Báo cáo sinh học: " A fast algorithm for estimating transmission probabilities in QTL detection designs with dense maps"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học quốc tế đề tài: A fast algorithm for estimating transmission probabilities in QTL detection designs with dense maps | Genetics Selection Evolution BioMed Central Research A fast algorithm for estimating transmission probabilities in QTL detection designs with dense maps Jean-Michel Elsen 1 Olivier Filangi2 Hélène Gilbert3 Pascale Le Roy2 and Carole Moreno1 Open Access Address 1INRA SAGA BP27 31326 Castanet Tolosan cedex France 2INRA GARen Agrocampus 35000 Rennes France and 3INRA GABI 78352 Jouy en Josas cedex France Email Jean-Michel Elsen - Olivier Filangi - Hélène Pascale Le Carole Moreno - Corresponding author Published 17 November 2009 Received 31 July 2009 r . rn J. rn Accepted 17 November 2009 Genetics Selection Evolution 2009 41 50 doi 1297-9686-41 -50 This article is available from http content 41 1 50 2009 Elsen et al licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract Background In the case of an autosomal locus four transmission events from the parents to progeny are possible specified by the grand parental origin of the alleles inherited by this individual. Computing the probabilities of these transmission events is essential to perform QTL detection methods. Results A fast algorithm for the estimation of these probabilities conditional to parental phases has been developed. It is adapted to classical QTL detection designs applied to outbred populations in particular to designs composed of half and or full sib families. It assumes the absence of interference. Conclusion The theory is fully developed and an example is given. Background Experimental designs used for mapping QTL in livestock based on linkage analysis techniques .

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