Additional file 8 of PhenoExam: gene set analyses through integration of different phenotype databases

  1. Cisterna, Alejandro 1
  2. González-Vidal, Aurora 1
  3. Ruiz, Daniel 1
  4. Ortiz, Jordi 1
  5. Gómez-Pascual, Alicia 1
  6. Chen, Zhongbo 2
  7. Nalls, Mike 345
  8. Faghri, Faraz 345
  9. Hardy, John 26
  10. Díez, Irene
  11. Maietta, Paolo
  12. Álvarez, Sara
  13. Ryten, Mina 2
  14. Botía, Juan A. 12
  1. 1 Universidad de Murcia
    info

    Universidad de Murcia

    Murcia, España

    ROR https://ror.org/03p3aeb86

  2. 2 University College London
    info

    University College London

    Londres, Reino Unido

    ROR https://ror.org/02jx3x895

  3. 3 National Institute on Aging
    info

    National Institute on Aging

    Baltimore, Estados Unidos

    ROR https://ror.org/049v75w11

  4. 4 Data Tecnica International (United States)
  5. 5 National Institutes of Health
    info

    National Institutes of Health

    Bethesda, Estados Unidos

    ROR https://ror.org/01cwqze88

  6. 6 Hong Kong University of Science and Technology
    info

    Hong Kong University of Science and Technology

    Hong Kong, Hong Kong

    ROR https://ror.org/00q4vv597

Mostrar afiliaciones +

Editor: figshare

Año de publicación: 2024

Tipo: Dataset

CC BY 4.0

DOI: 10.6084/M9.FIGSHARE.26558660.V1 lock_openAcceso abierto editor

Resumen

Additional file 8: Table S7. Genetic variants detected from Epi25 whole-exome sequencing in epilepsy predicted genes. Data and number of genetic variants from the Epi25 whole-exome sequencing (WES) case-control study of each epilepsy gene predicted, we obtained 665 genetic variants in cases and 446 in controls.

Referencias bibliográficas

  • 10.1038/35057050
  • 10.1146/annurev-med-100708-204735
  • 10.1101/gr.136127.111
  • 10.1093/hmg/ddh073
  • 10.1186/1471-2164-10-s1-s6
  • 10.1101/gr.160325.113
  • 10.1126/science.1062938
  • 10.1038/sj.embor.7400195
  • 10.1016/j.ajhg.2010.03.003
  • 10.1038/nrg1578
  • 10.1016/j.ajhg.2008.09.017
  • 10.1093/nar/gky1056
  • 10.1093/nar/gku1205
  • 10.1093/nar/gky1049
  • 10.1093/nar/gkaa891
  • 10.1056/nejmsr1406261
  • 10.1038/s41588-019-0528-2
  • 10.1186/s13073-018-0531-8
  • 10.1093/bioinformatics/btv301
  • 10.1038/s41593-021-00862-0
  • 10.1126/scitranslmed.3009262
  • 10.1016/s0022-510x(02)00256-3
  • 10.1038/ng.3406
  • 10.1212/01.wnl.0000258561.02248.2f
  • 10.1016/j.ajhg.2009.09.003
  • 10.1371/journal.pone.0115692
  • 10.1093/bioinformatics/btx426
  • 10.1093/nar/gkr251
  • 10.1093/nar/gkz1021
  • 10.1093/nar/gky930
  • 10.1890/14-0471.1
  • 10.1093/bib/bbz083
  • 10.1016/j.parkreldis.2019.06.025
  • 10.1038/nrn2337
  • 10.1093/brain/124.11.2131
  • 10.1016/j.ijge.2013.03.005
  • 10.1002/ana.10481
  • 10.1016/j.parkreldis.2018.06.025
  • 10.1111/ene.13762
  • 10.1002/mds.870110206
  • 10.1016/j.parkreldis.2013.10.008
  • 10.1038/srep13360
  • 10.1002/mds.26672
  • 10.1016/j.tins.2017.04.001
  • 10.1002/ana.25863
  • 10.1016/j.neurobiolaging.2003.12.010
  • 10.1001/archneur.60.3.337
  • 10.1016/j.jns.2009.08.031
  • 10.1016/j.jns.2009.08.034
  • 10.1093/brain/awh402
  • 10.4236/nm.2015.62011
  • 10.1136/jnnp.2004.056614
  • 10.1002/mds.23052
  • 10.1016/j.ejpn.2017.11.009
  • 10.1007/s11910-017-0735-0
  • 10.1007/s00415-012-6592-5
  • 10.1101/cshperspect.a022426
  • 10.1038/s41588-018-0067-2
  • 10.1111/j.1528-1167.2012.03622.x
  • 10.1017/s0012162200001419
  • 10.1111/epi.16444
  • 10.1038/s41598-019-50627-w
  • 10.1016/j.braindev.2009.03.005
  • 10.1016/j.yebeh.2015.03.017
  • 10.1016/s1474-4422(02)00160-6