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000142471 037__ $$aDKFZ-2019-00190
000142471 041__ $$aeng
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000142471 1001_ $$aLin, Hui-Yi$$b0
000142471 245__ $$aAA9int: SNP interaction pattern search using non-hierarchical additive model set.
000142471 260__ $$aOxford$$bOxford Univ. Press$$c2018
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000142471 520__ $$aThe use of single nucleotide polymorphism (SNP) interactions to predict complex diseases is getting more attention during the past decade, but related statistical methods are still immature. We previously proposed the SNP Interaction Pattern Identifier (SIPI) approach to evaluate 45 SNP interaction patterns/patterns. SIPI is statistically powerful but suffers from a large computation burden. For large-scale studies, it is necessary to use a powerful and computation-efficient method. The objective of this study is to develop an evidence-based mini-version of SIPI as the screening tool or solitary use and to evaluate the impact of inheritance mode and model structure on detecting SNP-SNP interactions.We tested two candidate approaches: the Five-Full and AA9int method. The Five-Full approach is composed of the five full interaction models considering three inheritance modes (additive, dominant and recessive). The AA9int approach is composed of nine interaction models by considering non-hierarchical model structure and the additive mode. Our simulation results show that AA9int has similar statistical power compared to SIPI and is superior to the Five-Full approach, and the impact of the non-hierarchical model structure is greater than that of the inheritance mode in detecting SNP-SNP interactions. In summary, it is recommended that AA9int is a powerful tool to be used either alone or as the screening stage of a two-stage approach (AA9int+SIPI) for detecting SNP-SNP interactions in large-scale studies.The AA9int and parAA9int functions (standard and parallel computing version) are added in the SIPI R package, which is freely available at https://linhuiyi.github.io/LinHY_Software/.Supplementary data are available at Bioinformatics online.
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000142471 7001_ $$aHuang, Po-Yu$$b1
000142471 7001_ $$aChen, Dung-Tsa$$b2
000142471 7001_ $$aTung, Heng-Yuan$$b3
000142471 7001_ $$aSellers, Thomas A$$b4
000142471 7001_ $$aPow-Sang, Julio M$$b5
000142471 7001_ $$aEeles, Rosalind$$b6
000142471 7001_ $$aEaston, Doug$$b7
000142471 7001_ $$aKote-Jarai, Zsofia$$b8
000142471 7001_ $$aAmin Al Olama, Ali$$b9
000142471 7001_ $$aBenlloch, Sara$$b10
000142471 7001_ $$aMuir, Kenneth$$b11
000142471 7001_ $$aGiles, Graham G$$b12
000142471 7001_ $$aWiklund, Fredrik$$b13
000142471 7001_ $$aGronberg, Henrik$$b14
000142471 7001_ $$aHaiman, Christopher A$$b15
000142471 7001_ $$aSchleutker, Johanna$$b16
000142471 7001_ $$aNordestgaard, Børge G$$b17
000142471 7001_ $$aTravis, Ruth C$$b18
000142471 7001_ $$aHamdy, Freddie$$b19
000142471 7001_ $$aNeal, David E$$b20
000142471 7001_ $$aPashayan, Nora$$b21
000142471 7001_ $$aKhaw, Kay-Tee$$b22
000142471 7001_ $$aStanford, Janet L$$b23
000142471 7001_ $$aBlot, William J$$b24
000142471 7001_ $$aThibodeau, Stephen N$$b25
000142471 7001_ $$aMaier, Christiane$$b26
000142471 7001_ $$aKibel, Adam S$$b27
000142471 7001_ $$aCybulski, Cezary$$b28
000142471 7001_ $$aCannon-Albright, Lisa$$b29
000142471 7001_ $$0P:(DE-He78)90d5535ff896e70eed81f4a4f6f22ae2$$aBrenner, Hermann$$b30$$udkfz
000142471 7001_ $$aKaneva, Radka$$b31
000142471 7001_ $$aBatra, Jyotsna$$b32
000142471 7001_ $$aTeixeira, Manuel R$$b33
000142471 7001_ $$aPandha, Hardev$$b34
000142471 7001_ $$aLu, Yong-Jie$$b35
000142471 7001_ $$aConsortium, PRACTICAL$$b36$$eCollaboration Author
000142471 7001_ $$aPark, Jong Y$$b37
000142471 773__ $$0PERI:(DE-600)1468345-3$$a10.1093/bioinformatics/bty461$$gVol. 34, no. 24$$n24$$p4141-4150$$tBioinformatics$$v34$$x1460-2059$$y2018
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