AprioriGWAS, a New Pattern Mining Strategy for Detecting Genetic Variants Associated with Disease through Interaction Effects

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	AprioriGWAS, a New Pattern Mining Strategy for Detecting Genetic Variants Associated with Disease through Interaction Effects
	Zhang, Qingrun 1; Long, Quan 1; Ott, Jurg 2,3
摘要	Identifying gene-gene interaction is a hot topic in genome wide association studies. Two fundamental challenges are: (1) how to smartly identify combinations of variants that may be associated with the trait from astronomical number of all possible combinations; and (2) how to test epistatic interaction when all potential combinations are available. We developed AprioriGWAS, which brings two innovations. (1) Based on Apriori, a successful method in field of Frequent Itemset Mining (FIM) in which a pattern growth strategy is leveraged to effectively and accurately reduce search space, AprioriGWAS can efficiently identify genetically associated genotype patterns. (2) To test the hypotheses of epistasis, we adopt a new conditional permutation procedure to obtain reliable statistical inference of Pearson's chi-square test for the 2 x f contingency table generated by associated variants. By applying AprioriGWAS to age-related macular degeneration (AMD) data, we found that: (1) angiopoietin 1 (ANGPT1) and four retinal genes interact with Complement Factor H (CFH). (2) GO term "glycosaminoglycan biosynthetic process" was enriched in AMD interacting genes. The epistatic interactions newly found by AprioriGWAS on AMD data are likely true interactions, since genes interacting with CFH are retinal genes, and GO term enrichment also verified that interaction between glycosaminoglycans (GAGs) and CFH plays an important role in disease pathology of AMD. By applying AprioriGWAS on Bipolar disorder in WTCCC data, we found variants without marginal effect show significant interactions. For example, multiple-SNP genotype patterns inside gene GABRB2 and GRIA1 (AMPA subunit 1 receptor gene). AMPARs are found in many parts of the brain and are the most commonly found receptor in the nervous system. The GABRB2 mediates the fastest inhibitory synaptic transmission in the central nervous system. GRIA1 and GABRB2 are relevant to mental disorders supported by multiple evidences.
	2014-06-01
语种	英语
发表期刊	PLOS COMPUTATIONAL BIOLOGY
ISSN	1553-734X
卷号	10 期号:6 页码:1-14
期刊论文类型	Article
收录类别	SCI
WOS记录号	WOS:000338848100007
引用统计	被引频次：19[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.psych.ac.cn/handle/311026/13461
专题	健康与遗传心理学研究室
作者单位	1.Icahn Sch Med Mt Sinai, Dept Genet & Genom Sci, Inst Genom & Multiscale Biol, New York, NY 10029 USA 2.Chinese Acad Sci, Inst Psychol, Beijing 100101, Peoples R China 3.Rockefeller Univ, Lab Stat Genet, New York, NY 10021 USA
推荐引用方式 GB/T 7714	Zhang, Qingrun,Long, Quan,Ott, Jurg. AprioriGWAS, a New Pattern Mining Strategy for Detecting Genetic Variants Associated with Disease through Interaction Effects[J]. PLOS COMPUTATIONAL BIOLOGY,2014,10(6):1-14.
APA	Zhang, Qingrun,Long, Quan,&Ott, Jurg.(2014).AprioriGWAS, a New Pattern Mining Strategy for Detecting Genetic Variants Associated with Disease through Interaction Effects.PLOS COMPUTATIONAL BIOLOGY,10(6),1-14.
MLA	Zhang, Qingrun,et al."AprioriGWAS, a New Pattern Mining Strategy for Detecting Genetic Variants Associated with Disease through Interaction Effects".PLOS COMPUTATIONAL BIOLOGY 10.6(2014):1-14.

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