Descripción del proyecto
La genética ayuda a comprender mejor la arteriopatía coronaria
Los científicos desarrollan estudios de asociación de genoma completo (GWAS, por sus siglas en inglés) para identificar qué genes participan en la enfermedad humana y para predecir la presencia de enfermedades. Durante los últimos años, los GWAS han permitido descubrir centenares de polimorfismos de nucleótido único (SNP, por sus siglas en inglés), el tipo de variación genética más común entre las personas, que está estrechamente relacionado con la arteriopatía coronaria. Sin embargo, dichos SNP normalmente solo describen unos pocos rasgos hereditarios. A fin de comprender mejor los mecanismos de la enfermedad, el proyecto EnDeCAD, financiado con fondos europeos, explorará el papel de cada locus de arteriopatía coronaria a nivel molecular. El conocimiento obtenido debería mejorar la predicción del riesgo, la identificación de biomarcadores y la selección del tratamiento en la prestación de servicios de salud.
Objetivo
In recent years, genome-wide association studies (GWAS) have discovered hundreds of single nucleotide polymorphisms (SNPs) which are significantly associated with coronary artery disease (CAD). However, the SNPs identified by GWAS explain typically only small portion of the trait heritability and vast majority of variants do not have known biological roles. This is explained by variants lying within noncoding regions such as in cell type specific enhancers and additionally ‘the lead SNP’ identified in GWAS may not be the ‘the causal SNP’ but only linked with a trait associated SNP. Therefore, a major priority for understanding disease mechanisms is to understand at the molecular level the function of each CAD loci. In this study we aim to bring the functional characterization of SNPs associated with CAD risk to date by focusing our search for causal SNPs to enhancers of disease relevant cell types, namely endothelial cells, macrophages and smooth muscle cells of the vessel wall, hepatocytes and adipocytes. By combination of massively parallel enhancer activity measurements, collection of novel eQTL data throughout cell types under disease relevant stimuli, identification of the target genes in physical interaction with the candidate enhancers and establishment of correlative relationships between enhancer activity and gene expression we hope to identify causal enhancer variants and link them with target genes to obtain a more complete picture of the gene regulatory events driving disease progression and the genetic basis of CAD. Linking these findings with our deep phenotypic data for cardiovascular risk factors, gene expression and metabolomics has the potential to improve risk prediction, biomarker identification and treatment selection in clinical practice. Ultimately, this research strives for fundamental discoveries and breakthrough that advance our knowledge of CAD and provides pioneering steps towards taking the growing array of GWAS for translatable results.
Ámbito científico
Palabras clave
Programa(s)
Régimen de financiación
ERC-STG - Starting GrantInstitución de acogida
70211 KUOPIO
Finlandia