AA9int: SNP interaction pattern search using non-hierarchical additive model set | Zendy

HuiYi Lin | Zendy; PoYu Huang | Zendy; DungTsa Chen | Zendy; Heng-Yuan Tung | Zendy; Thomas A. Sellers | Zendy; Julio M. PowSang | Zendy; Rosalind A. Eeles | Zendy; Doug Easton | Zendy; Zsofia KoteJarai | Zendy; Ali Amin Al Olama | Zendy; Sara Benlloch | Zendy; Kenneth Muir | Zendy; Graham G. Giles | Zendy; Fredrik Wiklund | Zendy; Henrik Grönberg | Zendy; Christopher A. Haiman | Zendy; Johanna Schleutker | Zendy; Børge G. Nordestgaard | Zendy; Ruth C. Travis | Zendy; Freddie C. Hamdy | Zendy; David E. Neal | Zendy; Nora Pashayan | Zendy; KayTee Khaw | Zendy; Janet L. Stanford | Zendy; William J. Blot | Zendy; Stephen N. Thibodeau | Zendy; Christiane Maier | Zendy; Adam S. Kibel | Zendy; Cezary Cybulski | Zendy; Lisa CanAlbright | Zendy; Hermann Brenner | Zendy; Radka Kaneva | Zendy; Jyotsna Batra | Zendy; Manuel R. Teixeira | Zendy; Hardev Pandha | Zendy; YongJie Lu | Zendy; Jong Y. Park | Zendy

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AA9int: SNP interaction pattern search using non-hierarchical additive model set

Author(s) -

HuiYi Lin,

PoYu Huang,

DungTsa Chen,

Heng-Yuan Tung,

Thomas A. Sellers,

Julio M. PowSang,

Rosalind A. Eeles,

Doug Easton,

Zsofia KoteJarai,

Ali Amin Al Olama,

Sara Benlloch,

Kenneth Muir,

Graham G. Giles,

Fredrik Wiklund,

Henrik Grönberg,

Christopher A. Haiman,

Johanna Schleutker,

Børge G. Nordestgaard,

Ruth C. Travis,

Freddie C. Hamdy,

David E. Neal,

Nora Pashayan,

KayTee Khaw,

Janet L. Stanford,

William J. Blot,

Stephen N. Thibodeau,

Christiane Maier,

Adam S. Kibel,

Cezary Cybulski,

Lisa CanAlbright,

Hermann Brenner,

Radka Kaneva,

Jyotsna Batra,

Manuel R. Teixeira,

Hardev Pandha,

YongJie Lu,

Jong Y. Park

Publication year - 2018

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bty461

Subject(s) - snp , computer science , inheritance (genetic algorithm) , single nucleotide polymorphism , tag snp , set (abstract data type) , hierarchical database model , epistasis , data mining , computational biology , genetics , biology , gene , genotype , programming language

The 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.

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