Pathway analysis of genomic data: concepts, methods, and prospects for future development

被引:191
作者
Ramanan, Vijay K. [1 ,2 ,3 ]
Shen, Li [3 ,4 ]
Moore, Jason H. [5 ,6 ,7 ]
Saykin, Andrew J. [1 ,3 ,4 ]
机构
[1] Indiana Univ Sch Med, Dept Med & Mol Genet, Indianapolis, IN 46202 USA
[2] Indiana Univ Sch Med, Med Scientist Training Program, Indianapolis, IN 46202 USA
[3] Indiana Univ Sch Med, Dept Radiol & Imaging Sci, Ctr Neuroimaging, Indianapolis, IN 46202 USA
[4] Indiana Univ Sch Med, Ctr Computat Biol & Bioinformat, Indianapolis, IN 46202 USA
[5] Dartmouth Med Sch, Dept Genet, Lebanon, NH 03756 USA
[6] Dartmouth Med Sch, Inst Quantitat Biomed Sci, Lebanon, NH 03756 USA
[7] Dartmouth Med Sch, Dept Community & Family Med, Lebanon, NH 03756 USA
基金
美国国家科学基金会; 美国国家卫生研究院;
关键词
pathway analysis; gene set; enrichment methods; genome-wide association study; functional annotation; complex diseases; GENE SET ENRICHMENT; STATISTICAL NETWORK MODELS; WIDE ASSOCIATION DATA; ALZHEIMERS-DISEASE; SYSTEMS BIOLOGY; CANCER; EXPRESSION; GWAS; SNP; IDENTIFICATION;
D O I
10.1016/j.tig.2012.03.004
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
Genome-wide data sets are increasingly being used to identify biological pathways and networks underlying complex diseases. In particular, analyzing genomic data through sets defined by functional pathways offers the potential of greater power for discovery and natural connections to biological mechanisms. With the burgeoning availability of next-generation sequencing, this is an opportune moment to revisit strategies for pathway-based analysis of genomic data. Here, we synthesize relevant concepts and extant methodologies to guide investigators in study design and execution. We also highlight ongoing challenges and proposed solutions. As relevant analytical strategies mature, pathways and networks will be ideally placed to integrate data from diverse -omics sources to harness the extensive, rich information related to disease and treatment mechanisms.
引用
收藏
页码:323 / 332
页数:10
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