Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học Journal of Biology đề tài: Spatial autocorrelation of ovine protein polymorphisms in Europe | 531 Genet Sei Evol 1996 28 531-536 Elsevier INRA Note Spatial autocorrelation of ovine protein polymorphisms in Europe JG Ordás JA Carriedo Departamento de Production Animal I Facultad de Veterinaria Universidad de León 24071 León Spain Received 3 June 1996 accepted 8 October 1996 Summary - The allelic frequencies of haemoglobin protein X arylesterase transferrin carbonic anhydrase and albumin in 71 European ovine populations were studied using spatial autocorrelation analysis. Haemoglobin and transferrin show significant clinal patterns. The observed clines may be a result of migrations and genetic drift occurring since the first domestication of ovines. sheep protein polymorphism spatial autocorrelation gene flow Resume - Autocorrelation spatiale de polymorphismes protéiques ovins en Europe. Les frequences des gènes de 1 hémoglobine la protéine X l arylestérase la transferrine I anhydrase carbonique et I albumine dans 71 populations ovines européennes ont été soumises à une analyse d autocorrelation spatiale. L hemoglobine et la transferrine montrent des structurations spatiales significatives. Les clines observes peuvent être le résultat de migrations et de derive génétique intervenues depuis la premiere domestication des ovins. mouton polymorphisme protéique autocorrelation spatiale flux génique INTRODUCTION Spatial autocorrelation techniques can be used to study the pattern of allelic frequencies among populations in a given area Sokal and Oden 1978 Oden 1984 . This type of technique has been used in studies of allelic frequencies in different animal species and humans eg Sokal et al 1980 Easteal 1985 Barbujani 1987 Barbujani et al 1994 . Similar studies have not been undertaken on species of domestic animals. The aim of this study is to analyze the distribution patterns of allelic frequencies of some protein polymorphisms in European ovine populations by spatial autocorrelation. The description of these patterns could contribute to improving our understanding
