Genetic evaluations of dairy goats with few pedigree data: different approaches to use molecular information

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作者
Samir Julián Calvo Cardona
Carolina Andrea García-Baccino
Carlos Santiago Escobar-Restrepo
Henry Cardona Cadavid
Juan David Corrales Álvarez
José Luis Gualdrón Duarte
Andres Rogberg-Muñoz
机构
[1] Universidad Tecnológica de Pereira,Departamento de Producción, Facultad de Agronomía
[2] Facultad de Ciencias de La Salud,Facultad de Ciencias Agropecuarias
[3] Programa de Medicina Veterinaria y Zootecnia,Unit of Animal Genomics
[4] Grupo de Investigación BIOPEC,undefined
[5] Universidad de Buenos Aires,undefined
[6] Grupo de investigación en Agronomía y Zootecnia-GIAZ,undefined
[7] Facultad de Ciencias Agropecuarias,undefined
[8] Universidad Católica de Oriente,undefined
[9] Universidad de Antioquia UdeA,undefined
[10] Facultad de Ciencias Agrarias,undefined
[11] Grupo de Investigación Agrociencias,undefined
[12] Biodiversidad y Territorio-GAMMA,undefined
[13] Universidad de La Salle,undefined
[14] GIGA-R,undefined
[15] University of Liège,undefined
[16] CONICET-Universidad de Buenos Aires. Instituto de Investigaciones en Producción Animal (INPA),undefined
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关键词
Animal breeding; Genetic evaluation; Markers; Population structure; Pedigree reconstruction;
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摘要
One of the limitations of implementing animal breeding programs in small-scale or extensive production systems is the lack of production records and genealogical records. In this context, molecular markers could help to gain information for the breeding program. This study addresses the inclusion of molecular data into traditional genetic evaluation models as a random effect by molecular pedigree reconstruction and as a fixed effect by Bayesian clustering. The methods were tested for lactation curve traits in 14 dairy goat herds with incomplete phenotypic data and pedigree information. The results showed an increment of 37.3% of the relationships regarding the originals with MOLCOAN and clustering into five genetic groups. Data leads to estimating additive variance, error variance, and heritability with four different models, including pedigree and molecular information. Deviance Information Criterion (DIC) values demonstrate a greater fitting of the models that include molecular information either as fixed (genetic clusters) or as random (molecular matrix) effects. The molecular information of simple markers can complement genetic improvement strategies in populations with little information.
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