Noto Davide, Gagliardo Carola Maria, Spina Rossella, Giammanco Antonina, Ciaccio Marcello, Cefalù Angelo B, Averna Maurizio
Department of Health Promotion, Maternal and Child Care, Internal Medicine and Medical Specialities "G. D'Alessandro" (PROMISE), University of Palermo, Via del Vespro 129, Palermo, 90127, Italy.
Department of Biomedicine, Neuroscience and Advanced Diagnostics (BIND), Section of Clinical Biochemistry, Clinical Molecular Medicine and Laboratory Medicine, University of Palermo, Palermo, Italy.
Mol Genet Genomics. 2024 Dec 20;300(1):4. doi: 10.1007/s00438-024-02202-w.
This paper describes a novel methodology based on GWAS filtering, aimed to find novel phenotypes associated to genetic loci independently of canonical risk factors using the large database of UK Biobank. Genome wide association studies (GWAS) is an untargeted methodology able to identify novel gene variants associated with diseases. Novel gene-phenotype associations might be discovered by this method. UKBiobank was interrogated by an automated routine to search associations between hundreds of phenotypes and single nucleotide polymorphisms (SNPs) resulting from GWAS, using Cardiovascular Disease as investigated trait. Six gene variants associated with CVD, independently of canonical risk factors, were identified using a variants database of more than 400k genotyped subjects (rs9349379 PHACTR1;intragenic_variant, rs74617384 LPA; intron_variant, rs4977574 CDKN2B-AS1;intron_variant, rs11191846 STN1;intron_variant, rs3184504, SH2B3;missense_variant, rs2929155 ADAMTS7;synonymous_variant). Novel clinical and biochemical phenotypes have been associated to the variants. The phenotypical characterization of the loci helped to propose mechanistic links that could explain their connection to CVD.
本文描述了一种基于全基因组关联研究（GWAS）筛选的新方法，旨在利用英国生物银行的大型数据库，找到与遗传位点相关的新表型，且不依赖于典型风险因素。全基因组关联研究（GWAS）是一种非靶向方法，能够识别与疾病相关的新基因变异。通过这种方法可能发现新的基因-表型关联。我们采用自动化程序对英国生物银行进行查询，以搜索数百种表型与GWAS产生的单核苷酸多态性（SNP）之间的关联，将心血管疾病作为研究性状。使用一个包含超过40万名基因分型受试者的变异数据库，我们识别出了6种与心血管疾病相关的基因变异，且不依赖于典型风险因素（rs9349379 PHACTR1；基因内变异，rs74617384 LPA；内含子变异，rs4977574 CDKN2B-AS1；内含子变异，rs11191846 STN1；内含子变异，rs3184504，SH2B3；错义变异，rs2929155 ADAMTS7；同义变异）。新的临床和生化表型已与这些变异相关联。对这些位点的表型特征分析有助于提出可能解释它们与心血管疾病联系的机制性联系。
