### Abstract

The methods to detect gene-gene interactions between variants in genome-wide association study (GWAS) datasets have not been well developed thus far. PLATO, the Platform for the Analysis, Translation and Organization of large-scale data, is a filter-based method bringing together many analytical methods simultaneously in an effort to solve this problem. PLATO filters a large, genomic dataset down to a subset of genetic variants, which may be useful for interaction analysis. As a precursor to the use of PLATO for the detection of gene-gene interactions, the implementation of a variety of single locus filters was completed and evaluated as a proof of concept. To streamline PLATO for efficient epistasis analysis, we determined which of 24 analytical filters produced redundant results. Using a kappa score to identify agreement between filters, we grouped the analytical filters into 4 filter classes; thus all further analyses employed four filters. We then tested the MAX statistic put forth by Sladek et al. ^{1} in simulated data exploring a number of genetic models of modest effect size. To find the MAX statistic, the four filters were run on each SNP in each dataset and the smallest p-value among the four results was taken as the final result. Permutation testing was performed to empirically determine the p-value. The power of the MAX statistic to detect each of the simulated effects was determined in addition to the Type 1 error and false positive rates. The results of this simulation study demonstrates that PLATO using the four filters incorporating the MAX statistic has higher power on average to find multiple types of effects and a lower false positive rate than any of the individual filters alone. In the future we will extend PLATO with the MAX statistic to interaction analyses for large-scale genomic datasets.

Original language | English (US) |
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Title of host publication | Pacific Symposium on Biocomputing 2010, PSB 2010 |

Pages | 315-326 |

Number of pages | 12 |

State | Published - Dec 1 2010 |

Event | 15th Pacific Symposium on Biocomputing, PSB 2010 - Kamuela, HI, United States Duration: Jan 4 2010 → Jan 8 2010 |

### Publication series

Name | Pacific Symposium on Biocomputing 2010, PSB 2010 |
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### Other

Other | 15th Pacific Symposium on Biocomputing, PSB 2010 |
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Country | United States |

City | Kamuela, HI |

Period | 1/4/10 → 1/8/10 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Computational Theory and Mathematics
- Biomedical Engineering
- Medicine(all)

### Cite this

*Pacific Symposium on Biocomputing 2010, PSB 2010*(pp. 315-326). (Pacific Symposium on Biocomputing 2010, PSB 2010).

}

*Pacific Symposium on Biocomputing 2010, PSB 2010.*Pacific Symposium on Biocomputing 2010, PSB 2010, pp. 315-326, 15th Pacific Symposium on Biocomputing, PSB 2010, Kamuela, HI, United States, 1/4/10.

**Finding unique filter sets in plato : A precursor to efficient interaction analysis in gwas data.** / Grady, Benjamin J.; Torstenson, Eric; Dudek, Scott M.; Giles, Justin; Sexton, David; Ritchie, Marylyn Deriggi.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Finding unique filter sets in plato

T2 - A precursor to efficient interaction analysis in gwas data

AU - Grady, Benjamin J.

AU - Torstenson, Eric

AU - Dudek, Scott M.

AU - Giles, Justin

AU - Sexton, David

AU - Ritchie, Marylyn Deriggi

PY - 2010/12/1

Y1 - 2010/12/1

N2 - The methods to detect gene-gene interactions between variants in genome-wide association study (GWAS) datasets have not been well developed thus far. PLATO, the Platform for the Analysis, Translation and Organization of large-scale data, is a filter-based method bringing together many analytical methods simultaneously in an effort to solve this problem. PLATO filters a large, genomic dataset down to a subset of genetic variants, which may be useful for interaction analysis. As a precursor to the use of PLATO for the detection of gene-gene interactions, the implementation of a variety of single locus filters was completed and evaluated as a proof of concept. To streamline PLATO for efficient epistasis analysis, we determined which of 24 analytical filters produced redundant results. Using a kappa score to identify agreement between filters, we grouped the analytical filters into 4 filter classes; thus all further analyses employed four filters. We then tested the MAX statistic put forth by Sladek et al. 1 in simulated data exploring a number of genetic models of modest effect size. To find the MAX statistic, the four filters were run on each SNP in each dataset and the smallest p-value among the four results was taken as the final result. Permutation testing was performed to empirically determine the p-value. The power of the MAX statistic to detect each of the simulated effects was determined in addition to the Type 1 error and false positive rates. The results of this simulation study demonstrates that PLATO using the four filters incorporating the MAX statistic has higher power on average to find multiple types of effects and a lower false positive rate than any of the individual filters alone. In the future we will extend PLATO with the MAX statistic to interaction analyses for large-scale genomic datasets.

AB - The methods to detect gene-gene interactions between variants in genome-wide association study (GWAS) datasets have not been well developed thus far. PLATO, the Platform for the Analysis, Translation and Organization of large-scale data, is a filter-based method bringing together many analytical methods simultaneously in an effort to solve this problem. PLATO filters a large, genomic dataset down to a subset of genetic variants, which may be useful for interaction analysis. As a precursor to the use of PLATO for the detection of gene-gene interactions, the implementation of a variety of single locus filters was completed and evaluated as a proof of concept. To streamline PLATO for efficient epistasis analysis, we determined which of 24 analytical filters produced redundant results. Using a kappa score to identify agreement between filters, we grouped the analytical filters into 4 filter classes; thus all further analyses employed four filters. We then tested the MAX statistic put forth by Sladek et al. 1 in simulated data exploring a number of genetic models of modest effect size. To find the MAX statistic, the four filters were run on each SNP in each dataset and the smallest p-value among the four results was taken as the final result. Permutation testing was performed to empirically determine the p-value. The power of the MAX statistic to detect each of the simulated effects was determined in addition to the Type 1 error and false positive rates. The results of this simulation study demonstrates that PLATO using the four filters incorporating the MAX statistic has higher power on average to find multiple types of effects and a lower false positive rate than any of the individual filters alone. In the future we will extend PLATO with the MAX statistic to interaction analyses for large-scale genomic datasets.

UR - http://www.scopus.com/inward/record.url?scp=79952742395&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79952742395&partnerID=8YFLogxK

M3 - Conference contribution

SN - 9814295299

SN - 9789814295291

T3 - Pacific Symposium on Biocomputing 2010, PSB 2010

SP - 315

EP - 326

BT - Pacific Symposium on Biocomputing 2010, PSB 2010

ER -