tailieunhanh - Báo cáo sinh học: " Assessing population genetic structure via the maximisation of genetic distance"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học quốc tế đề tài: Assessing population genetic structure via the maximisation of genetic distance | Genetics Selection Evolution BioMed Central Open Access Assessing population genetic structure via the maximisation of genetic distance Silvia T Rodriguez-Ramilo 1 2 Miguel A Toro1 3 and Jesús Fernandez1 Address 1Departamento de Mejora Genética Animal. Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria INIA . Crta. A Coruna Km. 7 5. 28040 Madrid Spain 2Departamento de Bioquímica Genética e Inmunología Facultad de Biología Universidad de Vigo 36310 Vigo Spain and 3Departamento de Producción Animal ETS Ingenieros Agrónomos Universidad Politécnica de Madrid Ciudad Universitaria 28040 Madrid Spain Email Silvia T Rodríguez-Ramilo - silviat@ Miguel AToro - Jesús Fernandez - jmj@ Corresponding author Published 9 November 2009 Received 13 March 2009 Genetics Selection Evolution 2009 41 49 doi l297-9686-4l -49 Accepted 9 November 2009 This article is available from http content 4l l 49 2009 Rodríguez-Ramilo 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 The inference of the hidden structure of a population is an essential issue in population genetics. Recently several methods have been proposed to infer population structure in population genetics. Methods In this study a new method to infer the number of clusters and to assign individuals to the inferred populations is proposed. This approach does not make any assumption on Hardy-Weinberg and linkage equilibrium. The implemented criterion is the maximisation via a simulated annealing algorithm of the averaged genetic distance between a predefined number of clusters. The performance of this method is .