Hostname: page-component-7c8c6479df-r7xzm Total loading time: 0 Render date: 2024-03-28T14:50:33.501Z Has data issue: false hasContentIssue false

Pedigree estimation of the (sub) population contribution to the total gene diversity: the horse coat colour case

Published online by Cambridge University Press:  22 February 2010

E. Bartolomé*
Affiliation:
Departamento de Ciencias Agroforestales, EUITA, Universidad de Sevilla, Ctra. Utrera, km1, 41013, Sevilla, Spain
F. Goyache
Affiliation:
Área de Genética y Reproducción Animal, SERIDA-Deva, Camino de Rioseco, 1225, E-33394, Gijón (Asturias), Spain
A. Molina
Affiliation:
Departamento de Genética, Facultad de Veterinaria, Universidad de Córdoba, Ctra. Madrid-Córdoba, km396a, 14071, Córdoba, Spain
I. Cervantes
Affiliation:
Departamento de Producción Animal, Facultad de Veterinaria, Universidad Complutense de Madrid, Avda. Puerta de Hierro s/n, 28040, Madrid, Spain
M. Valera
Affiliation:
Departamento de Ciencias Agroforestales, EUITA, Universidad de Sevilla, Ctra. Utrera, km1, 41013, Sevilla, Spain
J. P. Gutiérrez
Affiliation:
Departamento de Producción Animal, Facultad de Veterinaria, Universidad Complutense de Madrid, Avda. Puerta de Hierro s/n, 28040, Madrid, Spain
*
E-mail: ebartolome@us.es
Get access

Abstract

A method to quantify the contribution of subpopulations to genetic diversity in the whole population was assessed using pedigree information. The standardization of between- and within-subpopulation mean coancestries was developed to account for the different coat colour subpopulation sizes in the Spanish Purebred (SPB) horse population. The data included 166264 horses registered in the SPB Studbook. Animals born in the past 11 years (1996 to 2006) were selected as the ‘reference population’ and were grouped according to coat colour into eight subpopulations: grey (64 836 animals), bay (33 633), black (9414), chestnut (1243), buckskin (433), roan (107), isabella (57) and white (37). Contributions to the total genetic diversity were first assessed in the existing subpopulations and later compared with two scenarios with equal subpopulation size, one with the mean population size (13 710) and another with a low population size (100). Ancestor analysis revealed a very similar origin for the different groups, except for six ancestors that were only present in one of the groups likely to be responsible for the corresponding colour. The coancestry matrix showed a close genetic relationship between the bay and chestnut subpopulations. Before adjustment, Nei’s minimum distance showed a lack of differentiation among subpopulations (particularly among the black, chestnut and bay subpopulations) except for isabella and white individuals, whereas after adjustment, white, roan and grey individuals appeared less differentiated. Standardization showed that balancing coat colours would contribute preserving the genetic diversity of the breed. The global genetic diversity increased by 12.5% when the subpopulations were size standardized, showing that a progressive increase in minority coats would be profitable for the genetic diversity of this breed. The methodology developed could be useful for the study of the genetic structure of subpopulations with unbalanced sizes and to predict their genetic importance in terms of their contribution to genetic variability.

