Epigenome-wide association studies: current knowledge, strategies and recommendations.
Epigenome-wide association studies: current knowledge, strategies and recommendations.
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表观基因组关联研究：当前知识、策略和建议。
DOI：
10.1186/s13148-021-01200-8
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发表时间：
2021-12-04
影响因子：
5.7
通讯作者：
Lea RA
中科院分区：
文献类型：
--
作者：
Campagna MP;Xavier A;Lechner-Scott J;Maltby V;Scott RJ;Butzkueven H;Jokubaitis VG;Lea RA
复杂疾病的病因学和病理生理学是由遗传和环境因素之间的相互作用驱动的。这些疾病的风险和结果的变异性不完全由遗传或环境风险因素单独解释。因此，研究人员现在正在探索表观基因组，这是遗传学和环境可以相互作用的生物界面。越来越多的证据支持表观遗传机制在复杂疾病病理生理学中的作用。表观全基因组关联研究（EWASes）调查表型和表观遗传变异（最常见的是DNA甲基化）之间的关联。测量表观基因组范围甲基化的成本不断降低，生物信息学管道的可及性不断增加，这促成了近年来发表的EWASes的增加。在这里，我们回顾了这些EWASes目前的文献，并提供进一步的建议和策略，成功地进行。我们将我们的综述限制在使用甲基化数据的研究，因为这是研究最多的表观遗传机制;基于微阵列的数据，因为全基因组亚硫酸氢盐测序对大多数实验室来说仍然非常昂贵;基于血液的研究，因为外周血采集的非侵入性和存档DNA的可用性，以及公开可用的基于血细胞的甲基化数据的可访问性。此外，我们解决了EWAS分析的多个新领域，这些领域在以前的综述中没有涉及：（1）纵向研究设计，（2）芯片分析甲基化管道（ChAMP），（3）差异甲基化区域（DMR）鉴定范例，（4）甲基化数量性状基因座（methQTL）分析，（5）甲基化年龄分析和（6）使用统计去卷积从混合细胞数据鉴定细胞特异性差异甲基化。
The aetiology and pathophysiology of complex diseases are driven by the interaction between genetic and environmental factors. The variability in risk and outcomes in these diseases are incompletely explained by genetics or environmental risk factors individually. Therefore, researchers are now exploring the epigenome, a biological interface at which genetics and the environment can interact. There is a growing body of evidence supporting the role of epigenetic mechanisms in complex disease pathophysiology. Epigenome-wide association studies (EWASes) investigate the association between a phenotype and epigenetic variants, most commonly DNA methylation. The decreasing cost of measuring epigenome-wide methylation and the increasing accessibility of bioinformatic pipelines have contributed to the rise in EWASes published in recent years. Here, we review the current literature on these EWASes and provide further recommendations and strategies for successfully conducting them. We have constrained our review to studies using methylation data as this is the most studied epigenetic mechanism; microarray-based data as whole-genome bisulphite sequencing remains prohibitively expensive for most laboratories; and blood-based studies due to the non-invasiveness of peripheral blood collection and availability of archived DNA, as well as the accessibility of publicly available blood-cell-based methylation data. Further, we address multiple novel areas of EWAS analysis that have not been covered in previous reviews: (1) longitudinal study designs, (2) the chip analysis methylation pipeline (ChAMP), (3) differentially methylated region (DMR) identification paradigms, (4) methylation quantitative trait loci (methQTL) analysis, (5) methylation age analysis and (6) identifying cell-specific differential methylation from mixed cell data using statistical deconvolution.
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影响因子：
4.4
作者：
Bibikova, Marina;Barnes, Bret;Shen, Richard
通讯作者：
Shen, Richard
影响因子：
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作者：
Dedeurwaerder, Sarah;Defrance, Matthieu;Fuks, Francois
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Fuks, Francois
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Fuks F
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作者：
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通讯作者：
Zaitlen, Noah
影响因子：
5.7
作者：
Acevedo N;Reinius LE;Vitezic M;Fortino V;Söderhäll C;Honkanen H;Veijola R;Simell O;Toppari J;Ilonen J;Knip M;Scheynius A;Hyöty H;Greco D;Kere J
通讯作者：
Kere J