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从基因型到表型:通过染色质。

From Genotype to Phenotype: Through Chromatin.

机构信息

Department of Global Public Health and Primary Care, University of Bergen, 5018, Bergen, Norway.

Computational Biology Unit, Department of Clinical Science, University of Bergen, 5021, Bergen, Norway.

出版信息

Genes (Basel). 2019 Jan 23;10(2):76. doi: 10.3390/genes10020076.

DOI:10.3390/genes10020076
PMID:30678090
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6410296/
Abstract

Advances in sequencing technologies have enabled the exploration of the genetic basis for several clinical disorders by allowing identification of causal mutations in rare genetic diseases. Sequencing technology has also facilitated genome-wide association studies to gather single nucleotide polymorphisms in common diseases including cancer and diabetes. Sequencing has therefore become common in the clinic for both prognostics and diagnostics. The success in follow-up steps, i.e., mapping mutations to causal genes and therapeutic targets to further the development of novel therapies, has nevertheless been very limited. This is because most mutations associated with diseases lie in inter-genic regions including the so-called regulatory genome. Additionally, no genetic causes are apparent for many diseases including neurodegenerative disorders. A complementary approach is therefore gaining interest, namely to focus on control of the disease to generate more complete functional genomic maps. To this end, several recent studies have generated large-scale epigenetic datasets in a disease context to form a link between genotype and phenotype. We focus DNA methylation and important histone marks, where recent advances have been made thanks to technology improvements, cost effectiveness, and large meta-scale epigenome consortia efforts. We summarize recent studies unravelling the mechanistic understanding of epigenetic processes in disease development and progression. Moreover, we show how methodology advancements enable causal relationships to be established, and we pinpoint the most important issues to be addressed by future research.

摘要

测序技术的进步使得通过鉴定罕见遗传病中的因果突变来探索几种临床疾病的遗传基础成为可能。测序技术还促进了全基因组关联研究,以收集癌症和糖尿病等常见疾病中的单核苷酸多态性。因此,测序技术在临床中已广泛用于预后和诊断。然而,后续步骤的成功,即将突变映射到因果基因和治疗靶点,以进一步开发新疗法,仍然非常有限。这是因为大多数与疾病相关的突变都位于基因间区域,包括所谓的调控基因组。此外,许多疾病包括神经退行性疾病都没有明显的遗传原因。因此,一种互补的方法越来越受到关注,即专注于疾病的控制,以生成更完整的功能基因组图谱。为此,最近的几项研究在疾病背景下生成了大规模的表观遗传数据集,以建立基因型和表型之间的联系。我们重点介绍 DNA 甲基化和重要的组蛋白标记,最近由于技术改进、成本效益和大型元尺度表观基因组联盟的努力,这些方面取得了进展。我们总结了最近的研究,揭示了表观遗传过程在疾病发展和进展中的机制理解。此外,我们展示了方法学的进步如何能够确定因果关系,并指出未来研究需要解决的最重要问题。

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Causal Transcription Regulatory Network Inference Using Enhancer Activity as a Causal Anchor.利用增强子活性作为因果锚点进行因果转录调控网络推断。
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Exploring the Genetic Predisposition to Epigenetic Changes in Alzheimer's Disease.探讨阿尔茨海默病中表观遗传学变化的遗传易感性。
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Using expression quantitative trait loci data and graph-embedded neural networks to uncover genotype-phenotype interactions.利用表达数量性状位点数据和图嵌入神经网络揭示基因型-表型相互作用。
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