Xie Pingyuan, Pang Rijing, Zeng Luyao, Zhang Shuoping, Sun Lei, Yang Kaisen, Yang Xiaoyi, Zhou Shuang, Zhang Senlin, Liu Guangjian, Tan Yueqiu, Hu Liang, Gong Fei, Fei Jia, Lin Ge
Hunan Guangxiu Hospital, Hunan Normal University Health Science Center, Changsha, China.
Clinical Research Center for Reproduction and Genetics in Hunan Province, Reproductive and Genetic Hospital of CITIC-Xiangya, Changsha, China.
Hum Reprod Open. 2025 Sep 2;2025(4):hoaf054. doi: 10.1093/hropen/hoaf054. eCollection 2025.
Can ultra-low-coverage whole-genome sequencing (ulc-WGS) accurately identify abnormal ploidy during preimplantation genetic testing (PGT)?
The artificial intelligence (AI)-based PGT-Plus model demonstrates high accuracy in ploidy detection, offering a cost-effective solution that enhances clinical utility of PGT.
The predominant PGT for aneuploidy can identify chromosomal aneuploidies but cannot determine ploidy status. Transferring embryos with ploidy abnormalities can result in miscarriage and molar pregnancy. On the other hand, in ART, fertilization is assessed by morphological pronuclear assessment at the zygote stage. However, it has a low specificity in the prediction of abnormal ploidy status and embryos deemed abnormally fertilized can yield healthy pregnancies. Accurately identified abnormal ploidy in PGT-A can resolve current limitations and expand the utility range of PGT-A. Several studies have identified ploidy abnormalities; however, they were mainly based on single-nucleotide polymorphism (SNP) arrays or needed to combine additional targeted-next-generation sequencing (NGS) information. Studies based on ulc-WGS remain scarce.
The study consisted of two stages: methodology establishment and validation. An AI model, named PGT-Plus, was developed using 653 samples with known ploidy status, which was further validated using 792 different ploidy status samples. In the clinical application stage, the approach was used to analyse the ploidy status of 19 103 normally fertilized PGT blastocysts and 140 single pronucleus (1PN)-derived blastocysts collected between May 2022 and December 2023. All blastocysts were tested using trophectoderm biopsy and NGS.
PARTICIPANTS/MATERIALS SETTING METHODS: The methodology is based on the ulc-WGS data. First, based on samples with known ploidy status: the heterozygosity rate of high-frequency biallelic SNPs, the likelihood ratio (LLR) of alleles was calculated under different assumptions ('both parental homologs' [BPH] from a single parent, 'single parental homolog' [SPH] from each parent, disomy, and monosomy) by leveraging allele frequencies and linkage disequilibrium (LD) measured in the 1000 genomes project database. Twenty-three continuous candidate features derived from heterozygosity rates and LLRs of chromosomes or selected windows were included to establish the ploidy prediction AI model. Gini importance analysis and multicollinearity mitigation was performed for feature selection, then the performance of Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression for modelling was compared. Subsequently, the parameter optimization was performed based on the RF model. Ploidy constitution concordance was evaluated in known ploidy status samples. The frequency of abnormal ploidy in normal fertilized PGT blastocysts and 1PN-derived blastocysts (including conventional IVF and ICSI) was evaluated.
Eleven features were collected for model architecture compared to SVM and Logistic Regression; RF achieved superior performance for ploidy detection. The AI model achieved an AUC of 1 for genome-wide-uniparental diploidy (GW-UPD), 1 for triploidy, and 0.99 for diploidy. For the 792 validation samples, 99.5% of samples were successfully detected using the AI model, and the model showed 100% accuracy for ploidy classification. In the clinical application stage, out of 19 103 PGT samples, 19 069 were successfully analysed using the model, with 110 (0.57%) identified as having abnormal ploidy embryos. Among these, 12.7% (14/110) were identified as GW-UPD, and 87.3% (96/110) were triploid. Among 5563 diploid blastocysts transferred, 3478 clinical pregnancies were achieved. Subsequent ploidy analysis was performed for 217 spontaneous abortion and 935 prenatal diagnostic samples, and no abnormal ploidy was identified. Furthermore, of the 140 1PN embryos tested, 40 (28.6%) exhibited GW-UPD, 3 (2.1%) exhibited triploidy, and 97 (69.3%) were determined to be biparental and normally fertilized. Among the 97 biparental embryos, 46 were diploid, 11 were mosaic, and 40 were aneuploid. In terms of the insemination pattern, the percentage of abnormal ploidy in ICSI was significantly higher than in conventional IVF ( < 0.01, 37.1% vs. 2.9%, respectively). With full informed consent, 20 patients without euploidy from normal fertilization chose 1PN-derived biparental and diploid blastocysts to transfer, resulting in 10 clinical pregnancies and 9 ongoing pregnancies.
LARGE-SCALE DATA: N/A.
Some rare ploidy abnormalities, such as polyploidy with an equal number of identical sets of chromosomes and ploidy mosaicism cannot be accurately identified. Moreover, the origin of abnormal ploidy was not identified due to the unavailability of DNA from both parents.
The PGT-Plus AI model provides a ploidy evaluation method based on the conventional PGT-A data and integrates directly into standard PGT-A workflows. Clinical utility results suggest that the model is a valuable tool for identifying embryos with abnormal ploidy in PGT-A and rescuing normal diploid embryos from abnormally fertilized embryos. These findings demonstrate that PGT-Plus significantly enhances the diagnostic accuracy of PGT.
