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Updated: Jul 19, 2025

Transgenic Rodent Assay for Quantifying Male Germ Cell Mutant Frequency
Published on: August 6, 2014
Review: Genetic mutations affecting bull fertility.
Hubert Pausch1, Xena Marie Mapel1
1Animal Genomics, Department of Environmental Systems Science, ETH Zurich, Universitaetstrasse 2, 8092 Zurich, Switzerland.
Cattle genetics reveal key genes influencing male fertility. Identifying genetic variants helps improve artificial insemination success and avoid infertile bulls in breeding programs.
Area of Science:
- Animal Genetics
- Reproductive Biology
- Quantitative Genetics
Background:
- Cattle serve as a valuable model organism for studying male reproductive performance due to extensive genomic and phenotypic data.
- Artificial insemination and genomic prediction in cattle provide large datasets for genetic analysis of fertility traits.
Purpose of the Study:
- To identify genetic factors contributing to variation in male reproductive performance in cattle.
- To investigate the genetic basis of both monogenic reproductive disorders and quantitative fertility traits.
Main Methods:
- Genome-wide association testing (GWAS) using microarray-derived genotypes and detailed semen quality/insemination success phenotypes.
- Case-control association testing for identifying alleles responsible for monogenic reproductive disorders.
- Variance components estimation utilizing repeated semen quality measurements.
Main Results:
- Several causal recessive loss-of-function alleles for monogenic reproductive disorders were identified and are now monitored in breeding bulls.
- Genome-wide association studies identified quantitative trait loci (QTL) associated with male fertility, though underlying causal variants remain largely unknown.
- The identified QTL explain only a small proportion of the total heritability for male fertility.
Conclusions:
- Genetic analysis in cattle has successfully identified specific alleles causing reproductive disorders and highlighted QTL for fertility.
- Further integration of omics data (gene expression, etc.) with GWAS is crucial for fine-mapping causal variants underlying quantitative male fertility traits.
- Monitoring genetic variants aids in improving artificial insemination efficiency and overall herd reproductive performance.
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