A - Papers appearing in refereed journals
Visscher, P. M., Thompson, R. and Haley, C. S. 1996. Confidence intervals in QTL mapping by bootstrapping. Genetics. 143 (2), pp. 1013-1020.
|Authors||Visscher, P. M., Thompson, R. and Haley, C. S.|
The determination of empirical confidence intervals for the location of quantitative trait loci (QTLs) was investigated using simulation. Empirical confidence intervals were calculated using a bootstrap resampling method for a backcross population derived from inbred lines. Sample sizes were either 200 or 500 individuals, and the QTL explained 1,5, or 10% of the phenotypic variance. The method worked well in that the proportion of empirical confidence intervals that contained the simulated QTL was close to expectation. In general, the confidence intervals were slightly conservatively biased. Correlations between the test statistic and the width of the confidence interval were strongly negative, so that the stronger the evidence for a QTL segregating, the smaller the empirical confidence interval for its location. The size of the average confidence interval depended heavily on the population size and the effect of the QTL. Marker spacing had only a small effect on the average empirical confidence interval. The LOD drop-off method to calculate empirical support intervals gave confidence intervals that generally were too small, in particular if confidence intervals were calculated only for samples above a certain significance threshold. The bootstrap method is easy to implement and is useful in the analysis of experimental data.
|Year of Publication||1996|
|Journal citation||143 (2), pp. 1013-1020|
|Web address (URL)||https://www.genetics.org/content/143/2/1013|
|Open access||Published as bronze (free) open access|
|Funder project or code||207|
|01 Jun 1996|
|Publication process dates|
|Accepted||24 Feb 1996|
|Copyright license||Publisher copyright|
|Publisher||Genetics Society of America (GSA)|
Permalink - https://repository.rothamsted.ac.uk/item/87861/confidence-intervals-in-qtl-mapping-by-bootstrapping