Email updates

Keep up to date with the latest news and content from BMC Proceedings and BioMed Central.

This article is part of the supplement: Proceedings of the 13th European workshop on QTL mapping and marker assisted selection

Open Access Proceedings

Bayesian multi-QTL mapping for growth curve parameters

Henri C M Heuven12 and Luc L G Janss3

Author Affiliations

1 Clinical Sciences of Companion Animals Faculty of Veterinary Medicine, Utrecht University P.O. box 80163, 3508 TD Utrecht, The Netherlands

2 Animal Breeding and Genomics Centre, Wageningen University P.O. box 338, 6700AH Wageningen, the Netherlands

3 Aarhus University DJF Department of Genetics and Biotechnology P.O. Box 50, 8830 Tjele, Denmark

For all author emails, please log on.

BMC Proceedings 2010, 4(Suppl 1):S12  doi:10.1186/1753-6561-4-S1-S12

Published: 31 March 2010

Abstract

Background

Identification of QTL affecting a phenotype which is measured multiple times on the same experimental unit is not a trivial task because the repeated measures are not independent and in most cases show a trend in time. A complicating factor is that in most cases the mean increases non-linear with time as well as the variance. A two- step approach was used to analyze a simulated data set containing 1000 individuals with 5 measurements each. First the measurements were summarized in latent variables and subsequently a genome wide analysis was performed of these latent variables to identify segregating QTL using a Bayesian algorithm.

Results

For each individual a logistic growth curve was fitted and three latent variables: asymptote (ASYM), inflection point (XMID) and scaling factor (SCAL) were estimated per individual. Applying an 'animal' model showed heritabilities of approximately 48% for ASYM and SCAL while the heritability for XMID was approximately 24%. The genome wide scan revealed four QTLs affecting ASYM, one QTL affecting XMID and four QTLs affecting SCAL. The size of the QTL differed. QTL with a larger effect could be more precisely located compared to QTL with small effect. The locations of the QTLs for separate parameters were very close in some cases and probably caused the genetic correlation observed between ASYM and XMID and SCAL respectively. None of the QTL appeared on chromosome five.

Conclusions

Repeated observations on individuals were affected by at least nine QTLs. For most QTL a precise location could be determined. The QTL for the inflection point (XMID) was difficult to pinpoint and might actually exist of two closely linked QTL on chromosome one.