Finding unique filter sets in plato

A precursor to efficient interaction analysis in gwas data

Benjamin J. Grady, Eric Torstenson, Scott M. Dudek, Justin Giles, David Sexton, Marylyn Deriggi Ritchie

Research output: Chapter in Book/Report/Conference proceedingConference contribution

43 Citations (Scopus)

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 languageEnglish (US)
Title of host publicationPacific Symposium on Biocomputing 2010, PSB 2010
Pages315-326
Number of pages12
StatePublished - Dec 1 2010
Event15th Pacific Symposium on Biocomputing, PSB 2010 - Kamuela, HI, United States
Duration: Jan 4 2010Jan 8 2010

Publication series

NamePacific Symposium on Biocomputing 2010, PSB 2010

Other

Other15th Pacific Symposium on Biocomputing, PSB 2010
CountryUnited States
CityKamuela, HI
Period1/4/101/8/10

Fingerprint

Genes
Statistics
Genome-Wide Association Study
Genetic Models
Single Nucleotide Polymorphism
(1,2-diamino-4-nitrobenzene)dichloroplatinum(II)
Testing
Datasets

All Science Journal Classification (ASJC) codes

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

Cite this

Grady, B. J., Torstenson, E., Dudek, S. M., Giles, J., Sexton, D., & Ritchie, M. D. (2010). Finding unique filter sets in plato: A precursor to efficient interaction analysis in gwas data. In Pacific Symposium on Biocomputing 2010, PSB 2010 (pp. 315-326). (Pacific Symposium on Biocomputing 2010, PSB 2010).
Grady, Benjamin J. ; Torstenson, Eric ; Dudek, Scott M. ; Giles, Justin ; Sexton, David ; Ritchie, Marylyn Deriggi. / Finding unique filter sets in plato : A precursor to efficient interaction analysis in gwas data. Pacific Symposium on Biocomputing 2010, PSB 2010. 2010. pp. 315-326 (Pacific Symposium on Biocomputing 2010, PSB 2010).
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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.",
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Grady, BJ, Torstenson, E, Dudek, SM, Giles, J, Sexton, D & Ritchie, MD 2010, Finding unique filter sets in plato: A precursor to efficient interaction analysis in gwas data. in 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.

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Sexton, David

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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.

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M3 - Conference contribution

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Grady BJ, Torstenson E, Dudek SM, Giles J, Sexton D, Ritchie MD. Finding unique filter sets in plato: A precursor to efficient interaction analysis in gwas data. In Pacific Symposium on Biocomputing 2010, PSB 2010. 2010. p. 315-326. (Pacific Symposium on Biocomputing 2010, PSB 2010).