ORCID
https://orcid.org/0000-0001-5512-5132
Department
Biological Science
Year of Study
1
Full-time or Part-time Study
Full-time
Level
Postgraduate
Presentation Type
Poster
Supervisor
Deirdre Purfield
Supervisor
Noirin McHugh
Abstract
Poor reproductive performance has a negative impact on the profitability of sheep production systems with the number of lambs reared highlighted as a key driver of farm profitability. Nevertheless, the genetic improvement of reproductive traits in sheep has been constrained by their low heritability and pleiotropic nature. Understanding the underlying genetic architecture can improve genomic prediction estimates and enhance genetic gain.
This study will utilize large-scale genomic and phenotypic datasets to enhance genetic gain in sheep reproductive traits through four approaches: 1) to determine the effect of inbreeding depression on reproduction, 2) to identify causal variants impacting reproductive performance through genome-wide association studies (GWAS), 3) to assess the impact of including causal variations on genomic prediction accuracy, and 4) to evaluate the inclusion of non-additive effects on genomic prediction estimates. Data will be sourced from the Sheep Ireland database, which includes records for over 60,000 genotyped and 1 million phenotyped animals. Inbreeding coefficients and their effects on reproduction will be evaluated using genomic relationship matrices and runs of homozygosity. Genome-wide association studies using whole genome sequence will be completed to identify candidate genes and biological pathways associated with reproductive performance. Non-additive effects will be modelled using dominance relationship matrices and integrated into genomic prediction models.
The results generated from this project will increase genetic gain in the Irish sheep industry by increasing the accuracy of genomic prediction estimates and maximising the use of genomic information within breeding and management tools.
Keywords:
Sheep breeding, genomic selection, reproductive traits, inbreeding, inbreeding depression, GWAS, GBLUP, non-additive effect
Start Date
16-6-2025 11:00 AM
End Date
16-6-2025 12:00 PM
Recommended Citation
Rahman, Mujibur, "Exploiting genomics to advance genetic gain for reproductive traits in sheep" (2025). ORBioM (Open Research BioSciences Meeting). 6.
https://sword.mtu.ie/orbiom/2025/shorttalk/6
Included in
Agriculture Commons, Animal Sciences Commons, Biology Commons, Cell and Developmental Biology Commons, Ecology and Evolutionary Biology Commons, Genetics and Genomics Commons
Exploiting genomics to advance genetic gain for reproductive traits in sheep
Poor reproductive performance has a negative impact on the profitability of sheep production systems with the number of lambs reared highlighted as a key driver of farm profitability. Nevertheless, the genetic improvement of reproductive traits in sheep has been constrained by their low heritability and pleiotropic nature. Understanding the underlying genetic architecture can improve genomic prediction estimates and enhance genetic gain.
This study will utilize large-scale genomic and phenotypic datasets to enhance genetic gain in sheep reproductive traits through four approaches: 1) to determine the effect of inbreeding depression on reproduction, 2) to identify causal variants impacting reproductive performance through genome-wide association studies (GWAS), 3) to assess the impact of including causal variations on genomic prediction accuracy, and 4) to evaluate the inclusion of non-additive effects on genomic prediction estimates. Data will be sourced from the Sheep Ireland database, which includes records for over 60,000 genotyped and 1 million phenotyped animals. Inbreeding coefficients and their effects on reproduction will be evaluated using genomic relationship matrices and runs of homozygosity. Genome-wide association studies using whole genome sequence will be completed to identify candidate genes and biological pathways associated with reproductive performance. Non-additive effects will be modelled using dominance relationship matrices and integrated into genomic prediction models.
The results generated from this project will increase genetic gain in the Irish sheep industry by increasing the accuracy of genomic prediction estimates and maximising the use of genomic information within breeding and management tools.