Accounting for Variability in the Use of Permutation Testing to Detect Quantitative Trait Loci
Article first published online: 25 MAY 2004
Volume 56, Issue 1, pages 52–58, March 2000
How to Cite
Nettleton, D. and Doerge, R. W. (2000), Accounting for Variability in the Use of Permutation Testing to Detect Quantitative Trait Loci. Biometrics, 56: 52–58. doi: 10.1111/j.0006-341X.2000.00052.x
- Issue published online: 25 MAY 2004
- Article first published online: 25 MAY 2004
- Received September 1998. Revised June 1999. Accepted June 1999.
- Monte Carlo methods;
- Number of permutations;
- Permutation test;
- QTL mapping;
- Statistical genetics
Summary. Locating quantitative trait loci (QTL), or genomic regions associated with known molecular markers, is of increasing interest in a wide variety of applications ranging from human genetics to agricultural genetics. The hope of locating QTL (or genes) affecting a quantitative trait is that it will lead to characterization and possible manipulations of these genes. However, the complexity of both statistical and genetic issues surrounding the location of these regions calls into question the asymptotic statistical results supplying the distribution of the test statistics employed. Coupled with the power of current-day computing, permutation theory was reintroduced for the purpose of estimating the distribution of any test statistic used to test for the location of QTL. Permutation techniques have offered an attractive alternative to significance measures based on asymptotic theory. The ideas of permutation testing are extended in this application to include confidence intervals for the thresholds and p-values estimated in permutation testing procedures. The confidence intervals developed account for the Monte Carlo error associated with practical applications of permutation testing and lead to an effective method of determining an efficient permutation sample size.