A twin analysis to estimate genetic and environmental factors contributing to variation in weighted gene co-expression network module eigengenes

IF 1.6 3区 医学 Q3 GENETICS & HEREDITY
Nathan A. Gillespie, Tyler R. Bell, Gentry C. Hearn, Jonathan L. Hess, Ming T. Tsuang, Michael J. Lyons, Carol E. Franz, William S. Kremen, Stephen J. Glatt
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Abstract

Multivariate network-based analytic methods such as weighted gene co-expression network analysis are frequently applied to human and animal gene-expression data to estimate the first principal component of a module, or module eigengene (ME). MEs are interpreted as multivariate summaries of correlated gene-expression patterns and network connectivity across genes within a module. As such, they have the potential to elucidate the mechanisms by which molecular genomic variation contributes to individual differences in complex traits. Although increasingly used to test for associations between modules and complex traits, the genetic and environmental etiology of MEs has not been empirically established. It is unclear if, and to what degree, individual differences in blood-derived MEs reflect random variation versus familial aggregation arising from heritable or shared environmental influences. We used biometrical genetic analyses to estimate the contribution of genetic and environmental influences on MEs derived from blood lymphocytes collected on a sample of N = 661 older male twins from the Vietnam Era Twin Study of Aging (VETSA) whose mean age at assessment was 67.7 years (SD = 2.6 years, range = 62–74 years). Of the 26 detected MEs, 14 (56%) had statistically significant additive genetic variation with an average heritability of 44% (SD = 0.08, range = 35%–64%). Despite the relatively small sample size, this demonstration of significant family aggregation including estimates of heritability in 14 of the 26 MEs suggests that blood-based MEs are reliable and merit further exploration in terms of their associations with complex traits and diseases.

Abstract Image

通过孪生子分析估算导致加权基因共表达网络模块eigengenes变异的遗传和环境因素。
基于多变量网络的分析方法(如加权基因共表达网络分析)经常被应用于人类和动物的基因表达数据,以估算模块的第一主成分或模块基因(ME)。模块主成分被解释为模块内各基因相关基因表达模式和网络连通性的多变量总结。因此,它们有可能阐明分子基因组变异导致复杂性状个体差异的机制。虽然越来越多的研究用于检验模块与复杂性状之间的关联,但MEs的遗传和环境病因尚未得到经验性的证实。目前还不清楚血源性 ME 的个体差异是反映随机变异,还是反映遗传或共同环境影响下的家族聚集,以及反映的程度如何。我们使用生物计量遗传分析来估算遗传和环境对血液淋巴细胞中 MEs 的影响,这些血液淋巴细胞来自越南老龄化双胞胎研究(VETSA)的 N = 661 个老年男性双胞胎样本,评估时的平均年龄为 67.7 岁(SD = 2.6 岁,范围 = 62-74 岁)。在检测到的26个ME中,14个(56%)具有统计学意义上显著的加性遗传变异,平均遗传率为44%(SD=0.08,范围=35%-64%)。尽管样本量相对较小,但26个ME中的14个具有明显的家族聚集性,包括估计的遗传率,这表明基于血液的ME是可靠的,值得进一步探讨它们与复杂性状和疾病的关联。
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来源期刊
CiteScore
5.90
自引率
7.10%
发文量
40
审稿时长
4-8 weeks
期刊介绍: Neuropsychiatric Genetics, Part B of the American Journal of Medical Genetics (AJMG) , provides a forum for experimental and clinical investigations of the genetic mechanisms underlying neurologic and psychiatric disorders. It is a resource for novel genetics studies of the heritable nature of psychiatric and other nervous system disorders, characterized at the molecular, cellular or behavior levels. Neuropsychiatric Genetics publishes eight times per year.
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