Related Experiment Video
Updated: May 14, 2026
09:03
Semiconductor Sequencing for Preimplantation Genetic Testing for Aneuploidy
Published on: August 25, 2019
Maternal genetic variants associated with aneuploid conception: a narrative review
Siyao Ha1, Wenyi Liu1, Ping Yuan1,2
1Department of Obstetrics and Gynecology, the Reproductive Medicine Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
Human Reproduction Update
|May 13, 2026
Summary
Maternal genetic variants contribute to aneuploid conceptions, a cause of infertility and pregnancy loss. Identifying these variants can improve genetic diagnosis and reproductive health strategies.
Area of Science:
- Reproductive genetics
- Human genetics
- Genomics
Background:
- Aneuploid conception, leading to infertility and congenital disorders, stems from chromosome segregation errors.
- While maternal age is a risk factor, maternal genetic factors significantly influence aneuploidy risk.
Purpose of the Study:
- To review maternal genetic variants linked to aneuploid conceptions.
- To identify research gaps and future directions in this field.
Main Methods:
- Systematic literature search of PubMed, Embase, and Cochrane Library up to January 2026.
- Inclusion of human studies with genetic confirmation of aneuploidy and maternal variants (SNVs, SNPs, indels).
- Quality appraisal using a modified Newcastle-Ottawa Scale and variant classification per ACMG/AMP guidelines.
Main Results:
- Identified maternal variants functionally categorized into meiotic recombination, spindle dynamics, checkpoint enforcement, and maternal-to-zygotic transition.
- Highlighted likely pathogenic rare variants in KIF18A, ELL3, CEP120 and common variants in PLK4, CCDC66.
- Noted that some implicated genes are on commercial infertility panels, but direct aneuploidy association needs further validation.
Conclusions:
- Proposes 'aneuploidy predisposition' as a key dimension for infertility genetic diagnosis.
- Suggests this approach benefits women with unexplained infertility and recurrent aneuploidy.
- Emphasizes the need for AI-driven clinico-genomic studies and polygenic risk models.
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