Joint analysis of functionally related genes yields further candidates associated with Tetralogy of Fallot

  • 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 inherited and de novo variants in 2,871 congenital heart disease probands. Nat Genet. 2017;49:1593–601.

    CAS 
    Article 

    Google Scholar
     

  • Pierpont ME, Brueckner M, Chung WK, Garg V, Lacro RV, McGuire AL, et al. Genetic basis for congenital heart disease: revisited: a scientific statement from the American Heart Association. Circulation. 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 the major genetic contributors to nonsyndromic Tetralogy of Fallot. Circ Res. 2019;124:553–63.

    CAS 
    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.

    CAS 
    Article 

    Google Scholar
     

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

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

    CAS 
    Article 

    Google Scholar
     

  • Tong DMH, Hernandez RD. Population genetic simulation study of power in association testing across 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 analyse gene networks derived from functional enrichment and clustering. Bioinformatics. 2015;31:1686–8.

    CAS 
    Article 

    Google Scholar
     

  • Ghiassian SD, Menche J, Barabasi AL. A DIseAse MOdule Detection (DIAMOnD) algorithm derived from a systematic analysis of connectivity patterns of disease proteins 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 clustering 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.

    CAS 
    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.

    CAS 
    Article 

    Google Scholar
     

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

    CAS 
    Article 

    Google Scholar
     

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

    CAS 
    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.

    CAS 
    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 curated protein, genetic, and chemical interactions. Protein Sci. 2021;30:187–200.

    CAS 
    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.

    CAS 
    Article 

    Google Scholar
     

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

    CAS 
    Article 

    Google Scholar
     

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

    CAS 
    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 among 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. Mutations of presenilin genes in dilated cardiomyopathy and heart failure. Am J Hum Genet. 2006;79:1030–9.

    CAS 
    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 high free fatty acid levels and increased cardiovascular risk in Caucasians. Hum Mol Genet. 2004;13:2197–205.

    CAS 
    Article 

    Google Scholar
     

  • Read more here: Source link