基因大数据的集成分析

胡湘红; 彭衡; 杨灿; 张纵辉; 万翔; 罗智泉

doi:10.11959/j.issn.2096-0271.2019033

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基因大数据的集成分析

研究 | 更新时间：2024-06-03

- 基因大数据的集成分析
- Integrative analysis for big data in genomics
- 大数据 2019年5卷第4期页码：67-88
- 作者机构：
  
  1. 深圳市大数据研究院，广东深圳 518172
  2. 香港浸会大学数学系，香港 999077
  3. 香港科技大学数学系，香港 999077
- 作者简介：
  
  [ "胡湘红（1991- ），女，深圳市大数据研究院博士生，主要研究方向为生物信息。" ]
  [ "彭衡（1974- ），男，香港浸会大学数学系副教授，主要研究方向为金融计量经济学、生物信息、模型选择、非参数方法。" ]
  [ "杨灿（1980- ），男，香港科技大学数学系助理教授，主要研究方向为生物信息学、高维数据分析、统计遗传学。" ]
  [ "张纵辉（1981- ），男，深圳市大数据研究院副教授，主要研究方向为信号处理、最优化方法、数据通信。" ]
  [ "万翔（1972- ），男，深圳市大数据研究院研究科学家，主要研究方向为机器学习、医疗大数据、生物信息。" ]
  [ "罗智泉（1963- ），男，深圳市大数据研究院教授，主要研究方向为最优化方法、算法设计、信息科学。" ]
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.2019033
  中图分类号： TP391
- 网络首发：2019-07，
  
  纸质出版：2019-07-15
- 稿件说明：
移动端阅览
胡湘红, 彭衡, 杨灿, 等. 基因大数据的集成分析[J]. 大数据, 2019,5(4):67-88.

Xianghong HU, Heng PENG, Can YANG, et al. Integrative analysis for big data in genomics[J]. Big Data Research, 2019, 5(4): 67-88.
胡湘红, 彭衡, 杨灿, 等. 基因大数据的集成分析[J]. 大数据, 2019,5(4):67-88. DOI： 10.11959/j.issn.2096-0271.2019033.

Xianghong HU, Heng PENG, Can YANG, et al. Integrative analysis for big data in genomics[J]. Big Data Research, 2019, 5(4): 67-88. DOI： 10.11959/j.issn.2096-0271.2019033.

摘要

随着生物科技（如基因芯片和测序技术）的飞速发展，全世界已经积累了海量的数据。有效地整合和集成多层面和多维度的基因大数据，对于全方位解析从遗传变异到疾病发生的整个因果链条具有关键作用，可为个性化、精准医疗服务奠定科学的基础。从3个方面对基因大数据的集成分析进行综述：检测风险位点及其功能分析、基因多效性的分析、基于孟德尔随机化的因果推断。进一步结合具体的应用案例进行了阐述，最后对基因大数据的集成分析研究进行了总结以及展望。

Abstract

With the rapid development of bio-technology (e.g.

genotyping chip and sequencing)

world-wide researchers have accumulated massive data sets at different levels.Integrative analysis of multi-layered genomic data can greatly contribute to the completion of causal chain from genetic variants to phenotype variations

laying a scientific foundation for personalized and precise medicine.The integrative analysis from the following three aspects mainly reviewed:identification of causal variants and their functional annotation

pleiotropy in human complex traits

Mendelian randomization forcausal inference between phenotypes

and several case studies were provided.Finally

the importance of integrative analysis in genomic data for precision medicine was highlighted.

关键词

Keywords

references

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