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Abstract：
In China, ~1,072,100 small for gestational age (SGA) births occur annually. These SGA newborns are a high-risk population of developmental delay. Our study aimed to evaluate the genetic profile of SGA newborns in the newborn intensive care unit (NICU) and establish a prognosis prediction model by combining clinical and genetic factors.
A cohort of 723 SGA and 1317 appropriate for gestational age (AGA) newborns were recruited between June 2018 and June 2020. Clinical exome sequencing was performed for each newborn. The gene-based rare-variant collapsing analyses and the gene burden test were applied to identify the risk genes for SGA and SGA with poor prognosis. The Gradient Boosting Machine framework was used to generate two models to predict the prognosis of SGA. The performance of two models were validated with an independent cohort of 115 SGA newborns without genetic diagnosis from July 2020 to April 2022. All newborns in this study were recruited through the China Neonatal Genomes Project (CNGP) and were hospitalized in NICU, Children\'s Hospital of Fudan University, Shanghai, China.
Among the 723 SGA newborns, 88(12.2%) received genetic diagnosis, including 42(47.7%) with monogenic diseases and 46(52.3%) with chromosomal abnormalities. SGA with genetic diagnosis showed higher rates in severe SGA(54.5% vs. 41.9%, P=0.0025) than SGA without genetic diagnosis. SGA with chromosomal abnormalities showed higher incidences of physical and neurodevelopmental delay compared to those with monogenic diseases (45.7% vs. 19.0%, P=0.012). We filtered out 3 genes (ITGB4, TXNRD2, RRM2B) as potential causative genes for SGA and 1 gene (ADIPOQ) as potential causative gene for SGA with poor prognosis. The model integrating clinical and genetic factors demonstrated a higher area under the receiver operating characteristic curve (AUC) over the model based solely on clinical factors in both the SGA-model generation dataset (AUC=0.9[95% confidence interval 0.84-0.96] vs. AUC=0.74 [0.64-0.84]; P=0.00196) and the independent SGA-validation dataset (AUC=0.76 [0.6-0.93] vs. AUC=0.53[0.29-0.76]; P=0.0117).
SGA newborns in NICU presented with roughly equal proportions of monogenic and chromosomal abnormalities. Chromosomal disorders were associated with poorer prognosis. The rare-variant collapsing analyses studies have the ability to identify potential causative factors associated with growth and development. The SGA prognosis prediction model integrating genetic and clinical factors outperformed that relying solely on clinical factors. The application of genetic sequencing in hospitalized SGA newborns may improve early genetic diagnosis and prognosis prediction.
A cohort of 723 SGA and 1317 appropriate for gestational age (AGA) newborns were recruited between June 2018 and June 2020. Clinical exome sequencing was performed for each newborn. The gene-based rare-variant collapsing analyses and the gene burden test were applied to identify the risk genes for SGA and SGA with poor prognosis. The Gradient Boosting Machine framework was used to generate two models to predict the prognosis of SGA. The performance of two models were validated with an independent cohort of 115 SGA newborns without genetic diagnosis from July 2020 to April 2022. All newborns in this study were recruited through the China Neonatal Genomes Project (CNGP) and were hospitalized in NICU, Children\'s Hospital of Fudan University, Shanghai, China.
Among the 723 SGA newborns, 88(12.2%) received genetic diagnosis, including 42(47.7%) with monogenic diseases and 46(52.3%) with chromosomal abnormalities. SGA with genetic diagnosis showed higher rates in severe SGA(54.5% vs. 41.9%, P=0.0025) than SGA without genetic diagnosis. SGA with chromosomal abnormalities showed higher incidences of physical and neurodevelopmental delay compared to those with monogenic diseases (45.7% vs. 19.0%, P=0.012). We filtered out 3 genes (ITGB4, TXNRD2, RRM2B) as potential causative genes for SGA and 1 gene (ADIPOQ) as potential causative gene for SGA with poor prognosis. The model integrating clinical and genetic factors demonstrated a higher area under the receiver operating characteristic curve (AUC) over the model based solely on clinical factors in both the SGA-model generation dataset (AUC=0.9[95% confidence interval 0.84-0.96] vs. AUC=0.74 [0.64-0.84]; P=0.00196) and the independent SGA-validation dataset (AUC=0.76 [0.6-0.93] vs. AUC=0.53[0.29-0.76]; P=0.0117).
SGA newborns in NICU presented with roughly equal proportions of monogenic and chromosomal abnormalities. Chromosomal disorders were associated with poorer prognosis. The rare-variant collapsing analyses studies have the ability to identify potential causative factors associated with growth and development. The SGA prognosis prediction model integrating genetic and clinical factors outperformed that relying solely on clinical factors. The application of genetic sequencing in hospitalized SGA newborns may improve early genetic diagnosis and prognosis prediction.
