期刊论文详细信息
BMC Proceedings
Bayesian multi-QTL mapping for growth curve parameters
Luc L G Janss2  Henri C M Heuven1 
[1]Animal Breeding and Genomics Centre, Wageningen University P.O. box 338, 6700AH Wageningen, the Netherlands
[2]Aarhus University DJF Department of Genetics and Biotechnology P.O. Box 50, 8830 Tjele, Denmark
Others  :  1129507
DOI  :  10.1186/1753-6561-4-S1-S12
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【 摘 要 】

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.

【 授权许可】

   
2010 Heuven and Janss; licensee BioMed Central Ltd.

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【 参考文献 】
  • [1]Wu RL, Lin M: Opinion - Functional mapping - how to map and study the genetic architecture of dynamic complex traits. Nature Reviews Genetics 2006, 7:229-237.
  • [2]Yang RQ, Xu SZ: Bayesian shrinkage analysis of quantitative trait loci for dynamic traits. Genetics 2007, 176:1169-1185.
  • [3]R Development Core Team. R: A language and environment for statistical Computing. R Foundation for statistical computing, Vienna 2007.
  • [4]Gilmour AR, Gogel BJ, Cullis BR, Thompson R: ASReml user guide. Release 2.0 edition. 2006.
  • [5]George EI, Mcculloch RE: Variable Selection Via Gibbs Sampling. Journal of the American Statistical Association 1993, 88:881-889.
  • [6]Kass RE, Raftery AE: Bayes Factors. Journal of the American Statistical Association 1995, 90:773-795.
  • [7]Meuwissen TH, Goddard ME: Mapping multiple QTL using linkage disequilibrium and linkage analysis information and multitrait data. Genet Sel Evol 2004, 36:261-279. BioMed Central Full Text
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