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

• DOI: 10.1007/s11250-024-03948-6
• Publication Date: 2024-03-20T17:01:26Z
• Authors: Samir Julián Calv...; Carolina Andrea G...; Carlos Santiago E...; Henry Cardona Cad...; Juan David Corral...; José Luis Gualdró...; Andres Rogberg-Muñoz
• Keywords: Bayesian inference; Evolutionary biology; Plant Science; Bayesian probability; Quantitative Genetics; Agricultural and Biological Sciences; Heritability; Context (archaeology); Cluster analysis; Cultivar Evaluation and Mega-Environment Investigation; Genetic Value Prediction; Variance (accounting); Biochemistry, Genetics and Molecular Biology; Accounting; Genetics; FOS: Mathematics; Animals; Lactation; Business; Biology; 2. Zero hunger; Additive genetic effects; Models, Genetic; Goats; Statistics; Life Sciences; Paleontology; Bayes Theorem; Bayesian information criterion; Pedigree; Genetic Mapping; Phenotype; Milk; Genetic Architecture of Quantitative Traits; FOS: Biological sciences; Genomic Selection in Plant and Animal Breeding; Female; Deviance information criterion; Mathematics

Abstract 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.