Type
Full Paper
Copyright
Copyright © The Animal Consortium 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abad, M 2006. El caballo en la historia de España. Eds León University. León, Spain.Google Scholar
Agüera, E 2008. Córdoba, caballos y dehesas. Ed. Almuzara. Spain.Google Scholar
Arnason, T, Van Vleck, LD 2000. Genetic improvement of the horse. In The genetics of the horse (ed. A Bowling and A Ruvinsky), pp. 473498. CABI Publishing, Wallingford, UK.Google Scholar
Ballou, JD, Lacy, RC 1995. Identifying genetically important individuals for management of genetic variation in pedigreed populations. In Population management for survival and recovery: analytical methods and strategies in small population management (ed. JD Ballou, M Gilpin and TJ Foose), pp. 76111. Columbia University Press, New York, USA.Google Scholar
Boichard, D, Maignel, L, Verrier, E 1997. The value of using probabilities of gene origin to measure genetic variability in a population. Genetics Selection Evolution 29, 523.Google Scholar
Bowling, AT 2000. Genetics of colour variation. In The genetics of the horse (ed. A Bowling and A Ruvinsky), pp. 5370. CABI Publishing, Wallingford, UK.Google Scholar
Caballero, A, Toro, MA 2002. Analysis of genetic diversity for the management of conserved subdivided populations. Conservation Genetics 3, 289299.Google Scholar
Druml, T, Baumung, R, Sölkner, J 2009. Pedigree analysis in the Austrian Noriker draught horse: genetic diversity and the impact of breeding for coat colour on population structure. Journal of Animal Breeding and Genetics 126, 348356.Google Scholar
Eding, H, Bennewitz, J 2007. Measuring genetic diversity in farm animals. In Utilization and conservation of farm animal genetic resources (ed. K Oldenbroek), pp. 103130. Wageningen Academic Publishers, Wageningen, The Netherlands.CrossRefGoogle Scholar
Eding, H, Meuwissen, THE 2001. Marker-based estimates of between and within population kinships for the conservation of genetic diversity. Journal of Animal Breeding and Genetics 118, 141159.CrossRefGoogle Scholar
FAO 1998. Secondary guidelines for the national farm animal genetic resources management plans: management of small populations at risk. FAO, Rome, Italy.Google Scholar
Fernández, J, Toro, MA, Caballero, A 2008. Management of subdivided populations in conservation programs: development of a novel dynamic system. Genetics 179, 683692.Google Scholar
Ford, MJ 2002. Selection in captivity during supportive breeding may reduce fitness in the wild. Conservation Biology 16, 815825.Google Scholar
Foulley, J-L, Ollivier, L 2006. Estimating allelic richness and its diversity. Livestock Science 101, 150158.Google Scholar
Glazewska, I, Gralak, B 2006. Balancing selection in Polish Arabian horses. Livestock Science 105, 272276.Google Scholar
Glazewska, I, Jezierski, T 2004. Pedigree analysis of Polish Arabian horses based on founder contributions. Livestock Production Science 90, 293298.CrossRefGoogle Scholar
Gutiérrez, JP, Goyache, F 2005. A note on ENDOG: a computer program for analysing pedigree information. Journal of Animal Breeding and Genetics 122, 357360.CrossRefGoogle ScholarPubMed
Gutiérrez-Gil, B, Wiener, P, Williams, JL 2007. Genetic effects on coat colour in cattle: dilution of eumelanin and phaeomelanin pigments in an F2-Backcross Charolais × Holstein population. BMC Genetics 8, 112.Google Scholar
Icken, W, Bennewitz, J, Kalm, E 2007. Analysis of auction data for horses and influence factors. Züchtungskunde 79, 111118.Google Scholar
Lamoreux, ML, Wakamatsu, K, Ito, S 2001. Interaction of major coat color gene functions in mice as studied by chemical analysis of eumelanin and pheomelanin. Pigment Cell Research 14, 2331.CrossRefGoogle ScholarPubMed
Malécot, G 1948. Les Mathématiques de l’Hérédité. Masson et Cie, Paris, France.Google Scholar
Mariat, D, Taourit, S, Guerin, G 2003. A mutation in the MATP gene causes the cream coat color in the horse. Genetics Selection Evolution 35, 119133.Google Scholar
Nei, M 1989. Molecular evolutionary genetics. Columbia University Press, New York.Google Scholar
Oldenbroek, K 2007. Introduction. In Utilisation and conservation of farm animal genetic resources (ed. K Oldenbroek), pp. 1328. Wageningen Academic Publishers, Wageningen, The Netherlands.CrossRefGoogle Scholar
Ollivier, L, Foulley, J-L 2005. Aggregate diversity: new approach combining within- and between-breed diversity. Livestock Production Science 95, 247254.CrossRefGoogle Scholar
Ollivier, L, Foulley, J-L 2008. Managing genetic diversity, fitness and adaptation of farm animal genetic resources. In Adaptation and fitness in animal populations, evolutionary and breeding perspectives on genetic resource management (ed. JHJ van der Werf, H-U Graser, R Frankham and C Gondro), pp. 201227. Springer-Science and Business Media, BV, Australia.Google Scholar
Petit, RJ, El Mousadik, A, Pons, O 1998. Identifying populations for conservation on the basis of genetic markers. Conservation Biology 12, 844855.CrossRefGoogle Scholar
Stachurska, A, Brodacki, A 2000. Genetic structure of Malopolski horse population with respect to basic coat colours. Annals of Animal Science 27, 918.Google Scholar
Stachurska, A, Brodacki, A, Klimorowska, A 2005. Genetic structure of Silesian horse population with regard to the coat colour (in Polish). Roczniki Naukowe PTZ 1, 6372.Google Scholar
Stachurska, A, Pieta, M, Lojek, J, Szulowska, J 2007. Performance in racehorses of various colours. Livestock Science 106, 282286.Google Scholar
Thaon d’Arnoldi, C, Foulley, J-L, Ollivier, L 1998. An overview of the Weitzman approach to diversity. Genetics Selection Evolution 30, 149161.Google Scholar
Valera, M, Molina, A, Gutiérrez, JP, Gómez, J, Goyache, F 2005. Pedigree analysis in the Andalusian horse: population structure, genetic variability and influence of the Carthusian strain. Livestock Production Science 95, 5766.Google Scholar
Wang, J, Caballero, A 1999. Developments in predicting the effective size of subdivided populations. Heredity 82, 212226.Google Scholar
Weitzman, ML 1992. On diversity. The Quarterly Journal of Economics 107, 363405.CrossRefGoogle Scholar
Woolliams, J, Toro, M 2007. Chapter 3: what is genetic diversity? In Utilisation and conservation of farm animal genetic resources (ed. K Oldenbroek), pp. 5574. Wageningen Academic Publishers, Wageningen, The Netherlands.Google Scholar
Wright, S 1931. Evolution in Mendelian populations. Genetics 16, 97159.CrossRefGoogle ScholarPubMed
Zechner, P, Sölkner, J, Bodo, I, Druml, T, Baumung, R, Achmann, R, Marti, E, Habe, F, Brem, G 2002. Analysis of diversity and population structure in the Lipizzan horse breed based on pedigree information. Livestock Production Science 77, 137146.Google Scholar