Genetic Variation and Autism: A Field Synopsis and Systematic Meta-Analysis

Carregando...
Imagem de Miniatura
Citações na Scopus
7
Tipo de produção
article
Data de publicação
2020
Título da Revista
ISSN da Revista
Título do Volume
Editora
MDPI
Autores
LEE, Jinhee
SON, Min Ji
SON, Chei Yun
JEONG, Gwang Hun
LEE, Keum Hwa
LEE, Kwang Seob
KO, Younhee
KIM, Jong Yeob
LEE, Jun Young
RADUA, Joaquim
Citação
BRAIN SCIENCES, v.10, n.10, article ID 692, 25p, 2020
Projetos de Pesquisa
Unidades Organizacionais
Fascículo
Resumo
This study aimed to verify noteworthy findings between genetic risk factors and autism spectrum disorder (ASD) by employing the false positive report probability (FPRP) and the Bayesian false-discovery probability (BFDP). PubMed and the Genome-Wide Association Studies (GWAS) catalog were searched from inception to 1 August, 2019. We included meta-analyses on genetic factors of ASD of any study design. Overall, twenty-seven meta-analyses articles from literature searches, and four manually added articles from the GWAS catalog were re-analyzed. This showed that five of 31 comparisons for meta-analyses of observational studies, 40 out of 203 comparisons for the GWAS meta-analyses, and 18 out of 20 comparisons for the GWAS catalog, respectively, had noteworthy estimations under both Bayesian approaches. In this study, we found noteworthy genetic comparisons highly related to an increased risk of ASD. Multiple genetic comparisons were shown to be associated with ASD risk; however, genuine associations should be carefully verified and understood.
Palavras-chave
autism spectrum disorder, false positive report probability (FPRP), Bayesian false-discovery probability (BFDP), meta-analysis, Genome-Wide Association Studies (GWAS)
Referências
  1. American Psychiatric Association, 2013, DIAGNOSTIC STAT MANU
  2. Anney R, 2012, HUM MOL GENET, V21, P4781, DOI 10.1093/hmg/dds301
  3. Anney R, 2010, HUM MOL GENET, V19, P4072, DOI 10.1093/hmg/ddq307
  4. [Anonymous], 2017, MOL AUTISM, V8, P21, DOI 10.1186/s13229-017-0137-9
  5. Aoki Y, 2016, MOL NEUROBIOL, V53, P1579, DOI 10.1007/s12035-015-9116-3
  6. Bai D, 2019, JAMA PSYCHIAT, V76, P1035, DOI 10.1001/jamapsychiatry.2019.1411
  7. Chaste P, 2015, BIOL PSYCHIAT, V77, P775, DOI 10.1016/j.biopsych.2014.09.017
  8. Chen N, 2017, BEHAV BRAIN RES, V332, P110, DOI 10.1016/j.bbr.2017.05.028
  9. Christensen DL, 2016, MMWR SURVEILL SUMM, V65, P1, DOI 10.15585/mmwr.ss6503a1
  10. Curran S, 2011, AM J MED GENET B, V156B, P633, DOI 10.1002/ajmg.b.31201
  11. Glessner JT, 2009, NATURE, V459, P569, DOI 10.1038/nature07953
  12. Grove J, 2019, NAT GENET, V51, P431, DOI 10.1038/s41588-019-0344-8
  13. Huang CH, 2008, AM J MED GENET B, V147B, P903, DOI 10.1002/ajmg.b.30720
