全基因组关联研究通路分析方法现状

doi:10.16288/j.yczz.16-419

遗传 ›› 2017, Vol. 39 ›› Issue (8): 707-716.doi: 10.16288/j.yczz.16-419

全基因组关联研究通路分析方法现状

王钰嫣(),王子兴,胡耀达,王蕾,李宁,张彪,韩伟,姜晶梅()

中国医学科学院基础医学研究所,北京 100005

收稿日期:2017-03-25 修回日期:2017-04-25 出版日期:2017-08-20 发布日期:2017-12-25
作者简介:王钰嫣,博士研究生,专业方向：生物统计学。E-mail: wangyyamy@163.com|姜晶梅,博士,教授,研究方向：统计学方法在医学研究中的应用。E-mail: jingmeijiang@ibms.pumc.edu.cn
基金资助:
北京协和医学院研究生创新基金(10023-1001-1005)

Current status of pathway analysis in genome-wide association study

Yuyan Wang(),Zixing Wang,Yaoda Hu,Lei Wang,Ning Li,Biao Zhang,Wei Han,Jingmei Jiang()

Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Beijing 100005, China

Received:2017-03-25 Revised:2017-04-25 Online:2017-08-20 Published:2017-12-25
Supported by:
the Graduate Innovation Foundation of Peking Union Medical College, China(10023-1001-1005)

摘要/Abstract

摘要：

全基因组关联研究(genome-wide association study, GWAS)自2005年首次发表以来已不断增进人们对疾病遗传机制的认识,结合系统生物学并改进统计分析方法是对GWAS数据进行深度挖掘的重要途径。通路分析(pathway analysis)将GWAS所检测的遗传变异根据一定的生物学含义组合为集合进行分析,有利于发现对疾病单独效应小却在通路中相互关联的遗传变异,更有利于进行生物学解释。当前通路分析在GWAS数据上已有较为广泛的应用并取得初步成果。与此同时,通路分析的统计方法仍在不断发展。本文旨在介绍现有直接以SNP为对象的GWAS通路分析算法,根据方法中是否采用核函数分为非核算法和核算法两大类,其中非核算法主要包括基因功能富集分析(gene set enrichment analysis, GSEA)和分层贝叶斯优取(hierarchical Bayes prioritization, HBP),核算法包括线性核(linear kernel, LIN)、状态认证核(identity-by-status kernel, IBS)和尺度不变核(powered exponential kernel)。通过介绍这些方法的计算原理和优缺点,以期为新算法的构建提供更好的思路,为GWAS领域研究方法的选择提供参考。

关键词: 全基因组关联研究, 通路分析, 核算法

Abstract:

Since the first publication in 2005, the genome-wide association study (GWAS) strategy has contributed significantly to the understanding of the mechanisms of human genetic diseases. Integrations of statistical methods and systematic biology are important means to explore the GWAS data. Pathway analysis establishes the importance of genetic variants from GWAS and provides insights into their biological significance. It is conducive in correlating the genetic variants, which have only small but interactive changes, to their importance in the biological pathways. At present, pathway analysis has been widely applied to studies of GWAS data, with relatively good results. In the meantime, various analytical methods are being developed and adapted for research on more types of complex data. In this review, we summarize the statistical methods of pathway analysis on GWAS data, and divide them into non-kernel methods and kernel methods. The non-kernel methods include gene set enrichment analysis (GSEA) and hierarchical Bayes prioritization (HBP) analysis, while kernel methods include linear kernel (LIN), identity-by-status kernel (IBS) and powered exponential kernel. We have summarized the calculation principles and features of these statistical methods to provide insights for further developments of new algorithms in GWAS research.

Key words: genome-wide association study, pathway analysis, kernel methods

王钰嫣,王子兴,胡耀达,王蕾,李宁,张彪,韩伟,姜晶梅. 全基因组关联研究通路分析方法现状[J]. 遗传, 2017, 39(8): 707-716.

Yuyan Wang,Zixing Wang,Yaoda Hu,Lei Wang,Ning Li,Biao Zhang,Wei Han,Jingmei Jiang. Current status of pathway analysis in genome-wide association study[J]. Hereditas(Beijing), 2017, 39(8): 707-716.

参考文献

[1]	Hirschhorn JN, Daly MJ. Genome-wide association studies for common diseases and complex traits. Nat Rev Genet, 2005, 6( 2): 95-108.
[2]	Han JW, Zhang XJ. Current status of genome-wide association study. Hereditas (Beijing), 2011, 33(1): 25-35.
[2]	韩建文, 张学军. 全基因组关联研究现状. 遗传, 2011, 33(1): 25-35.
[3]	Yan WL. Genome-wide association study on complex diseases: genetic statistical issues. Hereditas (Beijing), 2008, 30(5): 543-549.
[3]	严卫丽. 复杂疾病全基因组关联研究进展——遗传统计分析. 遗传, 2008, 30(5): 543-549.
[4]	Yan WL. Genome-wide association study on complex diseases: study design and genetic markers. Hereditas (Beijing), 2008, 30(4): 400-406.
[4]	严卫丽. 复杂疾病全基因组关联研究进展——研究设计和遗传标记. 遗传, 2008, 30(4): 400-406.
[5]	Uitterlinden AG. An introduction to genome-wide association studies: GWAS for dummies. Semin Reprod Med, 2016, 34( 4): 196-204.
[6]	Zhou JP, Pei ZY, Chen YB, Chen RS. Strategies of genome-wide association study based on high-throughput sequencing. Hereditas (Beijing), 2014, 36(11): 1099-1111.
[6]	周家蓬, 裴智勇, 陈禹保, 陈润生. 基于高通量测序的全基因组关联研究策略. 遗传, 2014, 36(11): 1099-1111.
[7]	Metzker ML. Sequencing technologies-the next generation. Nat Rev Genet, 2010, 11( 1): 31-46.
[8]	Zhu ZH, Zhang FT, Hu H, Bakshi A, Robinson MR, Powell JE, Montgomery GW, Goddard ME, Wray NR, Visscher PM, Yang J. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet, 2016, 48( 5):481-487.
[9]	Klein RJ, Zeiss C, Chew EY, Tsai JY, Sackler RS, Haynes C, Henning AK, SanGiovanni JP, Mane SM, Mayne ST, Bracken MB, Ferris FL, Ott J, Barnstable C, Hoh J. Complement factor H polymorphism in age-related macular degeneration. Science, 2005, 308( 5720): 385-389.
[10]	Power RA, Parkhill J, de Oliveira T. Microbial genome-wide association studies: lessons from human GWAS. Nat Rev Genet, 2017, 18( 1): 41-50.
[11]	Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR, Madden PA, Heath AC, Martin NG, Montgomery GW, Goddard ME, Visscher PM. Common SNPs explain a large proportion of the heritability for human height. Nat Genet, 2010, 42( 7): 565-569.
[12]	Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet, 2011, 88( 1): 76-82.
[13]	Manolio TA. Bringing genome-wide association findings into clinical use. Nat Rev Genet, 2013, 14( 8): 549-558.

编辑推荐

Metrics

www.chinagene.cn
备案号：京ICP备09063187号-4
总访问:,今日访问:,当前在线:

全基因组关联研究通路分析方法现状

Current status of pathway analysis in genome-wide association study

RichHTML

PDF (PC)

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics