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Statistical power of QTL mapping methods applied to bacteria counts

Published online by Cambridge University Press:  01 February 2002

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
W. COPPIETERS
Affiliation:
Department of Genetics, Faculty of Veterinary Medicine, University of Liège, Boulevard de Colonster 20, B-4000 Liège, Belgium
J. M. ELSEN
Affiliation:
INRA, Station d'Amélioration Génétique des Animaux, BP 27, F-31326 Auzeville, France
F. LANTIER
Affiliation:
INRA, Laboratoire de Pathologie Infectieuse et Immunologie, F-37380 Nouzilly, France
C. MORENO
Affiliation:
INRA, Station d'Amélioration Génétique des Animaux, BP 27, F-31326 Auzeville, France
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
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Abstract

Most QTL mapping methods assume that phenotypes follow a normal distribution, but many phenotypes of interest are not normally distributed, e.g. bacteria counts (or colony-forming units, CFU). Such data are extremely skewed to the right and can present a high amount of zero values, which are ties from a statistical point of view. Our objective is therefore to assess the efficiency of four QTL mapping methods applied to bacteria counts: (1) least-squares (LS) analysis, (2) maximum-likelihood (ML) analysis, (3) non-parametric (NP) mapping and (4) nested ANOVA (AN). A transformation based on quantiles is used to mimic observed distributions of bacteria counts. Single positions (1 marker, 1 QTL) as well as chromosome scans (11 markers, 1 QTL) are simulated. When compared with the analysis of a normally distributed phenotype, the analysis of raw bacteria counts leads to a strong decrease in power for parametric methods, but no decrease is observed for NP. However, when a mathematical transformation (MT) is applied to bacteria counts prior to analysis, parametric methods have the same power as NP. Furthermore, parametric methods, when coupled with MT, outperform NP when bacteria counts have a very high proportion of zeros (70·8%). Our results show that the loss of power is mainly explained by the asymmetry of the phenotypic distribution, for parametric methods, and by the existence of ties, for the non-parametric method. Therefore, mapping of QTL for bacterial diseases, as well as for other diseases assessed by a counting process, should focus on the occurrence of ties in phenotypes before choosing the appropriate QTL mapping method.

Type
Research Article
Copyright
© 2001 Cambridge University Press
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