Center of Biostatistics for Clinical Epidemiology, School of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy.
Medical and Genomic Statistics Unit, Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy.
Hum Genet. 2019 Jul;138(7):739-748. doi: 10.1007/s00439-019-02024-6. Epub 2019 Jun 1.
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 (h) and common sib-household effect (c). Globally, results obtained from pedigree information showed a significant heritability (h: 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 h and [Formula: see text] ranged between 0.031 and 0.237. Finally, the common environmental c 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.
代谢综合征是一种复杂的人类疾病，其特征是一组病症（血压升高、血糖升高、腰部周围脂肪过多以及胆固醇或甘油三酯水平异常）。这些病症中的任何一种都会增加患严重疾病（如糖尿病或心血管疾病）的风险。目前，该综合征的遗传调控程度存在争议且部分未知。本研究的主要目的是使用完整的系谱和基因组信息来估计不同人群的遗传成分和常见环境效应。我们使用三个大型人群（Gubbio、ARIC 和 Ogliastra 队列）来估计代谢综合征的遗传性。由于既有系谱又有基因分型数据，因此应用了不同的方法来总结相关条件。使用平均信息约束极大似然法（AIREML）算法的线性混合模型（LLM）被用于划分方差并估计遗传性（h）和常见同胞家庭效应（c）。总体而言，从系谱信息中获得的结果显示出显著的遗传性（h：在 Gubbio 和 Ogliastra 中分别为 0.286 和 0.271），而使用 SNPs 数据则发现了较低但仍显著的遗传性（[Formula: see text]：在 ARIC 和 Ogliastra 中分别为 0.167 和 0.254）。h 和 [Formula: see text] 之间的剩余遗传性在 0.031 到 0.237 之间。最后，Gubbio 和 Ogliastra 中的共同环境 c 也具有显著意义，占表型方差的约 11%。不同人群和数据的可用性帮助我们更好地理解在估计代谢综合征的遗传性时会发生什么，并解释了不同的可能混杂因素。此外，比较不同结果的机会提供了更精确且偏差更小的遗传性估计。
