Trans-ethnic genomic informed risk assessment for Alzheimer's disease: An International Hundred K plus Cohorts Consortium study

Nenhuma Miniatura disponível
Citações na Scopus
1
Tipo de produção
article
Data de publicação
2023
DOI
Scopus
Web of Science
PubMed
Dimensions
Título da Revista
ISSN da Revista
Título do Volume
Editora
WILEY
Autores
SLEIMAN, Patrick M.
QU, Hui-Qi
CONNOLLY, John J.
MENTCH, Frank
TOLLMAN, Stephen
CHOUDHURY, Ananyo
RAMSAY, Michele
KATO, Norihiro
Citação
ALZHEIMERS & DEMENTIA, v.19, n.12, p.5765-5772, 2023
Projetos de Pesquisa
Unidades Organizacionais
Fascículo
Resumo
BackgroundAs a collaboration model between the International HundredK+ Cohorts Consortium (IHCC) and the Davos Alzheimer's Collaborative (DAC), our aim was to develop a trans-ethnic genomic informed risk assessment (GIRA) algorithm for Alzheimer's disease (AD). MethodsThe GIRA model was created to include polygenic risk score calculated from the AD genome-wide association study loci, the apolipoprotein E haplotypes, and non-genetic covariates including age, sex, and the first three principal components of population substructure. ResultsWe validated the performance of the GIRA model in different populations. The proteomic study in the participant sites identified proteins related to female infertility and autoimmune thyroiditis and associated with the risk scores of AD. ConclusionsAs the initial effort by the IHCC to leverage existing large-scale datasets in a collaborative setting with DAC, we developed a trans-ethnic GIRA for AD with the potential of identifying individuals at high risk of developing AD for future clinical applications.
Palavras-chave
Alzheimer's disease, data sharing, female infertility, genomic informed risk assessment, minority population, polygenic risk score, thyroid, trans-ethnic
URI
https://observatorio.fm.usp.br/handle/OPI/58787
Referências
  1. Ali SA, 2018, GLOBAL HEALTH ACTION, V11, P4, DOI 10.1080/16549716.2018.1507133
  2. Bae JB, 2020, BMC MED, V18, DOI 10.1186/s12916-020-01671-1
  3. Belbin O, 2007, HUM MOL GENET, V16, P2199, DOI 10.1093/hmg/ddm171
  4. Bellenguez C, 2022, NAT GENET, V54, P412, DOI 10.1038/s41588-022-01024-z
  5. Blum CB, 2016, PROG CARDIOVASC DIS, V59, P119, DOI 10.1016/j.pcad.2016.07.007
  6. Cacabelos Ramon, 2005, Methods Find Exp Clin Pharmacol, V27 Suppl A, P1
  7. Chételat G, 2020, LANCET NEUROL, V19, P951, DOI 10.1016/S1474-4422(20)30314-8
  8. Chor D, 2019, PLOS ONE, V14, DOI 10.1371/journal.pone.0216653
  9. Dartigues Jean-Francois, 2011, Rev Prat, V61, P926
  10. Desai AK, 2005, NEUROLOGY, V64, pS34, DOI 10.1212/WNL.64.12_suppl_3.S34
  11. Di Angelantonio E, 2017, LANCET, V390, P2360, DOI 10.1016/S0140-6736(17)31928-1
  12. Emrani S, 2020, ALZHEIMERS RES THER, V12, DOI 10.1186/s13195-020-00712-4
