Evidence has reported on the crucial role of (poly)phenols in the prevention of chronic diseases. However, their bioavailability greatly differs across individuals, causing uneven physiological responses. Here, we present the first genome-wide association studies (GWAS) on main dietary (poly)phenols-related traits to gain genetic insights on inter-individual differences and to unravel their potential pathophysiological impact. A total of 300 healthy individuals were recruited and biological samples, anthropometric measurements, health status and lifestyle information were collected. Urinary phenolic metabolites were used to identify clusters of individuals sharing similar metabolic phenotypes, as computed by principal components (PC) and sparse k-means algorithm (SKM). Moreover, individuals were divided by enterolactone metabolic capacity (EL), as an example for a given family of (poly)phenols. After genotyping and quality controls, 287 individuals and 511908 markers were retained (median age: 38 years old; 57% female). To empower genetic data, samples underwent genotype imputation on the Michigan Imputation Server, obtaining a total of 6022639 variants. The resulting clusters served as phenotypes (PC, SKM and EL) to perform the GWAS using PLINK2. The GWAS pointed out about 1500 significant variants for each trait. In silico functional analyses were performed using FUMAGWAS: variants were annotated by SNP2GENE, and the role of the associated genes was assessed by GENE2FUNC. Genes were first correlated with expression data from the GTEx catalog: differential expression was observed in the brain, the blood, and the colon. Moreover, pathways were investigated: PC showed associations with glucuronidation and different metabolic pathways; SKM with the neuronal system; EL with the ephrin signaling. Associations with miRNAs were also identified in all the traits. Finally, correlations with the GWAS catalog were investigated: significant hits were observed in the gut microbiome for all the traits; other hits were observed for cardiovascular diseases (PC), metabolites (SKM), and obesity and insulin level (EL).
Dissecting the genetics of (poly)phenol metabolism and bioavailability: insights from GWAS and in silico functional analyses / Treccani, Mirko; Mignogna, Cristiana; Rinaldi De Alvarenga, Jose' Fernando; Favari, Claudia; Bragazzi, Nicola; Agullo, Vicente; Morandini, Maria Sole; Rosi, Alice; Bresciani, Letizia; Dei Cas, Alessandra; Bonadonna, Riccardo; Brighenti, Furio; Del Rio, Daniele; Malerba, Giovanni; Barili, Valeria; Martorana, Davide; Mena, Pedro. - (2024). (Intervento presentato al convegno 11th International Congress on Polyphenols & Health tenutosi a Boston, MA nel 16-19/10/2024).
Dissecting the genetics of (poly)phenol metabolism and bioavailability: insights from GWAS and in silico functional analyses
Mirko Treccani;Cristiana Mignogna;Jose Fernando Rinaldi de Alvarenga;Claudia Favari;Nicola Bragazzi;Vicente Agullo;Maria Sole Morandini;Alice Rosi;Letizia Bresciani;Alessandra Dei Cas;Riccardo Bonadonna;Furio Brighenti;Daniele Del Rio;Valeria Barili;Davide Martorana;Pedro Mena
2024-01-01
Abstract
Evidence has reported on the crucial role of (poly)phenols in the prevention of chronic diseases. However, their bioavailability greatly differs across individuals, causing uneven physiological responses. Here, we present the first genome-wide association studies (GWAS) on main dietary (poly)phenols-related traits to gain genetic insights on inter-individual differences and to unravel their potential pathophysiological impact. A total of 300 healthy individuals were recruited and biological samples, anthropometric measurements, health status and lifestyle information were collected. Urinary phenolic metabolites were used to identify clusters of individuals sharing similar metabolic phenotypes, as computed by principal components (PC) and sparse k-means algorithm (SKM). Moreover, individuals were divided by enterolactone metabolic capacity (EL), as an example for a given family of (poly)phenols. After genotyping and quality controls, 287 individuals and 511908 markers were retained (median age: 38 years old; 57% female). To empower genetic data, samples underwent genotype imputation on the Michigan Imputation Server, obtaining a total of 6022639 variants. The resulting clusters served as phenotypes (PC, SKM and EL) to perform the GWAS using PLINK2. The GWAS pointed out about 1500 significant variants for each trait. In silico functional analyses were performed using FUMAGWAS: variants were annotated by SNP2GENE, and the role of the associated genes was assessed by GENE2FUNC. Genes were first correlated with expression data from the GTEx catalog: differential expression was observed in the brain, the blood, and the colon. Moreover, pathways were investigated: PC showed associations with glucuronidation and different metabolic pathways; SKM with the neuronal system; EL with the ephrin signaling. Associations with miRNAs were also identified in all the traits. Finally, correlations with the GWAS catalog were investigated: significant hits were observed in the gut microbiome for all the traits; other hits were observed for cardiovascular diseases (PC), metabolites (SKM), and obesity and insulin level (EL).I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
