Implications of pleiotropy: challenges and opportunities for mining Big Data in biomedicine.
Implications of pleiotropy: challenges and opportunities for mining Big Data in biomedicine.
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DOI：
10.3389/fgene.2015.00229
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发表时间：
2015
影响因子：
3.7
通讯作者：
Zhao H
中科院分区：
文献类型：
--
作者：
Yang C;Li C;Wang Q;Chung D;Zhao H
当一个基因座影响多个性状时，多效性就出现了。在过去的十年里，丰富的GWAS发现的各种性状揭示了许多这种现象的例子，表明广泛存在的多效性效应。这种现象的基础是看似无关的特征/疾病之间的生物学联系。表征多效性的分子机制不仅有助于解释疾病之间的关系，而且还有助于对每种特定疾病的病理机制提出新的见解，从而更好地预防，诊断和治疗疾病。然而，大多数多效性效应仍然难以捉摸，因为它们的功能作用尚未得到系统的研究。系统的调查需要在多层生物过程（例如，转录和翻译）。大数据在生物医学中的兴起，如高质量的多组学数据，生物医学成像数据和患者的电子病历，为我们研究多效性提供了前所未有的机会。在生物医学中，将非常需要计算效率和统计严格的方法来综合分析这些大数据。在这篇综述中，我们概述了系统分析多效性的方法学发展中的许多机遇和挑战，并强调了其对疾病预防，诊断和治疗的意义。
Pleiotropy arises when a locus influences multiple traits. Rich GWAS findings of various traits in the past decade reveal many examples of this phenomenon, suggesting the wide existence of pleiotropic effects. What underlies this phenomenon is the biological connection among seemingly unrelated traits/diseases. Characterizing the molecular mechanisms of pleiotropy not only helps to explain the relationship between diseases, but may also contribute to novel insights concerning the pathological mechanism of each specific disease, leading to better disease prevention, diagnosis and treatment. However, most pleiotropic effects remain elusive because their functional roles have not been systematically examined. A systematic investigation requires availability of qualified measurements at multilayered biological processes (e.g., transcription and translation). The rise of Big Data in biomedicine, such as high-quality multi-omics data, biomedical imaging data and electronic medical records of patients, offers us an unprecedented opportunity to investigate pleiotropy. There will be a great need of computationally efficient and statistically rigorous methods for integrative analysis of these Big Data in biomedicine. In this review, we outline many opportunities and challenges in methodology developments for systematic analysis of pleiotropy, and highlight its implications on disease prevention, diagnosis and treatment.