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亲属间相关性的表观遗传学贡献。

Epigenetic contribution to covariance between relatives.

机构信息

The Cohn Institute for the History and Philosophy of Science and Ideas, Tel Aviv University, Tel Aviv, Israel.

出版信息

Genetics. 2010 Apr;184(4):1037-50. doi: 10.1534/genetics.109.112466. Epub 2010 Jan 25.

Abstract

Recent research has pointed to the ubiquity and abundance of between-generation epigenetic inheritance. This research has implications for assessing disease risk and the responses to ecological stresses and also for understanding evolutionary dynamics. An important step toward a general evaluation of these implications is the identification and estimation of the amount of heritable, epigenetic variation in populations. While methods for modeling the phenotypic heritable variance contributed by culture have already been developed, there are no comparable methods for nonbehavioral epigenetic inheritance systems. By introducing a model that takes epigenetic transmissibility (the probability of transmission of ancestral phenotypes) and environmental induction into account, we provide novel expressions for covariances between relatives. We have combined a classical quantitative genetics approach with information about the number of opportunities for epigenetic reset between generations and assumptions about environmental induction to estimate the heritable epigenetic variance and epigenetic transmissibility for both asexual and sexual populations. This assists us in the identification of phenotypes and populations in which epigenetic transmission occurs and enables a preliminary quantification of their transmissibility, which could then be followed by genomewide association and QTL studies.

摘要

最近的研究指出了代际间表观遗传遗传的普遍性和丰富性。这项研究对评估疾病风险和对生态压力的反应,以及对理解进化动态都具有重要意义。对这些影响进行全面评估的一个重要步骤是确定和估计群体中可遗传的、表观遗传变异的数量。虽然已经开发出用于模拟由文化引起的表型遗传方差的方法,但对于非行为性表观遗传遗传系统,尚无可比的方法。通过引入一个考虑到表观遗传可传递性(祖先表型传递的概率)和环境诱导的模型,我们为亲属之间的协方差提供了新的表达式。我们将经典的数量遗传学方法与关于代际间表观遗传重置机会的数量的信息以及关于环境诱导的假设相结合,以估计无性和有性种群的可遗传表观遗传变异和表观遗传可传递性。这有助于我们识别发生表观遗传传递的表型和群体,并对其可传递性进行初步量化,随后可以进行全基因组关联和 QTL 研究。

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