Abstract

Key messageIn the QTL analysis of multi-parent populations, the inclusion of QTLs with various types of effects can lead to a better description of the phenotypic variation and increased power.For the type of QTL effect in QTL models for multi-parent populations (MPPs), various options exist to define them with respect to their origin. They can be modelled as referring to close parental lines or to further away ancestral founder lines. QTL models for MPPs can also be characterized by the homo- or heterogeneity of variance for polygenic effects. The most suitable model for the origin of the QTL effect and the homo- or heterogeneity of polygenic effects may be a function of the genetic distance distribution between the parents of MPPs. We investigated the statistical properties of various QTL detection models for MPPs taking into account the genetic distances between the parents of the MPP. We evaluated models with different assumptions about the QTL effect and the form of the residual term using cross validation. For the EU-NAM data, we showed that it can be useful to mix in the same model QTLs with different types of effects (parental, ancestral, or bi-allelic). The benefit of using cross-specific residual terms to handle the heterogeneity of variance was less obvious for this particular data set.

Highlights

  • The papers by Rebaï and Goffinet (1993) and Muranty (1996) are early examples of quantitative trait locus (QTL) detection with populations derived from more than two parents

  • We present some assumptions on the genetic properties of specific multi-parent populations (MPPs) and make plausible how these properties can affect the choice of a statistical model for QTL mapping

  • We propose a procedure to build multi-QTL effect models in which different loci can be modelled by different types of QTL effect

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Summary

Introduction

The papers by Rebaï and Goffinet (1993) and Muranty (1996) are early examples of quantitative trait locus (QTL) detection with populations derived from more than two parents. We consider MPPs as a collection of crosses between at least three different parents and focus on an NAM population which involves crosses between a central parent and a set of peripheral ones. An MPP QTL analysis would, be the joint analysis of such a population using a common marker map. Other authors have sometimes called it family mapping (Würschum 2012), combined cross analysis (Li et al 2005) or multiple-cross analysis (Jourjon et al 2005). We present some assumptions on the genetic properties of specific MPPs and make plausible how these properties can affect the choice of a statistical model for QTL mapping. MPPs allow to test genetic effects within different backgrounds (Blanc et al 2006) and so extend the statistical inference

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