| Title: | Six genetic variants are associated with cardiovascular disease independently from canonical risk factors: a new method to refine GWAS results based on the UKBiobank phenotype database |
| Journal: | Molecular Genetics and Genomics |
| Published: | 20 Dec 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39704901/ |
| DOI: | https://doi.org/10.1007/s00438-024-02202-w |
Publication 14773
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Abstract
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.</p>
12 Keywords
- Adaptor Proteins, Signal Transducing
- Biological Specimen Banks
- Cardiovascular Diseases
- Databases, Genetic
- Genetic Predisposition to Disease
- Genome-Wide Association Study
- Humans
- Microfilament Proteins
- Phenotype
- Polymorphism, Single Nucleotide
- Risk Factors
- United Kingdom
7 Authors
- Davide Noto
- Carola Maria Gagliardo
- Rossella Spina
- Antonina Giammanco
- Marcello Ciaccio
- Angelo B. Cefalù
- Maurizio Averna
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