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Pengcheng Xu

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Open access Sep 2026

High sperm DNA fragmentation is associated with increased embryonic chromosomal abnormality: a retrospective study of 8 years involving 5637 couples undergoing ART

Does sperm DNA fragmentation impair blastocyst chromosomal integrity? A high sperm DNA fragmentation index (DFI) is associated with an increased risk of blastocyst aneuploidy, particularly when the man has poor semen quality or when the female partner is aged ≥35 years. Evidence linking sperm DFI to embryo development and blastocyst chromosomal integrity remains inconsistent, and controversy persists regarding its association with blastocyst aneuploidy detected by preimplantation genetic testing for aneuploidy (PGT-A). Existing studies have often been limited by small sample sizes, inconsistent DFI assays, and insufficient controls of key confounders. This retrospective cohort study was conducted at a tertiary reproductive centre. A total of 5637 infertile couples undergoing assisted reproduction between January 2016 and March 2023 were screened, and 3948 couples with complete data were included after applying standardized exclusion criteria. All male partners who underwent sperm DNA fragmentation assessment using the Sperm Chromatin Structure Assay were included. Infertile couples were stratified by treatment modality, age of both partners, and male semen quality parameters. The primary outcome was blastocyst aneuploidy rate. Beta-binomial regression and zero-inflated beta-binomial regression were used to analyze embryonic outcomes, with model selection guided by the Akaike Information Criterion. Generalized linear mixed models were applied to account for clustering of multiple embryos within the same ovarian stimulation cycle. Higher DFI was associated with a higher rate of blastocyst aneuploidy. Compared to the low DFI group, the high DFI group had a significantly higher adjusted odds ratio (aOR) of aneuploidy (38.19% vs. 24.42%, aOR=1.35, 95% CI: 1.10–1.81, P=0.042), while there was no substantial difference between the medium and low DFI group (25.78% vs. 24.42%, aOR=0.93, 95% CI: 0.76–1.13, P=0.465). Particularly, when women were aged ≥35 years, the embryonic aneuploidy rate in the high DFI group was significantly higher than low DFI subgroup (52.02% vs. 38.75%, aOR=1.61, 95% CI: 1.12–2.32, P=0.010). Also, among men with severe oligoasthenoteratozoospermia (SOAT), high DFI significantly increased aneuploidy risk (36.81% vs. 18.03%, aOR=1.99, 95% CI: 1.11–3.57, P=0.022). For all men aged <40 years, high DFI was associated with a reduced likelihood of high-quality blastocyst formation (aOR=0.79, 95% CI: 0.64–0.99, P=0.044). Given the single-centre retrospective design, residual confounding cannot be excluded despite rigorous statistical adjustment. Routine sperm DFI assessment may provide clinically meaningful information for infertile couples, particularly those with advanced maternal age or compromised male fertility. These findings support the integration of DFI into embryo risk stratification and individualized treatment planning, potentially improving reproductive outcomes and resource allocation. This work was supported by the National Natural Science Foundation for Young Scientists (72504291), Hunan Provincial Natural Science Foundation of China (2025JJ50703), and the Scientific Research Foundation of Reproductive and Genetic Hospital of CITIC-Xiangya (YNXM-202411). None of the authors have any conflict of interest to disclose. N/A

L. Lou, Peng-Cheng Xu, P. Xie et al. · 0 citations
Book Open access Aug 2026

MUGO: Differentiable Combinatorial Optimization for Causal Variant Discovery in the Non-coding Genome

Deciphering how non-coding variants perturb gene regulation is central to translating GWAS loci into mechanism, yet existing prioritization methods rarely deliver cell-type-resolved molecular effects, causal variant-to-gene attribution, or principled reasoning about combinatorial interactions. We introduce MUGO (Multi-head Genomic Optimization), an in silico perturbation framework that casts variant discovery as differentiable combinatorial optimization over genomic sequence. MUGO relaxes discrete edits into a continuous probabilistic nucleotide mask and performs gradient-based optimization in input space to identify single- or multi-variant perturbations that maximize a user-specified molecular objective under a sequence-to-signal foundation model. This formulation makes genome-scale search computationally tractable while retaining direct, cell-type-specific molecular readouts and enabling precise quantification of non-additive interaction effects. Across five modalities and seven tissues using two foundation-model backbones, MUGO consistently outperforms three baselines in both optimization efficiency and effect modulation, while preserving robustness and cell-type specificity. Finally, MUGO-prioritized variants are enriched for GWAS signals across diverse tissues and a broad spectrum of complex traits, turning foundation-model predictions into scalable, cell-type-resolved hypotheses for causal variant discovery and combinatorial regulatory mechanisms. Code and documentation are available at https://github.com/aicb-ZhangLabs/MUGO.

Si-Ying Sun, Junhao Liu, Pengcheng Xu et al. · 0 citations

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