Introduction to QTL Mapping

Some basic concepts in genetics

This is a comprehensive presentation, prepared by Fred van Eeuwijk, Marcos Malosetti, Hans Jansen and Martin Boer of Wageningen University and Research Centre, that reviews basic conceps in genetics that are fundamental to QTL mapping.

QTL mapping - some basics

This presentation by Fred van Eeuwijk, Marcos Malosetti and Martin Boer of Wageningen University and Research Centre, reviews the fundamental principles of QTL mapping from identifying the genetic basis of phenotypic trait variation to testing for association between phenotypic traits and markers.

QTL mapping - further development

This presentation builds on the foundation laid in the previous presentation.


This presentation addresses inferential questions in QTL mapping:  which model to choose(fixed and random, subset selection); what level of test to use (multiple testing correction);  how many QTLs there are and which types of genetic effects they represent (additive / dominance/ epistasis/ QTLxE);  how to obtain point estimates for QTL allele effects;  how to obtain interval estimates for QTL allele effects;  how much of the phenotypic/genetic variance is explained by a QTL;  how to obtain point estimates for QTL locations; and how to obtain interval estimates QTL locations.

Marker regression SIM CIM in GenStat 14

This presentation provides an overview of marker regression, SIM and CIM in GenStat 14 - with reviews of the analytical pipeline,  phenotypic analyses and QTL analyses

Practical Introduction to QTL mapping

The major questions to be answered with a QTL experiment include: how many QTLs affect the trait? On which chromosomes are they located? Can we define confidence intervals for QTL locations? How big are the QTL effects? How precise are the estimates of QTL effects, i.e. standard errors? What is the origin of the favourable alleles? Answering these questions helps a breeder on how to use QTL information in their breeding strategy. This practical illustrates how those questions can be answered by discussing the different steps of QTL analysis: a) checking the phenotypic and molecular marker data, b) scanning the genome to detect QTLs, and c) selecting a final multi-QTL model to estimate QTL effects. The analysis is demonstrated using GenStat 14.

Training Materials - Mixed Model QTL Detection

This material introduces the different aspects of QTL detection with examples. It follows a didactical structure, first introducing basic concepts and gradually incorporating more advanced topics. Each section of the material has a short introduction followed by a number of questions around an example data set. The questions increase in complexity towards the end of the particular section. This is a hands-on training resource, where an example data set is presented and typical research questions are proposed to be solved by the researcher/trainee. An answer sheet with a short discussion of the different questions is included, making it possible to use the material for self-study.

The material has six examples/exercises: Exercise 1 – a simulated data set based on a 1 QTL model, to illustrate the basic principles of QTL detection using segregating populations (marker-based detection, simple interval mapping); Exercise 2 – a simulated data set where several QTLs are included in the model, to illustrate further developments in QTL mapping using segregating populations (eg: use of cofactors in composite interval mapping, QTL model selection, etc); Exercise 3 – a real data set where the principles learnt in exercises 1 and 2 are applied to a real situation; Exercise 4 – a real data set of several field trials, where the principles of field data analysis are discussed (mixed models for field experiments such as RCBD, Alpha design, spatial design, etc), estimation of important genetic parameters such as h2, genetic variance components, as well as the estimation of adjusted means; Exercise 5 – a real multi-environment data set, where phenotypic genotype by environment interaction (GxE) analysis is discussed as a first step of a QTL analysis. In this exercise, molecular marker information is included to discuss the principles of QTL mapping in multiple environments for detection of QTL and QTLxE; Exercise 6 – a real association mapping panel is used to illustrate the principles and statistical methods/models for linkage disequilibrium mapping.

These resource materials are downloadable below, together with an example dataset and list of relevant references.

Back to top