  14. Kim JY, 2019, LANCET PSYCHIAT, V6, P590, DOI 10.1016/S2215-0366(19)30181-6
  15. Kranz TM, 2016, AUTISM RES, V9, P1036, DOI 10.1002/aur.1597
  16. Kuo PH, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0138695
  17. Liu J, 2015, AM J MED GENET B, V168, P236, DOI 10.1002/ajmg.b.32304
  18. LoParo D, 2015, MOL PSYCHIATR, V20, P640, DOI 10.1038/mp.2014.77
  19. Lyall K, 2017, ANNU REV PUBL HEALTH, V38, P81, DOI 10.1146/annurev-publhealth-031816-044318
  20. Ma DQ, 2009, ANN HUM GENET, V73, P263, DOI 10.1111/j.1469-1809.2009.00523.x
  21. MacArthur J, 2017, NUCLEIC ACIDS RES, V45, pD896, DOI 10.1093/nar/gkw1133
  22. MacGregor AJ, 2000, TRENDS GENET, V16, P131, DOI 10.1016/S0168-9525(99)01946-0
  23. Mahdavi M, 2018, J MOL NEUROSCI, V65, P1, DOI 10.1007/s12031-018-1073-7
  24. Main PAE, 2012, NUTR METAB, V9, DOI 10.1186/1743-7075-9-35
  25. Modabbernia A, 2017, MOL AUTISM, V8, DOI 10.1186/s13229-017-0121-4
  26. Mohammad NS, 2016, PSYCHIAT GENET, V26, P281, DOI 10.1097/YPG.0000000000000152
  27. Noroozi R, 2018, J MOL NEUROSCI, V65, P432, DOI 10.1007/s12031-018-1114-2
  28. Panagiotou OA, 2012, INT J EPIDEMIOL, V41, P273, DOI 10.1093/ije/dyr178
  29. Pu DH, 2013, AUTISM RES, V6, P384, DOI 10.1002/aur.1300
  30. Purcell AE, 2001, NEUROLOGY, V57, P1618, DOI 10.1212/WNL.57.9.1618
  31. Rai V, 2016, METAB BRAIN DIS, V31, P727, DOI 10.1007/s11011-016-9815-0
  32. Robinson EB, 2016, NAT GENET, V48, P552, DOI 10.1038/ng.3529
  33. Ronald A, 2011, AM J MED GENET B, V156B, P255, DOI 10.1002/ajmg.b.31159
  34. Skaar DA, 2005, MOL PSYCHIATR, V10, P563, DOI 10.1038/sj.mp.4001614
  35. Song RR, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0025603
  36. State MW, 2011, NAT NEUROSCI, V14, P1499, DOI 10.1038/nn.2924
  37. Szklarczyk D, 2015, NUCLEIC ACIDS RES, V43, pD447, DOI 10.1093/nar/gku1003
  38. Torrico B, 2017, AUTISM RES, V10, P202, DOI 10.1002/aur.1662
  39. Torrico B, 2015, EUR J HUM GENET, V23, P1694, DOI 10.1038/ejhg.2015.37
  40. Wacholder S, 2004, J NATL CANCER I, V96, P434, DOI 10.1093/jnci/djh075
  41. Wakefield J, 2007, AM J HUM GENET, V81, P208, DOI 10.1086/519024
  42. Waltes R, 2014, HUM GENET, V133, P781, DOI 10.1007/s00439-013-1416-y
  43. Wang K, 2009, NATURE, V459, P528, DOI 10.1038/nature07999
  44. Wang ZL, 2014, AM J MED GENET B, V165, P192, DOI 10.1002/ajmg.b.32222
  45. Warrier V, 2015, MOL AUTISM, V6, DOI 10.1186/s13229-015-0041-0
  46. Werling AM, 2016, J NEURAL TRANSM, V123, P353, DOI 10.1007/s00702-015-1458-5
  47. Xia K, 2014, MOL PSYCHIATR, V19, P1212, DOI 10.1038/mp.2013.146
  48. Xu GF, 2018, JAMA-J AM MED ASSOC, V319, P81, DOI 10.1001/jama.2017.17812
  49. Yang PY, 2017, AUTISM RES, V10, P1722, DOI 10.1002/aur.1822
  50. Zablotsky Benjamin, 2015, Natl Health Stat Report, P1
  51. Zhang T, 2019, AUTISM RES, V12, P553, DOI 10.1002/aur.2078