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Strategies to Identify Genetic Variants Causing Infertility.
Xinbao Ding1, John C Schimenti1
1Cornell University, College of Veterinary Medicine, Department of Biomedical Sciences, Ithaca, NY 14853, USA.
Trends in Molecular Medicine
|January 12, 2021
Summary
Identifying genetic causes of infertility is crucial for developing effective therapies. Genome modulation and editing technologies are advancing the ability to test and potentially correct these genetic variants.
Area of Science:
- Reproductive biology
- Genetics
- Genomics
Background:
- Approximately 50% of infertility cases have suspected genetic origins, yet identifying specific genetic causes remains a significant hurdle.
- Advances in genomics offer powerful tools for investigating the genetic underpinnings of infertility across diverse study designs.
- Establishing the causal role of identified genetic variants is essential for translating diagnostic findings into viable therapeutic strategies.
Purpose of the Study:
- To review current strategies for identifying causative genetic variants in infertility.
- To highlight the role of modern genomics in exploring genetic infertility factors.
- To discuss the potential of genome modulation and editing technologies in functional variant testing and clinical correction.
Main Methods:
- Review of literature on genetic infertility research.
- Analysis of genomic approaches for variant identification.
- Examination of genome editing technologies for functional validation and therapeutic development.
Main Results:
- Genomics tools enable sophisticated exploration of genetic infertility causes.
- Genome modulation and editing technologies offer methods for functional variant testing.
- These technologies present potential avenues for clinical correction of infertility-related genetic variants.
Conclusions:
- Identifying causative genetic variants is key to advancing infertility diagnostics and treatments.
- Genome editing technologies are revolutionizing the functional assessment of genetic variants.
- Future research should focus on integrating these technologies for clinical applications in infertility management.
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