  13. Fasolino M, 2022, NAT METAB, V4, P284, DOI 10.1038/s42255-022-00531-x
  14. Finer S, 2020, INT J EPIDEMIOL, V49, DOI 10.1093/ije/dyz174
  15. Huang YD, 2014, NEUROBIOL DIS, V72, P3, DOI 10.1016/j.nbd.2014.08.025
  16. Jun GR, 2017, ALZHEIMERS DEMENT, V13, P727, DOI 10.1016/j.jalz.2016.12.012
  17. Kanekiyo T, 2014, NEURON, V81, P740, DOI 10.1016/j.neuron.2014.01.045
  18. Khan TK, 2016, BIOMARKERS IN ALZHEIMER'S DISEASE, P27, DOI 10.1016/B978-0-12-804832-0.00002-X
  19. Khera AV, 2018, NAT GENET, V50, P1219, DOI 10.1038/s41588-018-0183-z
  20. Koutsodendris N, 2022, ANNU REV PATHOL-MECH, V17, P73, DOI 10.1146/annurev-pathmechdis-030421-112756
  21. Lambert SA, 2019, HUM MOL GENET, V28, pR133, DOI 10.1093/hmg/ddz187
  22. Lindstedt G, 1998, CLIN CHEM LAB MED, V36, P663, DOI 10.1515/CCLM.1998.118
  23. Manolio TA, 2020, LANCET DIGIT HEALTH, V2, pE567, DOI 10.1016/S2589-7500(20)30242-9
  24. Martens YA, 2022, NEURON, V110, P1304, DOI 10.1016/j.neuron.2022.03.004
  25. Martin AR, 2019, NAT GENET, V51, P584, DOI 10.1038/s41588-019-0379-x
  26. MATTHEWS CH, 1993, NAT GENET, V5, P83, DOI 10.1038/ng0993-83
  27. MCKHANN G, 1984, NEUROLOGY, V34, P939, DOI 10.1212/WNL.34.7.939
  28. Ogama N, 2016, GERIATR GERONTOL INT, V16, P167, DOI 10.1111/ggi.12447
  29. Orlowski M., 2018, FOLLICLE STIMULATING, V6
  30. Patel AP, 2023, ANNU REV MED, V74, P141, DOI 10.1146/annurev-med-042921-112629
  31. Petersen RC, 2001, ARCH NEUROL-CHICAGO, V58, P1985, DOI 10.1001/archneur.58.12.1985
  32. Phillips MC, 2014, IUBMB LIFE, V66, P616, DOI 10.1002/iub.1314
  33. Qu HQ, 2022, PEDIATR DIABETES, V23, P320, DOI 10.1111/pedi.13310
  34. Ramsay M, 2016, GLOB HEALTH EPIDEM G, V1, DOI 10.1017/gheg.2016.17
  35. Ramsay M, 2016, INT J EPIDEMIOL, V45, P305, DOI 10.1093/ije/dyv187
  36. Resnick SM, 2002, JAMA-J AM MED ASSOC, V288, P2170, DOI 10.1001/jama.288.17.2170
  37. Riedel BC, 2016, J STEROID BIOCHEM, V160, P134, DOI 10.1016/j.jsbmb.2016.03.012
  38. Roh HW, 2022, PSYCHIAT INVEST, V19, P100, DOI 10.30773/pi.2021.0335
  39. Roses Allen D., 1994, Current Opinion in Biotechnology, V5, P663, DOI 10.1016/0958-1669(94)90091-4
  40. Scheltens P, 2011, ALZHEIMERS DEMENT, V7, P486, DOI 10.1016/j.jalz.2011.04.011
  41. Shao HB, 2012, NEUROLOGY, V79, P1846, DOI 10.1212/WNL.0b013e318271f823
  42. Sirugo G, 2019, CELL, V177, P26, DOI [10.1016/j.cell.2019.02.048, 10.1016/j.cell.2019.04.032]
  43. Stevenson-Hoare J, 2023, BRAIN, V146, P690, DOI 10.1093/brain/awac128
  44. Sudlow C, 2015, PLOS MED, V12, DOI 10.1371/journal.pmed.1001779
  45. Tan R S, 2003, Aging Male, V6, P13, DOI 10.1080/713604736
  46. Tan ZS, 2008, ARCH INTERN MED, V168, P1514, DOI 10.1001/archinte.168.14.1514
  47. Wray NR, 2021, JAMA PSYCHIAT, V78, P101, DOI 10.1001/jamapsychiatry.2020.3049

Coleções
Artigos e Materiais de Revistas Científicas - FM/MCM