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Título : Estudio de la estructura poblacional de cuatro razas ovinas españolas mediante un chip de SNPs
Otros títulos : Genetic structure of four Spanish sheep breeds using a SNP-chip
Autor : García Gámez, E.
Gutiérrez Gil, B.
Kijas, J.
Calvo Lacosta, Jorge Hugo
International Sheep Genomics Consortium (ISGC)
Arranz, J.J.
Fecha de publicación : 2011
Citación : García-Gámez, E., Gutiérrez-Gil, B., Kijas, J., Calvo, J.H., International Sheep Genomics Consortium (ISGC), Arranz, J.J. “Estudio de la estructura poblacional de cuatro razas ovinas españolas mediante un chip de SNPs”. En: 41 Jornadas de Estudio. XIV Jornadas sobre Producción Animal. AIDA. Zaragoza, España, 17 y 18 de mayo de 2011, pp.530-532. ISBN: 978-84-615-0062-8
Resumen : Modern genomic tools allow genome-wide association studies (GWAS) to find genes underlying economically important traits in animals. In human, these studies have found many associations that cannot be always replicated. One of the reasons for this is the presence of population stratification in groups considered to be homogeneous. The objective of this work was to study the genetic structure of four Spanish sheep breeds (Castellana, Churra, Ojalada and Rasa Aragonesa) included in the Sheep HapMap. We selected 154 SNPs from the Ovine SNP50BeadChip, using PLINK options. Five independent analyses were made with STRUCTURE software to calculate the most likely number of clusters. The closest breeds were Ojalada and Rasa Aragonesa; Awassi was the most different when compared with any of the Spanish breeds. Population structure analysis revealed K=6 to be the most likely number of clusters. According to these results, Awassi was the most different breed (K=2), whereas among the Spanish populations, Churra was the only one clustering separately from the rest (K=3). Moreover, there were more differences within this breed than among the others (K=6). This study represents a first approach to assess/study population structure in Spanish sheep breeds and understand how these populations will behave in future GWAS
URI : http://hdl.handle.net/10532/2024
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