TAILIEUCHUNG - Báo cáo sinh học: " Detection and modelling of time-dependent QTL in animal populations"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học thế giới đề tài: Detection and modelling of time-dependent QTL in animal populations | Genet. Sel. Evol. 40 2008 177-194 INRA EDP Sciences 2008 DOI gse 2007043 Available online at Original article Detection and modelling of time-dependent QTL in animal populations Mogens S. LUND1 Peter Sorensen1 Per MADSEN1 Florence JAFFRE ZIC2 1 Faculty of Agricultural Sciences Department of Genetics and Biotechnology University of Aarhus Research Center Foulum . Box 50 8830 Tjele Denmark 2 UR337 Station de génétique quantitative et appliquée INRA 78350 Jouy-en-Josas France Received 2 January 2007 accepted 3 September 2007 Abstract - A longitudinal approach is proposed to map QTL affecting function-valued traits and to estimate their effect over time. The method is based on fitting mixed random regression models. The QTL allelic effects are modelled with random coefficient parametric curves and using a gametic relationship matrix. A simulation study was conducted in order to assess the ability of the approach to fit different patterns of QTL over time. It was found that this longitudinal approach was able to adequately fit the simulated variance functions and considerably improved the power of detection of time-varying QTL effects compared to the traditional univariate model. This was confirmed by an analysis of protein yield data in dairy cattle where the model was able to detect QTL with high effect either at the beginning or the end of the lactation that were not detected with a simple 305 day model. QTL detection longitudinal data random regression models 1. INTRODUCTION Detection of quantitative trait loci QTL has been an active field of research in animal genetics over recent years. Many of the traits of interest in these studies are measured repeatedly over time. In this paper time is used as a point along the trajectory of a longitudinal trait. Examples are milk production fat and protein yields or somatic cell count for dairy cattle growth curves for pigs or beef cattle and age-specific fitness components such as survival and

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