AA9int

SNP interaction pattern search using non-hierarchical additive model set

PRACTICAL Consortium

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

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. Availability and implementation: 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 information: Supplementary data are available at Bioinformatics online.

Original languageEnglish (US)
Pages (from-to)4141-4150
Number of pages10
JournalBioinformatics (Oxford, England)
Volume34
Issue number24
DOIs
StatePublished - Dec 15 2018

Fingerprint

Pattern Search
Additive Models
Single nucleotide Polymorphism
Nucleotides
Polymorphism
Single Nucleotide Polymorphism
Interaction
Model structures
Screening
Bioinformatics
Parallel processing systems
Statistical Power
Statistical methods
Model
Evaluate
Availability
Parallel Computing
Computational Biology
Statistical method

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

Cite this

AA9int : SNP interaction pattern search using non-hierarchical additive model set. / PRACTICAL Consortium.

In: Bioinformatics (Oxford, England), Vol. 34, No. 24, 15.12.2018, p. 4141-4150.

Research output: Contribution to journalArticle

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title = "AA9int: SNP interaction pattern search using non-hierarchical additive model set",
abstract = "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. Availability and implementation: 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 information: Supplementary data are available at Bioinformatics online.",
author = "{PRACTICAL Consortium} and Lin, {Hui Yi} and Huang, {Po Yu} and Chen, {Dung Tsa} and Tung, {Heng Yuan} and Sellers, {Thomas A.} and Pow-Sang, {Julio M.} and Rosalind Eeles and Doug Easton and Zsofia Kote-Jarai and {Amin Al Olama}, Ali and Sara Benlloch and Kenneth Muir and Giles, {Graham G.} and Fredrik Wiklund and Henrik Gronberg and Haiman, {Christopher A.} and Johanna Schleutker and Nordestgaard, {B{\o}rge G.} and Travis, {Ruth C.} and Freddie Hamdy and Neal, {David E.} and Nora Pashayan and Khaw, {Kay Tee} and Stanford, {Janet L.} and Blot, {William J.} and Thibodeau, {Stephen N} and Christiane Maier and Kibel, {Adam S.} and Cezary Cybulski and Lisa Cannon-Albright and Hermann Brenner and Radka Kaneva and Jyotsna Batra and Teixeira, {Manuel R.} and Hardev Pandha and Lu, {Yong Jie} and Park, {Jong Y.}",
year = "2018",
month = "12",
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T2 - SNP interaction pattern search using non-hierarchical additive model set

AU - PRACTICAL Consortium

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.

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. Availability and implementation: 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 information: Supplementary data are available at Bioinformatics online.

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DO - 10.1093/bioinformatics/bty461

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