Name
Abstract | ||
|---|---|---|
Genome-wide association study (GWAS) aims to find genetic factors underlying complex phenotypic traits, for which epistasis or gene-gene interaction detection is often preferred over single-locus approach. However, the computational burden has been a major hurdle to apply epistasis test in the genome-wide scale due to a large number of single nucleotide polymorphism (SNP) pairs to be tested.We have developed a set of three efficient programs, FastANOVA, COE and TEAM, that support epistasis test in a variety of problem settings in GWAS. These programs utilize permutation test to properly control error rate such as family-wise error rate (FWER) and false discovery rate (FDR). They guarantee to find the optimal solutions, and significantly speed up the process of epistasis detection in GWAS.A web server with user interface and source codes are available at the website http://www.csbio.unc.edu/epistasis/. The source codes are also available at SourceForge http://sourceforge.net/projects/epistasis/. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1186/1751-0473-6-1 | Source code for biology and medicine |
Keywords | Field | DocType |
user interface,genome wide association study,permutation test,false discovery rate,biomedical research,single nucleotide polymorphism,source code,bioinformatics,error rate,family wise error rate | False discovery rate,Biology,Epistasis,Genome-wide association study,Single-nucleotide polymorphism,Bioinformatics,Genetics,SNP,Phenotypic trait | Journal |
Volume | Issue | ISSN |
6 | 1 | 1751-0473 |
Citations | PageRank | References |
4 | 0.44 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiang Zhang | 1 | 452 | 49.83 |
| Shunping Huang | 2 | 67 | 6.17 |
| Fei Zou | 3 | 14 | 1.99 |
| Wei Wang | 4 | 7122 | 746.33 |