susan, Carney, Regina M., Cuccaro, Michael L., Vance, Jeffery M., Gilbert, John R., Wang, Li-San, Farrer, Lindsay A., Reitz, Christiane, Haines, Jonathan L., Beecham, Gary W., Martin, Eden R., Schellenberg, Gerard D., Mayeux, Richard P., and Pericak-Vance, Margaret A.
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- 2017
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47. Admixture mapping identifies novel Alzheimer's disease risk regions in African Americans.
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Rajabli, Farid, Tosto, Giuseppe, Hamilton‐Nelson, Kara L., Kunkle, Brian W., Vardarajan, Badri N., Naj, Adam, Whitehead, Patrice G., Gardner, Olivia K., Bush, William S., Sariya, Sanjeev, Mayeux, Richard P., Farrer, Lindsay A., Cuccaro, Michael L., Vance, Jeffrey M., Griswold, Anthony J., Schellenberg, Gerard D., Haines, Jonathan L., Byrd, Goldie S., Reitz, Christiane, and Beecham, Gary W.
- Abstract
Background: This study used admixture mapping to prioritize the genetic regions associated with Alzheimer's disease (AD) in African American (AA) individuals, followed by ancestry‐aware regression analysis to fine‐map the prioritized regions. Methods: We analyzed 10,271 individuals from 17 different AA datasets. We performed admixture mapping and meta‐analyzed the results. We then used regression analysis, adjusting for local ancestry main effects and interactions with genotype, to refine the regions identified from admixture mapping. Finally, we leveraged in silico annotation and differential gene expression data to prioritize AD‐related variants and genes. Results: Admixture mapping identified two genome‐wide significant loci on chromosomes 17p13.2 (p = 2.2 × 10−5) and 18q21.33 (p = 1.2 × 10−5). Our fine mapping of the chromosome 17p13.2 and 18q21.33 regions revealed several interesting genes such as the MINK1, KIF1C, and BCL2. Discussion: Our ancestry‐aware regression approach showed that AA individuals have a lower risk of AD if they inherited African ancestry admixture block at the 17p13.2 locus. Highlights: We identified two genome‐wide significant admixture mapping signals: on chromosomes 17p13.2 and 18q21.33, which are novel in African American (AA) populations.Our ancestry‐aware regression approach showed that AA individuals have a lower risk of Alzheimer's disease (AD) if they inherited African ancestry admixture block at the 17p13.2 locus.We found that the overall proportion of African ancestry does not differ between the cases and controls that suggest African genetic ancestry alone is not likely to explain the AD prevalence difference between AA and non‐Hispanic White populations. [ABSTRACT FROM AUTHOR]
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- 2023
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48. Genetic variants in the SHISA6 gene are associated with delayed cognitive impairment in two family datasets
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Ramos, Jairo, primary, Caywood, Laura J., additional, Prough, Michael B., additional, Clouse, Jason E., additional, Herington, Sharlene D., additional, Slifer, Susan H., additional, Fuzzell, M. Denise, additional, Fuzzell, Sarada L., additional, Hochstetler, Sherri D., additional, Miskimen, Kristy L., additional, Main, Leighanne R., additional, Osterman, Michael D., additional, Zaman, Andrew F., additional, Whitehead, Patrice L., additional, Adams, Larry D., additional, Laux, Renee A., additional, Song, Yeunjoo E., additional, Foroud, Tatiana M., additional, Mayeux, Richard P., additional, St. George‐Hyslop, Peter, additional, Ogrocki, Paula K., additional, Lerner, Alan J., additional, Vance, Jeffery M., additional, Cuccaro, Michael L., additional, Haines, Jonathan L., additional, Pericak‐Vance, Margaret A., additional, and Scott, William K., additional
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- 2022
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49. A new risk locus on chromosome 1 is suggested by genome‐wide association study in Peruvians for Alzheimer disease.
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Akgun, Bilcag, Cornejo‐Olivas, Mario, Custodio, Nilton, Rajabli, Farid, Soto‐Añari, Marcio F., Montesinos, Rosa, Yang, Zikun, Huaman, Basilio C., Reyes‐Dumeyer, Dolly, Cedeno, Jeffrey A, Rivero, Joe, Mena, Pedro R., Adams, Larry D., Whitehead, Patrice, Hamilton‐Nelson, Kara L., Rios‐Pinto, Julia, Medina‐Colque, Angel, Dalgard, Clifton L., Isasi, Rosario, and Cornejo‐Herrera, Ivan
- Abstract
Background: Increasing ethnic/ancestral diversity in genetic studies is critical for defining the genetic architecture of Alzheimer disease (AD). Amerindian (AI) populations are substantially underrepresented in AD genetic studies. The Peruvian (PE) population, with up to ∼80% of AI ancestry, provides a unique opportunity to assess the role of AI ancestry in AD. We performed the first genome‐wide association study (GWAS) in the PE population to identify novel AD susceptibility loci and characterize known AD genetic risk loci. Method: The PE dataset includes array‐genotype and phenotype data from 542 individuals (189 cases; 353 controls), imputed to the NHLBI TOPMedv5 haplotype reference panel. We used a generalized linear mixed‐model (SAIGE software) for the GWAS analysis. We analyzed two separate models; the first model accounted for sex, age, and population substructure, while the second model also included the dosage of APOEe4. In both models, we included a genetic relationship matrix as a random effect to account for any potential relatedness. To determine if the associations are specific to specific ancestries, we employed ancestry‐aware approaches using the RFMix software. Result: APOE was significantly associated with AD with an effect size comparable to that found in non‐Hispanic white (NHW) populations (OR = 3.3(2.2‐4.8),pv = 8.0×10−10). Two additional known AD loci, TREML2 (pv = 0.008) and CLU (pv = 0.012), showed nominal significance Variants at three additional loci reached suggestive significance (pv<1×10−6): NFASC (pv = 9.4×10−8;chromosome 1), STK32A (pv = 9.3×10−7; chromosome 5), and LOC100132830 (pv = 6.7×10−7;chromosome 6). The NFASC locus neared genome‐wide significance in the APOE adjusted model (pv = 6.7×10−8). The haplotypes associated with AD at the NFASC locus were found to be of European origin. Additionally, the STK32A locus was found to have a protective effect specifically among individuals of AI background. We did not observe significant heterogeneity of effect at the APOE and LOC100132830 loci across different ancestral backgrounds. Conclusion: PE GWAS identified a novel, promising AD susceptibility locus in the NFASC gene of European origin. We also detected a potential protective effect in the STK32A locus on AI background, emphasizing the importance of incorporating ancestry‐aware approaches in gene discovery in admixed populations. [ABSTRACT FROM AUTHOR]
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- 2023
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50. Genetic architecture of RNA editing regulation in Alzheimer’s disease across diverse ancestral populations
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Gardner, Olivia K, primary, Van Booven, Derek, additional, Wang, Lily, additional, Gu, Tianjie, additional, Hofmann, Natalia K, additional, Whitehead, Patrice L, additional, Nuytemans, Karen, additional, Hamilton-Nelson, Kara L, additional, Adams, Larry D, additional, Starks, Takiyah D, additional, Cuccaro, Michael L, additional, Martin, Eden R, additional, Vance, Jeffery M, additional, Bush, William S, additional, Byrd, Goldie S, additional, Haines, Jonathan L, additional, Beecham, Gary W, additional, Pericak-Vance, Margaret A, additional, and Griswold, Anthony J, additional
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- 2022
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