Genomic sequencing is poised to expand newborn screening for treatable childhood-onset disorders. Over 30 international research studies and companies are exploring its use, collectively aiming to screen more than 500,000 infants. A key challenge is determining which genes to include in screening. Among 27 newborn sequencing programs, the number of genes analyzed ranged from 134 to 4,299, with only 74 genes included by over 80% of programs. To understand this variability, we assembled a dataset with 25 characteristics of 4,389 genes included in any program and used a multivariate regression analysis to identify characteristics associated with inclusion across programs. These characteristics included presence on the US Recommended Uniform Screening panel, evidence regarding the natural history of disease, and efficacy of treatment. We then used a machine learning model to generate a ranked list of genes, offering a data-driven approach to the future prioritization of disorders for public health newborn screening efforts.