Estimating Modifying Effect of Age on Genetic and Environmental Variance Components in Twin Models
Estimating Modifying Effect of Age on Genetic and Environmental Variance Components in Twin Models
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DOI：
10.1534/genetics.115.183905
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发表时间：
2016-04-01
期刊：
影响因子：
3.3
通讯作者：
Pitkaniemi, Janne
中科院分区：
文献类型：
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作者：
He, Liang;Sillanpaa, Mikko J.;Pitkaniemi, Janne
几十年来，人们一直采用双胞胎研究来解开各种性状的相对遗传和环境贡献。然而，基于经典双生子模型的遗传力估计没有考虑方差分量随年龄的动态行为。随着年龄的变化，遗传成分的方差可能意味着存在基因-环境（G x E）的相互作用，一般的全基因组关联研究（GWAS）未能捕获，这可能导致双胞胎设计和GWAS之间的遗传力估计的不一致。现有的双胞胎研究的参数G × E相互作用模型是有限的，假设一个线性或二次形式的方差曲线相对于主持人，但是，在现实中受到过度限制。在这里，我们提出了基于样条的方法来探索遗传和环境成分的方差曲线。我们选择加性遗传，共同和独特的环境方差分量（ACE）模型作为出发点。我们对待的方差函数的B-样条或P-样条建模的年龄的分量方差。我们开发了一个经验贝叶斯方法来估计方差曲线及其置信带，并提供了一个R包供公众使用。我们的模拟表明，对于> 10，000个双胞胎对的数据集，所提出的方法可以准确地捕获组件方差的均方误差动态行为。使用所提出的方法作为一种替代和主要扩展的经典双胞胎模型，我们的分析与一个大规模的芬兰双胞胎数据集（19,510对同卵双胞胎和27,312对异卵双胞胎）发现，A，C，体质指数（BMI）的E、E分量在不同的年龄段有不同的变化，BMI的遗传度下降到50%左右中年以后。结果进一步表明，BMI的遗传力下降是由于独特环境方差的增加，这为BMI的年龄特异性遗传力和G × E交互作用提供了更多的证据。这些发现强调了所提出的模型在促进双胞胎研究中的根本重要性和意义，以调查特定于年龄和其他修改因素的遗传力。
Twin studies have been adopted for decades to disentangle the relative genetic and environmental contributions for a wide range of traits. However, heritability estimation based on the classical twin models does not take into account dynamic behavior of the variance components over age. Varying variance of the genetic component over age can imply the existence of gene-environment (G x E) interactions that general genome-wide association studies (GWAS) fail to capture, which may lead to the inconsistency of heritability estimates between twin design and GWAS. Existing parametric G x E interaction models for twin studies are limited by assuming a linear or quadratic form of the variance curves with respect to a moderator that can, however, be overly restricted in reality. Here we propose spline-based approaches to explore the variance curves of the genetic and environmental components. We choose the additive genetic, common, and unique environmental variance components (ACE) model as the starting point. We treat the component variances as variance functions with respect to age modeled by B-splines or P-splines. We develop an empirical Bayes method to estimate the variance curves together with their confidence bands and provide an R package for public use. Our simulations demonstrate that the proposed methods accurately capture dynamic behavior of the component variances in terms of mean square errors with a data set of >10,000 twin pairs. Using the proposed methods as an alternative and major extension to the classical twin models, our analyses with a large-scale Finnish twin data set (19,510 MZ twins and 27,312 DZ same-sex twins) discover that the variances of the A, C, and E components for body mass index (BMI) change substantially across life span in different patterns and the heritability of BMI drops to approximate to 50% after middle age. The results further indicate that the decline of heritability is due to increasing unique environmental variance, which provides more insights into age-specific heritability of BMI and evidence of G x E interactions. These findings highlight the fundamental importance and implication of the proposed models in facilitating twin studies to investigate the heritability specific to age and other modifying factors.