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Estimation of metabolic syndrome heritability in three large populations including full pedigree and genomic information.

Abstract：

: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 ([Formula: see text]: 0.167 and 0.254 in ARIC and Ogliastra). The remaining heritability between h2 and [Formula: see text] 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.

journal_name

Hum Genet

journal_title

Human genetics

authors

Graziano F,Biino G,Bonati MT,Neale BM,Do R,Concas MP,Vaccargiu S,Pirastu M,Terradura-Vagnarelli O,Cirillo M,Laurenzi M,Mancini M,Zanchetti A,Grassi M

doi

10.1007/s00439-019-02024-6

subject

Has Abstract

pub_date

2019-07-01 00:00:00

pages

739-748

issue

eissn

0340-6717

issn

1432-1203

pii

10.1007/s00439-019-02024-6

journal_volume

138

pub_type

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Estimation of metabolic syndrome heritability in three large populations including full pedigree and genomic information.