Analysis of heterogeneity and epistasis in physiological mixed populations by combined structural equation modelling and latent class analysis.
Analysis of heterogeneity and epistasis in physiological mixed populations by combined structural equation modelling and latent class analysis.
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通过结合结构方程建模和潜在类别分析分析生理混合种群中异质性和上毒的分析。
DOI：
10.1186/1471-2156-9-43
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发表时间：
2008-07-08
期刊：
影响因子：
2.9
通讯作者：
Jorgensen, Torben
中科院分区：
文献类型：
--
作者：
Fenger, Mogens;Linneberg, Allan;Werge, Thomas;Jorgensen, Torben
生物系统是相互作用的，其中遗传变异有助于表型异质性的分子网络。这种异质性传统上被建模为二分类特征（例如，受影响与未受影响）。考虑到这种网络的复杂性和遗传变异，这未免过于简单化了。在这项关于2型糖尿病的研究中，利用不同生理过程的表型指标，结合结构方程模型，在潜在类别框架中解决了异质性。我们模拟了临床条件“代谢综合征”，这是一种已知的具有临床终点（2型糖尿病）的异质性和多基因条件。在这里提出的模型中，不包括遗传因素，除了基因在网络中运作之外，没有假设任何遗传模型。通过对先前与代谢综合征和2型糖尿病相关的几个基因的分析，证明了研究人群分层对遗传相互作用的影响。分析显示，在一个大型健康研究人群中，存在19个不同的亚群，它们具有不同的糖尿病发病倾向。将受试者分配到亚群体的熵值接近0.9，非常准确。尽管在整个研究样本中，很少有基因变异与代谢综合征直接相关，但几乎三分之一的可能的上位性相互作用是高度显著的。特别是，在研究群体分层后，相互作用的数量增加，这表明相互作用在异质群体中被掩盖。此外，在亚种群中分析时，遗传变异平均增加了35倍。本研究的主要结论是，当在生理同质亚群中进行研究并将上位性纳入分析时，发现遗传变异和复杂性状之间真正关联的可能性大大增加，而上位性（即遗传网络）无处不在，应该是建模任何生物过程的基础。
Biological systems are interacting, molecular networks in which genetic variation contributes to phenotypic heterogeneity. This heterogeneity is traditionally modelled as a dichotomous trait (e.g. affected vs. non-affected). This is far too simplistic considering the complexity and genetic variations of such networks. In this study on type 2 diabetes mellitus, heterogeneity was resolved in a latent class framework combined with structural equation modelling using phenotypic indicators of distinct physiological processes. We modelled the clinical condition "the metabolic syndrome", which is known to be a heterogeneous and polygenic condition with a clinical endpoint (type 2 diabetes mellitus). In the model presented here, genetic factors were not included and no genetic model is assumed except that genes operate in networks. The impact of stratification of the study population on genetic interaction was demonstrated by analysis of several genes previously associated with the metabolic syndrome and type 2 diabetes mellitus. The analysis revealed the existence of 19 distinct subpopulations with a different propensity to develop diabetes mellitus within a large healthy study population. The allocation of subjects into subpopulations was highly accurate with an entropy measure of nearly 0.9. Although very few gene variants were directly associated with metabolic syndrome in the total study sample, almost one third of all possible epistatic interactions were highly significant. In particular, the number of interactions increased after stratifying the study population, suggesting that interactions are masked in heterogenous populations. In addition, the genetic variance increased by an average of 35-fold when analysed in the subpopulations. The major conclusions from this study are that the likelihood of detecting true association between genetic variants and complex traits increases tremendously when studied in physiological homogenous subpopulations and on inclusion of epistasis in the analysis, whereas epistasis (i.e. genetic networks) is ubiquitous and should be the basis in modelling any biological process.
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影响因子：
1.8
作者：
Conti, DV;Cortessis, V;Thomas, DC
通讯作者：
Thomas, DC
DOI：
10.1017/s001667230100502x
发表时间：
2001-08-01
期刊：
GENETICAL RESEARCH
影响因子：
--
作者：
Dawson, KJ;Belkhir, K
通讯作者：
Belkhir, K
影响因子：
4
作者：
Albert, R
通讯作者：
Albert, R
影响因子：
10.7
作者：
Bertorelle, G;Excoffier, L
通讯作者：
Excoffier, L
影响因子：
2.4
作者：
Dorogovtsev, SN;Goltsev, AV;Mendes, JFF
通讯作者：
Mendes, JFF