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

TY - JOUR

T1 - AA9int

T2 - SNP interaction pattern search using non-hierarchical additive model set

AU - Lin, Hui-Yi

AU - Huang, Po-Yu

AU - Chen, Dung-Tsa

AU - Tung, Heng-Yuan

AU - Sellers, Thomas A

AU - Pow-Sang, Julio M

AU - Eeles, Rosalind

AU - Easton, Doug

AU - Kote-Jarai, Zsofia

AU - Amin Al Olama, Ali

AU - Benlloch, Sara

AU - Muir, Kenneth

AU - Giles, Graham G

AU - Wiklund, Fredrik

AU - Gronberg, Henrik

AU - Haiman, Christopher A

AU - Schleutker, Johanna

AU - Nordestgaard, Børge G

AU - Travis, Ruth C

AU - Hamdy, Freddie

AU - Neal, David E

AU - Pashayan, Nora

AU - Khaw, Kay-Tee

AU - Stanford, Janet L

AU - Blot, William J

AU - Thibodeau, Stephen N

AU - Maier, Christiane

AU - Kibel, Adam S

AU - Cybulski, Cezary

AU - Cannon-Albright, Lisa

AU - Brenner, Hermann

AU - Kaneva, Radka

AU - Batra, Jyotsna

AU - Teixeira, Manuel R

AU - Pandha, Hardev

AU - Lu, Yong-Jie

AU - Park, Jong Y

AU - PRACTICAL consortium

PY - 2018/12/15

Y1 - 2018/12/15

N2 - Motivation: 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. Results: 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.

AB - Motivation: 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. Results: 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.

U2 - 10.1093/bioinformatics/bty461

DO - 10.1093/bioinformatics/bty461

M3 - Journal article

C2 - 29878078

SN - 1367-4803

VL - 34

SP - 4141

EP - 4150

JO - Bioinformatics

JF - Bioinformatics

IS - 24

ER -