TAILIEUCHUNG - Báo cáo sinh học: "Mapping quantitative trait loci (QTL) in sheep. II. Meta-assembly and identification of novel QTL for milk production traits in sheep"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học quốc tế đề tài: Mapping quantitative trait loci (QTL) in sheep. II. Meta-assembly and identification of novel QTL for milk production traits in sheep | Genetics Selection Evolution BioMed Central Research Mapping quantitative trait loci QTL in sheep. II. Meta-assembly and identification of novel QTL for milk production traits in sheep Herman W Raadsma Elisabeth Jonas David McGill Matthew Hobbs Mary K Lam and Peter C Thomson Address ReproGen - Animal Bioscience Group Faculty of Veterinary Science University of Sydney 425 Werombi Road Camden NSW 2570 Australia Email Herman W Raadsma - raadsma@ Elisabeth Jonas - ejonas@ David McGill - dmcgill@ Matthew Hobbs - Mary K Lam - maryl@ Peter C Thomson - petert@ Corresponding author Open Access Published 22 October 2009 Received 2 July 2009 Genetics Selection Evolution 2009 41 45 doi 1297-9686-41 -45 Accepted 22 October 2009 This article is available from http content 41 1M5 2009 Raadsma 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_ An Awassi X Merino X Merino backcross family of 172 ewes was used to map quantitative trait loci QTL for different milk production traits on a framework map of 200 loci across all autosomes. From five previously proposed mathematical models describing lactation curves the Wood model was considered the most appropriate due to its simplicity and its ability to determine ovine lactation curve characteristics. Derived milk traits for milk fat protein and lactose yield as well as percentage composition and somatic cell score were used for single and two-QTL approaches using maximum likelihood estimation and regression analysis. A total of 15 significant P and additional .

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