Theoretical and Applied Genetics

, Volume 125, Issue 5, pp 933–953

Multivariate whole genome average interval mapping: QTL analysis for multiple traits and/or environments

Original Paper

DOI: 10.1007/s00122-012-1884-9

Cite this article as:
Verbyla, A.P. & Cullis, B.R. Theor Appl Genet (2012) 125: 933. doi:10.1007/s00122-012-1884-9

Abstract

A major aim in some plant-based studies is the determination of quantitative trait loci (QTL) for multiple traits or across multiple environments. Understanding these QTL by trait or QTL by environment interactions can be of great value to the plant breeder. A whole genome approach for the analysis of QTL is presented for such multivariate applications. The approach is an extension of whole genome average interval mapping in which all intervals on a linkage map are included in the analysis simultaneously. A random effects working model is proposed for the multivariate (trait or environment) QTL effects for each interval, with a variance–covariance matrix linking the variates in a particular interval. The significance of the variance–covariance matrix for the QTL effects is tested and if significant, an outlier detection technique is used to select a putative QTL. This QTL by variate interaction is transferred to the fixed effects. The process is repeated until the variance–covariance matrix for QTL random effects is not significant; at this point all putative QTL have been selected. Unlinked markers can also be included in the analysis. A simulation study was conducted to examine the performance of the approach and demonstrated the multivariate approach results in increased power for detecting QTL in comparison to univariate methods. The approach is illustrated for data arising from experiments involving two doubled haploid populations. The first involves analysis of two wheat traits, α-amylase activity and height, while the second is concerned with a multi-environment trial for extensibility of flour dough. The method provides an approach for multi-trait and multi-environment QTL analysis in the presence of non-genetic sources of variation.

Supplementary material

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Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  1. 1.School of Agriculture, Food and WineThe University of AdelaideGlen OsmondAustralia
  2. 2.Mathematics, Informatics and Statistics and Food Futures National Research FlagshipUrrbraeAustralia
  3. 3.School of Mathematics and Applied StatisticsUniversity of WollongongWollongongAustralia
  4. 4.Mathematics, Informatics and Statistics and Food Futures National Research FlagshipActonAustralia

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