Estimation of metabolic syndrome heritability in three large populations including full pedigree and genomic information
| Autor: | Massimo Cirillo, Ron Do, Ginevra Biino, Mario Mancini, Alberto Zanchetti, Maria Pina Concas, Simona Vaccargiu, Francesca Graziano, Oscar Terradura-Vagnarelli, Mario Pirastu, Benjamin M. Neale, Martino Laurenzi, Mario Grassi, Maria Teresa Bonati |
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| Přispěvatelé: | Graziano, F, Biino, G, Bonati, M, Neale, B, Do, R, Concas, M, Vaccargiu, S, Pirastu, M, Terradura-Vagnarelli, O, Cirillo, M, Laurenzi, M, Mancini, M, Zanchetti, A, Grassi, M, Bonati, Mt, Neale, Bm, Concas, Mp, Grassi, M., Do, TUAN TU |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Male
Linear mixed model Genotype Restricted maximum likelihood Population Single-nucleotide polymorphism Genome-wide association study heritability Biology Polymorphism Single Nucleotide Generalized linear mixed model Cohort Studies 03 medical and health sciences Genetic Models Statistics Genetics Humans SNP Genetic Predisposition to Disease Polymorphism Genetics (clinical) 030304 developmental biology Metabolic Syndrome 0303 health sciences Genome Models Genetic Genome Human 030305 genetics & heredity Confounding Statistical genetic Genomics Single Nucleotide Heritability Female Genetics Population Genome-Wide Association Study Pedigree Statistical genetics Genomic Cohort Studie Human |
| Popis: | Metabolic syndrome is a complex human disorder characterized by a cluster of conditions (increased blood pressure, hyperglycemia, excessive body fat around the waist, and abnormal cholesterol or triglyceride levels). Any of these conditions increases the risk of serious disorders such as diabetes or cardiovascular disease. Currently, the degree of genetic regulation of this syndrome is under debate and partially unknown. The principal aim of this study was to estimate the genetic component and the common environmental effects in different populations using full pedigree and genomic information. We used three large populations (Gubbio, ARIC, and Ogliastra cohorts) to estimate the heritability of metabolic syndrome. Due to both pedigree and genotyped data, different approaches were applied to summarize relatedness conditions. Linear mixed models (LLM) using average information restricted maximum likelihood (AIREML) algorithm were applied to partition the variances and estimate heritability (h2) and common sib–household effect (c2). Globally, results obtained from pedigree information showed a significant heritability (h2: 0.286 and 0.271 in Gubbio and Ogliastra, respectively), whereas a lower, but still significant heritability was found using SNPs data ( $$h_{\text{SNP}}^{2}$$ : 0.167 and 0.254 in ARIC and Ogliastra). The remaining heritability between h2 and $$h_{\text{SNP}}^{2}$$ ranged between 0.031 and 0.237. Finally, the common environmental c2 in Gubbio and Ogliastra were also significant accounting for about 11% of the phenotypic variance. Availability of different kinds of populations and data helped us to better understand what happened when heritability of metabolic syndrome is estimated and account for different possible confounding. Furthermore, the opportunity of comparing different results provided more precise and less biased estimation of heritability. |
| Databáze: | OpenAIRE |
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