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Non-parametric interval mapping in half-sib designs: use of midranks to account for ties

Published online by Cambridge University Press:  25 June 2003

P. TILQUIN
Affiliation:
Unité de Génétique, Faculté d'ingénierie biologique, agronomique et environnementale, Université catholique de Louvain, Croix du Sud 2 bte 14, B-1348 Louvain-la-Neuve, Belgium
I. VAN KEILEGOM
Affiliation:
Institut de Statistique, Université catholique de Louvain, Voie du Roman Pays 20, B-1348 Louvain-la-Neuve, Belgium
W. COPPIETERS
Affiliation:
Department of Genetics, Faculty of Veterinary Medicine, University of Liège, Boulevard de Colonster 20, B-4000 Liège, Belgium
E. LE BOULENGÉ
Affiliation:
Unité d'environmétrie et géomatique, Faculté d'ingénierie biologique, agronomique et environnementale, Université catholique de Louvain, Croix du Sud 2 bte 16, B-1348 Louvain-la-Neuve, Belgium
P. V. BARET
Affiliation:
Unité de Génétique, Faculté d'ingénierie biologique, agronomique et environnementale, Université catholique de Louvain, Croix du Sud 2 bte 14, B-1348 Louvain-la-Neuve, Belgium

Abstract

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In QTL analysis of non-normally distributed phenotypes, non-parametric approaches have been proposed as an alternative to the use of parametric tests on mathematically transformed data. The non-parametric interval mapping test uses random ranking to deal with ties. Another approach is to assign to each tied individual the average of the tied ranks (midranks). This approach is implemented and compared to the random ranking approach in terms of statistical power and accuracy of the QTL position. Non-normal phenotypes such as bacteria counts showing high numbers of zeros are simulated (0–80% zeros). We show that, for low proportions of zeros, the power estimates are similar but, for high proportions of zeros, the midrank approach is superior to the random ranking approach. For example, with a QTL accounting for 8% of the total phenotypic variance, a gain from 8% to 11% of power can be obtained. Furthermore, the accuracy of the estimated QTL location is increased when using midranks. Therefore, if non-parametric interval mapping is chosen, the midrank approach should be preferred. This test might be especially relevant for the analysis of disease resistance phenotypes such as those observed when mapping QTLs for resistance to infectious diseases.

Type
Research Article
Copyright
© 2003 Cambridge University Press