Format

Send to

Choose Destination
Theor Appl Genet. 1996 Dec;93(8):1193-201. doi: 10.1007/BF00223450.

Estimation of the contribution of quantitative trait loci (QTL) to the variance of a quantitative trait by means of genetic markers.

Author information

1
INRA-UPS-INAPG, Station de Génétique Végétale du Moulon, Ferme du Moulon, 91190, Gif sur Yvette, France.

Abstract

The estimation of the contribution of an individual quantitative trait locus (QTL) to the variance of a quantitative trait is considered in the framework of an analysis of variance (ANOVA). ANOVA mean squares expectations which are appropriate to the specific case of QTL mapping experiments are derived. These expectations allow the specificities associated with the limited number of genotypes at a given locus to be taken into account. Discrepancies with classical expectations are particularly important for two-class experiments (backcross, recombinant inbred lines, doubled haploid populations) and F2 populations. The result allows us firstly to reconsider the power of experiments (i.e. the probability of detecting a QTL with a given contribution to the variance of the trait). It illustrates that the use of classical formulae for mean squares expectations leads to a strong underestimation of the power of the experiments. Secondly, from the observed mean squares it is possible to estimate directly the variance associated with a locus and the fraction of the total variance associated to this locus (r l (2) ). When compared to other methods, the values estimated using this method are unbiased. Considering unbiased estimators increases in importance when (1) the experimental size is limited; (2) the number of genotypes at the locus of interest is large; and (3) the fraction of the variation associated with this locus is small. Finally, specific mean squares expectations allows us to propose a simple analytical method by which to estimate the confidence interval of r l (2) . This point is particularly important since results indicate that 95% confidence intervals for r l (2) can be rather wide:2-23% for a 10% estimate and 8-34% for a 20% estimate if 100 individuals are considered.

PMID:
24162530
DOI:
10.1007/BF00223450

Supplemental Content

Full text links

Icon for Springer
Loading ...
Support Center