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Open Access Research

Methods for the detection of multiple linked QTL applied to a mixture of full and half sib families

Hélène Gilbert1* and Pascale Le Roy2

Author Affiliations

1 Station de génétique quantitative et appliquée UR337, INRA, F-78352 Jouy-en-Josas, France

2 INRA-Agrocampus, F-35040 Rennes, France

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Genetics Selection Evolution 2007, 39:139-158  doi:10.1186/1297-9686-39-2-139


The electronic version of this article is the complete one and can be found online at: http://www.gsejournal.org/content/39/2/139


Received:22 May 2006
Accepted:8 November 2006
Published:17 February 2007

© 2007 INRA, EDP Sciences

Abstract

A new multiple trait strategy based on discriminant analysis was studied for efficient detection of linked QTL in outbred sib families, in comparison with a multivariate likelihood technique. The discriminant analysis technique describes the segregation of a linear combination of the traits in a univariate likelihood. This combination is calculated for each pair of positions depending on the inheritance of the pairs of QTL haplotypes in the progeny. The gains in power and accuracy for position estimations of multiple trait methods in grid searches were evaluated in reference to single trait detections of linked QTL. The methods were applied to simulated designs with two correlated traits submitted to various effects from the linked QTL. Multiple trait strategies were generally more powerful and accurate than the single trait technique. Linked QTL were distinguished when they were separated enough to identify informative recombinations: at least two genetic markers and 25 cM between the QTL under the simulated conditions. Except in a particular case, discriminant analysis was at least as powerful as the multivariate technique and its implementation was five times faster. Combining the advantages from both methodologies, we finally propose a complete strategy for rapid and efficient systematic multivariate detections in outbred populations.

Keywords:
QTL detection; linked QTL; multiple trait; sib families; simulations

Research

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