Mixed model QTL mapping in GenStat

Presented by

Fred van Eeuwijk & Marcos Malosetti
Biometris, Wageningen UR, P.O. Box 100, 6700 AC Wageningen, The Netherlands

When and where

Monday 11 July, Palm Cove
Note: This takes place the day before the Australasian Applied Statistics Conference (GenStat & ASReml)

Who should attend

Graduate students and professionals interested in a flexible QTL mapping approach applicable to single traits in single environments as well as to multiple traits and multiple environments, for standard biparental populations aswell as association panels and multi-parent populations. It is recommended that attendants have some familiarity with analysis of variance, regression, mixed models, and basic quantitative genetic concepts.


QTL mapping is introduced as an extension of mixed model analysis of single traits in single trials. Effectively, on a genomic grid genetic covariates are fitted that represent contrasts in unobserved QTL genotype probabilitiesgiven marker information. The calculation of these genetic covariates will be explained for various types of populations: inbreeders, outbreeders and association panels. After mixed model QTL analysis for single traits in singletrials, extensions will be described for multiple trials and multiple traits. Hands on QTL analyses will done in Windows dialogue form using GenStat.

Learning Objectives

By the end of the course you should be able to:Construct a genetic map from marker scores on different types of breeding populationsPerform a QTL analysis for a wide array of breeding populations, for single and multiple environments, and single and multiple traitsUse various inference procedures for assessing QTL evidenceReport QTL locations and effects

Instruction methods

Theory will be presented in the form of lectures. Supervised practical classes will allow attendants to become familiar with the details of the actions required to perform a QTL analysis in GenStat using Windows’ dialogues. These practical classes will also serve to learn how to interpret QTL mixed model analysis output.



  • Linkage analysis
  • Construction of genetic maps
  • Phenotypic analysis of single trials
  • Marker regression
  • Simple and composite interval mapping


  • Phenotypic analysis of multiple trials
  • Genotype by environment interaction
  • QTL analysis of multiple trials
  • QTL by environment interaction
  • Association mapping
  • QTL analysis of multiple traits