Hierarchical clustering analysis of blood plasma lipidomics profiles from mono- and dizygotic twin families
Hierarchical clustering analysis of blood plasma lipidomics profiles from mono- and dizygotic twin families
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DOI：
10.1038/ejhg.2012.110
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发表时间：
2013-01-01
影响因子：
5.2
通讯作者：
Boomsma, Dorret I.
中科院分区：
文献类型：
--
作者：
Draisma, Harmen H. M.;Reijmers, Theo H.;Boomsma, Dorret I.
双生子和家系研究通常用于阐明遗传和环境变异对表型变异的相对贡献。在这里，我们应用定量遗传学方法的基础上分层聚类，血浆脂质组学数据中获得的健康队列组成的37个单卵和28个双卵双胞胎对，和52个他们的生物非双胞胎兄弟姐妹。这些数据提供了所研究的血液样品中各种脂质浓度的信息。层次聚类的一个重要优点是它可以应用于高维的“组学”类型的数据，而使用许多其他定量遗传方法来分析此类数据受到大量相关变量的阻碍。在这项研究中，我们结合了两个脂质组学数据集，来自两个不同的测量块，我们通过“分位数相等”校正了块效应。在合并数据的分析中，脂质组学谱的平均相似性在单卵（MZ）cotwin之间最高，在双卵（DZ）cotwin之间、在性别匹配的非双胞胎兄弟姐妹之间和在性别匹配的无关参与者之间分别逐渐降低。我们的研究结果表明：（1）共享的遗传背景，共享的环境，相似的年龄有助于个体之间血浆脂质组学谱的相似性;（2）通过分位数相等和组合不同测量块中获得的数据集，定量遗传分析的能力得到增强。European Journal of Human Genetics（2013）21，95-101; doi：10.1038/ejhg.2012.110; 2012年6月20日在线发表
Twin and family studies are typically used to elucidate the relative contribution of genetic and environmental variation to phenotypic variation. Here, we apply a quantitative genetic method based on hierarchical clustering, to blood plasma lipidomics data obtained in a healthy cohort consisting of 37 monozygotic and 28 dizygotic twin pairs, and 52 of their biological nontwin siblings. Such data are informative of the concentrations of a wide range of lipids in the studied blood samples. An important advantage of hierarchical clustering is that it can be applied to a high-dimensional 'omics' type data, whereas the use of many other quantitative genetic methods for analysis of such data is hampered by the large number of correlated variables. For this study we combined two lipidomics data sets, originating from two different measurement blocks, which we corrected for block effects by 'quantile equating'. In the analysis of the combined data, average similarities of lipidomics profiles were highest between monozygotic (MZ) cotwins, and became progressively lower between dizygotic (DZ) cotwins, among sex-matched nontwin siblings and among sex-matched unrelated participants, respectively. Our results suggest that (1) shared genetic background, shared environment, and similar age contribute to similarities in blood plasma lipidomics profiles among individuals; and (2) that the power of quantitative genetic analyses is enhanced by quantile equating and combination of data sets obtained in different measurement blocks. European Journal of Human Genetics (2013) 21, 95-101; doi: 10.1038/ejhg.2012.110; published online 20 June 2012