Joint Analysis of Functionally Linked Genes Yields Other Candidates Associated with Tetralogy of Fallot – Journal of Human Genetics

  • Bailliard F, Anderson RH. Tetralogy of Fallot. Orphanet J Rare Dis. 2009;4:2.

    Article

    Google Scholar

  • Jin SC, Homsy J, Zaidi S, Lu Q, Morton S, DePalma SR, et al. Contribution of rare hereditary and de novo variants in 2871 congenital heart disease probands. Nat Genet. 2017;49:1593–601.

    CASE
    Article

    Google Scholar

  • Pierpont ME, Brueckner M, Chung WK, Garg V, Lacro RV, McGuire AL, et al. Genetic basis of congenital heart disease: revisited: a scientific statement from the American Heart Association. Traffic. 2018;138:e653–e711.

    Article

    Google Scholar

  • Page DJ, Miossec MJ, Williams SG, Monaghan RM, Fotiou E, Cordell HJ, et al. Whole exome sequencing reveals major genetic contributors to nonsyndromic tetralogy of Fallot. Circ Res. 2019;124:553–63.

    CASE
    Article

    Google Scholar

  • Reuter MS, Chaturvedi RR, Jobling RK, Pellecchia G, Hamdan O, Sung WWL, et al. Clinical genetic risk variants inform a functional protein interaction network for tetralogy of Fallot. Circ Genom Precis Med. 2021;14:e003410.

    CASE
    Article

    Google Scholar

  • Skoric-Milosavljevic D, Lahrouchi N, Bosada FM, Dombrowsky G, Williams SG, Lesurf R, et al. Rare KDR variants, encoding VEGF receptor 2, are associated with tetralogy of Fallot. Genet Med. 2021;23:1952–60.

  • Sham PC, Purcell SM. Statistical power and significance tests in large-scale genetic studies. Nat Rev Genet. 2014;15:335–46.

    CASE
    Article

    Google Scholar

  • DMH forceps, Hernandez RD. Genetic simulation study of power populations in association testing through genetic architectures and study designs. Genet Epidemiol. 2020;44:90–103.

    Article

    Google Scholar

  • Aibar S, Fontanillo C, Droste C, De Las Rivas J. Functional gene networks: R/Bioc package to generate and analyze gene networks derived from functional enrichment and clustering. Bioinformatics. 2015;31:1686–8.

    CASE
    Article

    Google Scholar

  • Ghiassian SD, Menche J, Barabasi AL. A DIeAse MOdule Detection (DIAMonD) algorithm derived from a systematic analysis of disease protein connectivity patterns in the human interactome. PLoS Comput Biol. 2015;11:e1004120.

    Article

    Google Scholar

  • Sun PG, Gao L, Han S. Prediction of human disease-related gene clusters by cluster analysis. Int J Biol Sci. 2011;7:61–73.

    Article

    Google Scholar

  • Siitonen A, Kytovuori L, Nalls MA, Gibbs R, Hernandez DG, Ylikotila P, et al. Finnish Parkinson’s disease study integrating protein-protein interaction network data with exome sequencing analysis. Sci Rep. 2019;9:18865.

    CASE
    Article

    Google Scholar

  • Smedley D, Kohler S, Czeschik JC, Amberger J, Bocchini C, Hamosh A, et al. Walking the interactome for candidate prioritization in exome sequencing studies of Mendelian diseases. Bioinformatics. 2014;30:3215–22.

    CASE
    Article

    Google Scholar

  • Yepes S, Tucker MA, Koka H, ​​Xiao Y, Jones K, Vogt A, et al. Using whole-exome sequencing networks and protein interactions to prioritize candidate genes for germline susceptibility to cutaneous melanoma. Sci Rep. 2020;10:17198.

    CASE
    Article

    Google Scholar

  • Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for unifying biology. The Gene Ontology Consortium. Nat Genet. 2000;25:25–9.

    CASE
    Article

    Google Scholar

  • Gene Ontology C. The Gene Ontology resource: enriching a gold mine. Nucleic Acids Res. 2021;49:D325–D34.

    Article

    Google Scholar

  • Stark C, Breitkreutz BJ, Reguly T, Boucher L, Breitkreutz A, Tyers M. BioGRID: A general repository for interaction datasets. Nucleic Acids Res. 2006;34:D535–9.

    CASE
    Article

    Google Scholar

  • Oughtred R, Rust J, Chang C, Breitkreutz BJ, Stark C, Willems A, et al. The BioGRID database: a comprehensive biomedical resource of organized protein, genetic and chemical interactions. Sci protein. 2021;30:187–200.

    CASE
    Article

    Google Scholar

  • Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature. 2016;536:285–91.

    CASE
    Article

    Google Scholar

  • Li Y, Klena NT, Gabriel GC, Liu X, Kim AJ, Lemke K, et al. Global genetic analysis in mice reveals the central role of cilia in congenital heart disease. Nature. 2015;521:520–4.

    CASE
    Article

    Google Scholar

  • Alby C, Piquand K, Huber C, Megarbane A, Ichkou A, Legendre M, et al. Mutations in KIAA0586 cause life-threatening ciliopathies ranging from a hydrolethal phenotype to polydactyly of the short ribs syndrome. Am J Hum Genet. 2015;97:311–8.

    CASE
    Article

    Google Scholar

  • Morton SU, Shimamura A, Newburger PE, Opotowsky AR, Quiat D, Pereira AC, et al. Association of damaging variants in genes with increased cancer risk in patients with congenital heart disease. JAMA Cardiol. 2021;6:457–62.

    Article

    Google Scholar

  • Li D, Parks SB, Kushner JD, Nauman D, Burgess D, Ludwigsen S, et al. Presenilin gene mutations in dilated cardiomyopathy and heart failure. Am J Hum Genet. 2006;79:1030–9.

    CASE
    Article

    Google Scholar

  • Ma X, Bacci S, Mlynarski W, Gottardo L, Soccio T, Menzaghi C, et al. A common haplotype at the CD36 locus is associated with elevated levels of free fatty acids and increased cardiovascular risk in Caucasians. Hum Mol Genet. 2004;13:2197–205.

    CASE
    Article

    Google Scholar

  • #Joint #Analysis #Functionally #Linked #Genes #Yields #Candidates #Tetralogy #Fallot #Journal #Human #Genetics

    Leave a Comment

    Your email address will not be published. Required fields are marked *