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Marker-assisted selection vis-à-vis bull fertility: coming full circle–a review
- Source :
- Molecular Biology Reports. 47:9123-9133
- Publication Year :
- 2020
- Publisher :
- Springer Science and Business Media LLC, 2020.
-
Abstract
- Bull fertility is considered an indispensable trait, as far as farm economics is concerned since it is the successful conception in a cow that provides calf crop, along with the ensuing lactation. This ensures sustainability of a dairy farm. Traditionally, bull fertility did not receive much attention by the farm managers and breeding animals were solely evaluated based on phenotypic predictors, namely, sire conception rate and seminal parameters in bull. With the advent of the molecular era in animal breeding, attempts were made to unravel the genetic complexity of bull fertility by the identification of genetic markers related to the trait. Marker-Assisted Selection (MAS) is a methodology that aims at utilizing the genetic information at markers and selecting improved populations for important traits. Traditionally, MAS was pursued using a candidate gene approach for identifying markers related to genes that are already known to have a physiological function related to the trait but this approach had certain shortcomings like stringent criteria for significance testing. Now, with the availability of genome-wide data, the number of markers identified and variance explained in relation to bull fertility has gone up. So, this presents a unique opportunity to revisit MAS by selection based on the information of a large number of genome-wide markers and thus, improving the accuracy of selection.
- Subjects :
- 0301 basic medicine
Candidate gene
business.industry
animal diseases
media_common.quotation_subject
Sire
Fertility
General Medicine
Biology
Marker-assisted selection
Explained variation
Biotechnology
03 medical and health sciences
030104 developmental biology
0302 clinical medicine
030220 oncology & carcinogenesis
Genetics
Trait
Identification (biology)
business
Molecular Biology
Selection (genetic algorithm)
media_common
Subjects
Details
- ISSN :
- 15734978 and 03014851
- Volume :
- 47
- Database :
- OpenAIRE
- Journal :
- Molecular Biology Reports
- Accession number :
- edsair.doi...........7f450d282002e17442b1b2783a3ea5d6