摘要：
背景：在中国，每年发生〜1,072,100胎龄小（SGA）出生。这些SGA新生儿是发育迟缓的高危人群。我们的研究旨在评估新生儿重症监护病房（NICU）中SGA新生儿的遗传特征，并结合临床和遗传因素建立预后预测模型。
方法：在2018年6月至2020年6月之间招募了723名SGA和1317名适合胎龄（AGA）的新生儿。对每个新生儿进行临床外显子组测序。应用基于基因的罕见变异塌陷分析和基因负荷测试来确定SGA和SGA预后不良的风险基因。梯度提升机框架用于生成两个模型来预测SGA的预后。从2020年7月至2022年4月，对115名没有遗传诊断的SGA新生儿的独立队列进行了验证。本研究中的所有新生儿均通过中国新生儿基因组计划（CNGP）招募，并在NICU住院，复旦大学附属儿童医院,上海,中国。
结果：在723名SGA新生儿中，88（12.2%）接受基因诊断，包括42例（47.7％）单基因疾病和46例（52.3％）染色体异常。经基因诊断的SGA在严重的SGA中显示出较高的发生率（54.5％vs.41.9%,P=0.0025)比SGA无基因诊断。与单基因疾病相比，具有染色体异常的SGA显示出更高的身体和神经发育迟缓发生率（45.7％vs.19.0%,P=0.012)。我们筛选出3个基因（ITGB4，TXNRD2，RRM2B）作为SGA的潜在致病基因，1个基因（ADIPOQ）作为SGA的潜在致病基因，预后不良。在两个SGA模型生成数据集中，整合临床和遗传因素的模型显示出比仅基于临床因素的模型更高的受试者工作特征曲线下面积（AUC=0.9[95％置信区间0.84-0.96]vs.AUC=0.74[0.64-0.84]；P=0.00196）和独立的SGA验证数据集（AUC=0.76[0.6-0.93]vs.AUC=0.53[0.29-0.76];P=0.0117)。
结论：NICU中的SGA新生儿表现出大致相等比例的单基因和染色体异常。染色体疾病与预后较差相关。稀有变异塌陷分析研究能够识别与生长和发育相关的潜在致病因素。整合遗传和临床因素的SGA预后预测模型优于仅依赖临床因素的预测模型。基因测序在住院SGA新生儿中的应用可提高早期基因诊断和预后预测。
方法：在2018年6月至2020年6月之间招募了723名SGA和1317名适合胎龄（AGA）的新生儿。对每个新生儿进行临床外显子组测序。应用基于基因的罕见变异塌陷分析和基因负荷测试来确定SGA和SGA预后不良的风险基因。梯度提升机框架用于生成两个模型来预测SGA的预后。从2020年7月至2022年4月，对115名没有遗传诊断的SGA新生儿的独立队列进行了验证。本研究中的所有新生儿均通过中国新生儿基因组计划（CNGP）招募，并在NICU住院，复旦大学附属儿童医院,上海,中国。
结果：在723名SGA新生儿中，88（12.2%）接受基因诊断，包括42例（47.7％）单基因疾病和46例（52.3％）染色体异常。经基因诊断的SGA在严重的SGA中显示出较高的发生率（54.5％vs.41.9%,P=0.0025)比SGA无基因诊断。与单基因疾病相比，具有染色体异常的SGA显示出更高的身体和神经发育迟缓发生率（45.7％vs.19.0%,P=0.012)。我们筛选出3个基因（ITGB4，TXNRD2，RRM2B）作为SGA的潜在致病基因，1个基因（ADIPOQ）作为SGA的潜在致病基因，预后不良。在两个SGA模型生成数据集中，整合临床和遗传因素的模型显示出比仅基于临床因素的模型更高的受试者工作特征曲线下面积（AUC=0.9[95％置信区间0.84-0.96]vs.AUC=0.74[0.64-0.84]；P=0.00196）和独立的SGA验证数据集（AUC=0.76[0.6-0.93]vs.AUC=0.53[0.29-0.76];P=0.0117)。
结论：NICU中的SGA新生儿表现出大致相等比例的单基因和染色体异常。染色体疾病与预后较差相关。稀有变异塌陷分析研究能够识别与生长和发育相关的潜在致病因素。整合遗传和临床因素的SGA预后预测模型优于仅依赖临床因素的预测模型。基因测序在住院SGA新生儿中的应用可提高早期基因诊断和预后预测。
