Finding unique filter sets in plato: A precursor to efficient interaction analysis in gwas data

Pacific Symposium on Biocomputing 2010, PSB 2010

Volumn , Issue , 2010, Pages 315-326

  Grady, Benjamin J ^a   Torstenson, Eric ^a   Dudek, Scott M ^a   Giles, Justin ^a   Sexton, David ^a   Ritchie, Marylyn D ^a  

^a VANDERBILT UNIVERSITY MEDICAL CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANALYTICAL METHOD; DATA SETS; EFFECT SIZE; EFFICIENT INTERACTION; FALSE POSITIVE RATES; FILTER SETS; FILTER-BASED; GENE-GENE INTERACTION; GENETIC MODELS; GENETIC VARIANTS; GENOME-WIDE ASSOCIATION; INTERACTION ANALYSIS; P-VALUES; PERMUTATION TESTING; PROOF OF CONCEPT; SIMULATION STUDIES;

ASSOCIATION REACTIONS; COMPUTER SIMULATION; GENES; HIDDEN MARKOV MODELS;

STATISTICS;

ARTICLE; BIOLOGICAL MODEL; BIOLOGY; COMPUTER SIMULATION; EPISTASIS; GENETIC ASSOCIATION; GENETIC DATABASE; GENETIC PREDISPOSITION; HUMAN; KNOWLEDGE BASE; SINGLE NUCLEOTIDE POLYMORPHISM; STATISTICAL ANALYSIS; STATISTICS;

COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; DATABASES, GENETIC; EPISTASIS, GENETIC; GENETIC PREDISPOSITION TO DISEASE; GENOME-WIDE ASSOCIATION STUDY; HUMANS; KNOWLEDGE BASES; MODELS, GENETIC; POLYMORPHISM, SINGLE NUCLEOTIDE;

EID: 79952742395     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (27)

1. Sladek R, Rocheleau G, Rung J et al. A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature. 2007;445:881-885.
2. Hirschhorn JN, Altshuler D. Once and again-issues surrounding replication in genetic association studies. J Clin Endocrinol Metab. 2002;87:4438-4441.
3. Moore JH. The ubiquitous nature of epistasis in determining susceptibility to common human diseases. Hum Hered. 2003;56:73-82.
4. Culverhouse R, Klein T, Shannon W. Detecting epistatic interactions contributing to quantitative traits. Genet Epidemiol. 2004;27:141-152.
5. Williams SM, Addy JH, Phillips JA, III et al. Combinations of variations in multiple genes are associated with hypertension. Hypertension. 2000;36:2-6.
6. Carrasquillo MM, McCallion AS, Puffenberger EG, Kashuk CS, Nouri N, Chakravarti A. Genome-wide association study and mouse model identify interaction between RET and EDNRB pathways in Hirschsprung disease. Nat Genet. 2002;32:237-244.
7. Dipple KM, McCabe ER. Phenotypes of patients with "simple" Mendelian disorders are complex traits: thresholds, modifiers, and systems dynamics. Am J Hum Genet. 2000;66:1729-1735.
8. Dipple KM, McCabe ER. Modifier genes convert "simple" Mendelian disorders to complex traits. Mol Genet Metab. 2000;71:43-50.
9. Edwards TL, Bush WS, Turner SD et al. Generating Linkage Disequilibrium Patterns in Data Simulations Using genomeSIMLA. Lecture Notes in Computer Science. 2008;4793:24-35.
10. Dudek S, Motsinger AA, Velez D, Williams SM, Ritchie MD. Data simulation software for whole-genome association and other studies in human genetics. Pac Symp Biocomput. 2006;11:499-510.
11. Bush WS, Edwards TL, Dudek SM, McKinney BA, Ritchie MD. Alternative contingency table measures improve the power and detection of multifactor dimensionality reduction. BMC Bioinformatics. 2008;9:238.
12. Agresti A. Categorical Data Analysis. New York: John Wiley & Sons; 1990.
13. Armitage P. Tests for linear trends in proportions and frequencies. Biometrics. 1955;11:375-386.
14. Cochran WG. Some methods for strengthening the common chi-squared tests. Biometrics. 1954;10:417-451.
15. Freidlin B, Zheng G, Li Z, Gastwirth JL. Trend tests for case-control studies of genetic markers: power, sample size and robustness. Hum Hered. 2002;53:146-152.
16. Shannon CE. A Mathematical Theory of Communication. The Bell System Technical Journal. 1948;27:379-423.
17. Forbes AD. Classification-algorithm evaluation: five performance measures based on confusion matrices. J Clin Monit. 1995;11:189-206.
18. Jewell, N. P. Statistics for Epidemiology. 2004. Boca Raton, FL, CRC Press LLC.
19. Ritchie MD, Hahn LW, Roodi N et al. Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am J Hum Genet. 2001;69:138-147.
20. Ritchie M, Motsinger AA. Multifactor dimensionality reduction for detecting gene-gene and geneenvironment interactions in pharmacogenomics studies. Pharmacogenomics. 2005;6:823-834.
21. Wickens, T. D. Multiway Contingency Tables Analysis for the Social Sciences. 1989. Hillsdale, NJ, Lawrence Erlbaum Associates, Inc.
22. Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;33:159-174.
23. Bush WS, Dudek SM, Ritchie MD. Biofilter: a knowledge-integration system for the multi-locus analysis of genome-wide association studies. Pac Symp Biocomput. 2009;368-379.
24. Purcell S, Neale B, Todd-Brown K et al. PLINK: a tool set for whole-genome association and populationbased linkage analyses. Am J Hum Genet. 2007;81:559-575.
25. Wolpert DH, Macready WG. No Free Lunch Theorems for Optimization. Transactions on Evolutionary Computation. 1997;1:67-82.
26. Motsinger-Reif AA, Reif DM, Fanelli TJ, Ritchie MD. A comparison of analytical methods for genetic association studies. Genetic Epidemiology. 2008;32:767-778.
27. Duell EJ, Bracci PM, Moore JH, Burk RD, Kelsey KT, Holly EA. Detecting pathway-based gene-gene and gene-environment interactions in pancreatic cancer. Cancer Epidemiology, Biomarkers, and Prevention. 2008;17:1470-1479.