Detection and modelling of time-dependent QTL in animal populations

Mogens S. Lund,Per Madsen,Peter Sorensen,Florence Jaffrézic

doi:10.1186/1297-9686-40-2-177

Mogens S. Lund, Per Madsen + Show 2 more

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https://doi.org/10.1186/1297-9686-40-2-177

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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.

Highlights

Detection of quantitative trait loci (QTL) has been an active field of research in animal genetics over recent years
In QTL mapping studies, longitudinal traits have generally been modelled as one record even though it is a function of several measurements recorded over a time period
The objectives of this study were (i) to assess the ability of the approach to fit different patterns of QTL effects over time in a simulated data set, (ii) to verify the hypothesis that the effects of QTL for protein production in dairy cattle generally change over time, and (iii) to verify the hypothesis that the power to identify a QTL is higher for the proposed method than with a traditional univariate method

Summary

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. In QTL mapping studies, longitudinal traits have generally been modelled as one record even though it is a function of several measurements recorded over a time period. This model fits the average QTL effect over time, which might be appropriate if the effect of the QTL is constant over time.

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