Confidence intervals in QTL mapping by bootstrapping

Journal Article · Sat Jun 01 04:00:00 EDT 1996 · Genetics

OSTI ID:543030

Visscher, P M; Thompson, R; Haley, C S ^[1]

---

1. Roslin Institute Edinburgh, Midlothian (United Kingdom)

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. 27 refs., 2 figs., 6 tabs.

• OSTI ID: 543030
• Journal Information: Genetics, Journal Name: Genetics Journal Issue: 2 Vol. 143; ISSN GENTAE; ISSN 0016-6731
• Country of Publication: United States
• Language: English

Related Subjects

55 BIOLOGY AND MEDICINE
BASIC STUDIES
99 GENERAL AND MISCELLANEOUS
BIOLOGICAL MARKERS
CHROMOSOMES
DATA ANALYSIS
ERRORS
EXPERIMENTAL DATA
GENETIC MAPPING
GENETIC VARIABILITY
GENETICS
PHENOTYPE
POPULATIONS
SIMULATION
SIZE
STATISTICAL MODELS