STUDY FUNDING/COMPETING INTERESTS: This study was supported by grants from Major Scientific Program of CITIC Group (No. 2023ZXKYB34100, to Ge.L.), Hunan Provincial Grant for Innovative Province Construction (2019SK4012), Hunan Xiangjiang New District (Changsha High-tech Zone) key core technology research project in 2023, and Science Foundation of Hunan Province (Grant 2023JJ30422). All authors declared no conflicts of interest..
超低覆盖度全基因组测序（ulc-WGS）能否在植入前基因检测（PGT）期间准确识别异常倍性？
基于人工智能（AI）的PGT-Plus模型在倍性检测中表现出高准确性，提供了一种具有成本效益的解决方案，增强了PGT的临床实用性。
主要的非整倍体PGT可以识别染色体非整倍体，但无法确定倍性状态。移植具有倍性异常的胚胎可能会导致流产和葡萄胎妊娠。另一方面，在辅助生殖技术（ART）中，通过对合子阶段的原核进行形态学评估来评估受精情况。然而，其在预测异常倍性状态方面特异性较低，被认为受精异常的胚胎可能会产生健康的妊娠。在PGT-A中准确识别异常倍性可以解决当前的局限性并扩大PGT-A的应用范围。多项研究已识别出倍性异常；然而，这些研究主要基于单核苷酸多态性（SNP）阵列，或需要结合额外的靶向新一代测序（NGS）信息。基于ulc-WGS的研究仍然很少。
该研究包括两个阶段：方法建立和验证。使用653个已知倍性状态的样本开发了一个名为PGT-Plus的AI模型，并用792个不同倍性状态的样本对其进行了进一步验证。在临床应用阶段，该方法用于分析2022年5月至2023年12月期间收集的19103个正常受精的PGT囊胚和140个单原核（1PN）来源的囊胚的倍性状态。所有囊胚均采用滋养外胚层活检和NGS进行检测。
参与者/材料设置方法：该方法基于ulc-WGS数据。首先，基于已知倍性状态的样本：利用1000基因组计划数据库中测量的等位基因频率和连锁不平衡（LD），计算高频双等位基因SNP的杂合率，以及在不同假设（来自单亲的“双亲同源染色体”[BPH]、来自双亲的“单亲同源染色体”[SPH]、二倍体和单体）下等位基因的似然比（LLR）。纳入从染色体或选定窗口的杂合率和LLR得出的23个连续候选特征，以建立倍性预测AI模型。对特征进行基尼重要性分析和多重共线性缓解以进行特征选择，然后比较随机森林（RF）、支持向量机（SVM）和逻辑回归用于建模的性能。随后，基于RF模型进行参数优化。在已知倍性状态的样本中评估倍性构成一致性。评估正常受精的PGT囊胚和1PN来源的囊胚（包括传统体外受精和卵胞浆内单精子注射）中异常倍性的频率。
与SVM和逻辑回归相比，为模型构建收集了11个特征；RF在倍性检测方面表现出卓越性能。该AI模型对全基因组单亲二倍体（GW-UPD）的曲线下面积（AUC）为1，对三倍体为1，对二倍体为0.99。对于792个验证样本，使用AI模型成功检测出99.5%的样本，且该模型在倍性分类方面显示出100%的准确性。在临床应用阶段，在19103个PGT样本中，使用该模型成功分析了19069个样本，其中110个（0.57%）被鉴定为具有异常倍性胚胎。其中，12.7%（14/110）被鉴定为GW-UPD，87.3%（96/110）为三倍体。在移植的5563个二倍体囊胚中，实现了3478例临床妊娠。对217例自然流产和935例产前诊断样本进行了后续倍性分析，未发现异常倍性。此外，在检测的140个1PN胚胎中，40个（28.6%）表现为GW-UPD，3个（2.1%）表现为三倍体，97个（69.3%）被确定为双亲受精且正常。在97个双亲胚胎中，46个为二倍体，11个为嵌合体，40个为非整倍体。就受精方式而言，卵胞浆内单精子注射中异常倍性的百分比显著高于传统体外受精（<0.01，分别为37.1%和2.9%）。在获得充分知情同意后，20例未获得正常受精的整倍体患者选择移植1PN来源的双亲二倍体囊胚，结果有10例临床妊娠和9例持续妊娠。
无。
一些罕见的倍性异常，如具有相同染色体组数相等的多倍体和倍性嵌合体，无法准确识别。此外，由于无法获得双亲的DNA，异常倍性的起源未被确定。
PGT-Plus AI模型基于传统的PGT-A数据提供了一种倍性评估方法，并可直接整合到标准的PGT-A工作流程中。临床实用性结果表明，该模型是在PGT-A中识别具有异常倍性胚胎并从异常受精胚胎中挽救正常二倍体胚胎的有价值工具。这些发现表明，PGT-Plus显著提高了PGT的诊断准确性。
研究资金/竞争利益：本研究得到了中信集团重大科研项目（编号2023ZXKYB34100，资助给葛.L.）、湖南省创新型省份建设专项（2019SK4012）、2023年湖南湘江新区（长沙高新区）关键核心技术研究项目以及湖南省自然科学基金（资助编号2023JJ30422）的资助。所有作者均声明无利益冲突。
