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

Journal Article


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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, a-amylase activity

    and height, while the second is concerned with a multienvironment

    trial for extensibility of flour dough. The

    method provides an approach for multi-trait and multienvironment

    QTL analysis in the presence of non-genetic

    sources of variation.

Publication Date


  • 2012

Citation


  • Verbyla, A. P. & Cullis, B. R. (2012). Multivariate whole genome average interval mapping: QTL analysis for multiple traits and/or environments. Theoretical and Applied Genetics: international journal of plant breeding research, 125 (5), 933-953.

Scopus Eid


  • 2-s2.0-84865610502

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=9427&context=infopapers

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/2092

Has Global Citation Frequency


Number Of Pages


  • 20

Start Page


  • 933

End Page


  • 953

Volume


  • 125

Issue


  • 5

Place Of Publication


  • Germany

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, a-amylase activity

    and height, while the second is concerned with a multienvironment

    trial for extensibility of flour dough. The

    method provides an approach for multi-trait and multienvironment

    QTL analysis in the presence of non-genetic

    sources of variation.

Publication Date


  • 2012

Citation


  • Verbyla, A. P. & Cullis, B. R. (2012). Multivariate whole genome average interval mapping: QTL analysis for multiple traits and/or environments. Theoretical and Applied Genetics: international journal of plant breeding research, 125 (5), 933-953.

Scopus Eid


  • 2-s2.0-84865610502

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=9427&context=infopapers

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/2092

Has Global Citation Frequency


Number Of Pages


  • 20

Start Page


  • 933

End Page


  • 953

Volume


  • 125

Issue


  • 5

Place Of Publication


  • Germany