Bailliard F, Anderson RH. Tetralogy of Fallot. Orphanet J Rare Dis. 2009;4:2.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Google Scholar
Gene Ontology C. The Gene Ontology resource: enriching a gold mine. Nucleic Acids Res. 2021;49:D325–D34.
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.
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.
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.
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.
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.
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.
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.
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.
Google Scholar
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