A context-specific evaluation of polygenic embryo screening in best-prognosis preimplantation genetic testing cycles
Clicks: 7
ID: 327802
2026
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Emerging Content
1.8
/100
7 views
6 readers
AI Quality Assessment
Not analyzed
Readership in this journal
EmergingRanked #36 of 43 articles by views in Human reproduction open
Most read
Least read
Bar heights use a square-root scale.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Abstract STUDY QUESTION In best-prognosis preimplantation genetic testing (PGT) cycles—defined by the availability of multiple transferable embryos—can polygenic embryo screening (PES) provide meaningful, context-dependent stratification that may influence embryo prioritization? SUMMARY ANSWER In a sister-pair breast cancer validation cohort, the polygenic risk score (PRS) showed modest predictive performance, and when applied retrospectively to embryos, differences in modelled susceptibility were context-dependent, primarily influenced by the number of available embryos, parental polygenic risk profile, the limited discriminative power of the PRS, and the presence of disease-related monogenic variants. WHAT IS KNOWN ALREADY The use of PES as an embryo-ranking tool within PGT remains controversial. Its potential utility depends on embryo availability and clinical context, yet empirical evaluations in real-world PGT settings remain limited. STUDY DESIGN, SIZE, DURATION This retrospective study combined (i) family-based validation using 184 affected–unaffected sister pairs, and (ii) embryo-level analysis of 310 preimplantation genetic testing for monogenic disorders (PGT-M) cycles comprising 1,722 embryos. Simulation modelling estimated differences in modelled embryo-level PRS percentile ranking under varying clinical and genetic scenarios. PARTICIPANTS/MATERIALS, SETTING, METHODS Family-based performance of a breast cancer PRS was evaluated using affected and unaffected sister pairs. Embryo genotype data from PGT-M cycles were used to model PES under varying conditions, including the number of transferable embryos, the presence of pathogenic monogenic variants, and parental PRS percentile strata. MAIN RESULTS AND THE ROLE OF CHANCE Within-family PRS discrimination was modest: sisters in the top 10% of the PRS distribution had 4.07-fold higher breast cancer odds than their siblings (95% CI: 1.55–6.33). In the PGT-M cohort, 55% of cycles produced ≥3 transferable embryos, with a median within-cycle spread of approximately 25 PRS percentiles. In BRCA1/2-related cycles, PRS did not introduce distinct risk strata but revealed dispersion in modelled susceptibility among embryos sharing the same monogenic background. Embryo PRS distributions were concordant with parental profiles: 71% of embryos from couples with parental PRS ≥80th percentile were classified as high-PRS, compared with 7% when at least one parent was ≤20th percentile. Simulation analyses indicated that PRS-based embryo prioritization was associated with a 12-point lower mean embryo PRS percentile compared with morphology-based embryo prioritization in PGT-M cycles, attenuating to 10 points when PGT-A was incorporated. These differences reflect a statistical surrogate outcome only and do not demonstrate reduction in lifetime disease incidence or other clinical benefits. LARGE SCALE DATA Clinical data from 310 PGT-M cycles involving 1,722 embryos and a family-based validation cohort of 184 affected–unaffected sister pairs were analyzed. No population-scale dataset was generated. LIMITATIONS, REASONS FOR CAUTION These findings derive from high-prognosis PGT-M cycles and may not generalize to other PGT settings. Current PRS models have modest performance and variable transferability across ancestries. The retrospective design, limited subgroup sizes, and modelling assumptions (e.g., aneuploidy and sex distribution) further constrain interpretation. The primary simulation outcome—PRS percentile shift—is a hypothesis-generating surrogate endpoint, not a clinical outcome; clinical benefit was not assessed or demonstrated. These findings should not be interpreted as supporting routine clinical implementation of PES; premature use may increase parental anxiety, inequitable access, and pressure to rank embryos using predictions that remain unvalidated at the embryo level. WIDER IMPLICATIONS OF THE FINDINGS This study provides an empirical framework for evaluating the current limits of PES within PGT. The observed PRS percentile shifts appear concentrated in best-prognosis cycles with multiple transferable embryos and elevated parental PRS, whereas little change is observed when embryo numbers are limited or risk is dominated by high-penetrance monogenic variants. These findings are hypothesis-generating and may inform future prospective research and ethical discussion, while underscoring that clinical utility and implementation require further validation within appropriately governed settings. FUNDING This study was funded by Major Scientific Program of CITIC Group (No. 2023ZXKYB34100), the Science Foundation of Hunan Province (Grant 2023JJ30422), and Health Research Project of Hunan Provincial Health Commission (grant number: W20243089). DISCLOSURES The authors declare no conflicts of interest.
| Reference Key |
openalex_W7211938660
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Qi Wang, Sicong Zeng, Zixu Chen, Liang Hu, Xiao Hu, Pingyuan Xie, Juan Du, Xilin Xu, Guangxiu Lu, Yue‐Qiu Tan, Ge Lin |
| Journal | Human reproduction open |
| Year | 2026 |
| DOI |
10.1093/hropen/hoag078
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.