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Mohammad Razmkabir

Mohammad Razmkabir

Academic rank: Associate Professor
ORCID:
Education: PhD.
ScopusId: 7896321
HIndex:
Faculty: Faculty of Agriculture
Address: Department of Animal Science, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran.
Phone: 00989188758565

Research

Title
Estimation of additive and dominance genetic variance components for female fertility traits in Iranian Holstein cows
Type
JournalPaper
Keywords
Dairy cow; dominance variance; fertility; non-additive
Year
2018
Journal JOURNAL OF AGRICULTURAL SCIENCE
DOI
Researchers Heydar Ghiasi ، Rostam Abdollahi-Arpanahi ، Mohammad Razmkabir ، Majid Khaldari ، Reza Taherkhani

Abstract

The aim of the current study was to estimate additive and dominance genetic variance components for days from calving to first service (DFS), a number of services to conception (NSC) and days open (DO). Data consisted of 25 518 fertility records from first parity dairy cows collected from 15 large Holstein herds of Iran. To estimate the variance components, two models, one including only additive genetic effects and another fitting both additive and dominance genetic effects together, were used. The additive and dominance relationship matrices were constructed using pedigree data. The estimated heritability for DFS, NSC and DO were 0.068, 0.035 and 0.067, respectively. The differences between estimated heritability using the additive genetic and additive-dominance genetic models were negligible regardless of the trait under study. The estimated dominance variance was larger than the estimated additive genetic variance. The ratio of dominance variance to phenotypic variance was 0.260, 0.231 and 0.196 for DFS, NSC and DO, respectively. Akaike’s information criteria indicated that the model fitting both additive and dominance genetic effects is the best model for analysing DFS, NSC and DO. Spearman’s rank correlations between the predicted breeding values (BV) from additive and additive-dominance models were high (0.99). Therefore, ranking of the animals based on predicted BVs was the same in both models. The results of the current study confirmed the importance of taking dominance variance into account in the genetic evaluation of dairy cows.