表观遗传学背景预测基因表达变异和遗传相同个体的生殖特征。

Amy K Webster, John H Willis, Erik Johnson, Peter Sarkies, Patrick C Phillips
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摘要

近几十年来,全基因组关联研究(GWAS)一直是了解性状和疾病个体差异的生物学基础的主要方法。然而,GWAS方法已被证明在解释个体差异方面的预测能力有限,特别是对于环境因素在其病因中发挥重要作用的复杂性状和疾病。事实上,即使在基因相同的个体中,个体差异仍然存在,尽管在大多数生物体中,完全分离基因和环境的因果关系是困难或不可能的。为了了解在没有遗传差异的情况下个体差异的基础,我们在共享环境中测量了180种基因相同的年轻成年秀丽隐杆线虫的两个数量繁殖性状,并对每种蠕虫进行了单个转录组学研究。我们鉴定了数百个基因,其中表达变异与生殖特征密切相关,其中一些基因依赖于先前的环境经验,另一些基因是随机的。多个小基因组合在一起对个体的生殖特征具有高度预测性,平均解释了这两个特征超过一半到四分之一的变异。我们使用RNA干扰来识别一组因果基因,从而操纵预测基因的mRNA水平,证明了这种方法在预测和理解潜在生物学方面的实用性。最后,我们发现预测基因的染色质环境富集了H3K27三甲基化,这表明关键性状背后的个体基因表达差异可能部分由染色质结构驱动。总之,这项工作表明,独立于潜在遗传差异而产生的基因表达的个体差异,在形成等于或超过遗传变异水平的生殖特征方面,既是预测性的,也是因果性的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Gene expression variation across genetically identical individuals predicts reproductive traits.

In recent decades, genome-wide association studies (GWAS) have been the major approach to understand the biological basis of individual differences in traits and diseases. However, GWAS approaches have limited predictive power to explain individual differences, particularly for complex traits and diseases in which environmental factors play a substantial role in their etiology. Indeed, individual differences persist even in genetically identical individuals, although fully separating genetic and environmental causation is difficult in most organisms. To understand the basis of individual differences in the absence of genetic differences, we measured two quantitative reproductive traits in 180 genetically identical young adult Caenorhabditis elegans roundworms in a shared environment and performed single-individual transcriptomics on each worm. We identified hundreds of genes for which expression variation was strongly associated with reproductive traits, some of which depended on individuals' historical environments and some of which was random. Multiple small sets of genes together were highly predictive of reproductive traits, explaining on average over half and over a quarter of variation in the two traits. We manipulated mRNA levels of predictive genes to identify a set of causal genes, demonstrating the utility of this approach for both prediction and understanding underlying biology. Finally, we found that the chromatin environment of predictive genes was enriched for H3K27 trimethylation, suggesting that gene expression variation may be driven in part by chromatin structure. Together, this work shows that individual, non-genetic differences in gene expression are both highly predictive and causal in shaping reproductive traits.

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