Over 30 international research studies and commercial laboratories are exploring the use of genomic sequencing to screen apparently healthy newborns for genetic disorders. These programs have individualized processes for determining which genes and genetic disorders are queried and reported in newborns. We compared lists of genes from 26 research and commercial newborn screening programs and found substantial heterogeneity among the genes included. A total of 1,750 genes were included in at least one newborn genome sequencing program, but only 74 genes were included on >80% of gene lists, 16 of which are not associated with conditions on the Recommended Uniform Screening Panel. We used a linear regression model to explore factors related to the inclusion of individual genes across programs, finding that a high evidence base as well as treatment efficacy were two of the most important factors for inclusion. We applied a machine learning model to predict how suitable a gene is for newborn sequencing. As knowledge about and treatments for genetic disorders expand, this model provides a dynamic tool to reassess genes for newborn screening implementation. This study highlights the complex landscape of gene list curation among genomic newborn screening programs and proposes an empirical path forward for determining the genes and disorders of highest priority for newborn screening programs.

中文翻译：

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确定预测纳入新生儿基因组测序计划的遗传性疾病的特征

超过 30 个国际研究和商业实验室正在探索使用基因组测序来筛查表面健康的新生儿是否有遗传疾病。这些程序具有个性化的流程，用于确定在新生儿中查询和报告哪些基因和遗传疾病。我们比较了 26 个研究和商业新生儿筛查项目的基因列表，发现所包含的基因之间存在很大的异质性。至少一个新生儿基因组测序计划总共包含 1,750 个基因，但只有 74 个基因包含在 >80% 的基因列表中，其中 16 个基因与推荐统一筛查小组中的条件无关。我们使用线性回归模型来探索与跨项目纳入单个基因相关的因素，发现高证据基础和治疗功效是纳入的两个最重要的因素。我们应用机器学习模型来预测基因是否适合新生儿测序。随着有关遗传性疾病的知识和治疗方法的不断扩展，该模型提供了一种动态工具来重新评估基因以实施新生儿筛查。这项研究强调了基因组新生儿筛查计划中基因列表管理的复杂情况，并提出了确定新生儿筛查计划最优先的基因和疾病的经验路径。