Benchmarking autosomal recessive disease prevalence estimation from allele frequencies against newborn screening data

Accurate estimates for the prevalence of rare congenital diseases are critical for understanding disease epidemiology and enabling drug development. Prevalence estimates can inform public health investment, identify communities with high disease burden or underdiagnosis, and reveal areas of unmet clinical need. With the advent of global-scale biobanks, genetics-based models to estimate the prevalence of disease have become viable. Autosomal recessive (AR) rare diseases are particularly tractable for this approach given that disease prevalence can be estimated from the pathogenic allele frequency (AF) in carriers from unaffected populations. Despite the usefulness of such estimates, this approach has not been validated against real-world clinical datasets at scale. Newborn screening (NBS) programs, which test newborns for a panel of neonatal diseases using quantitative diagnostic methods, provide a comparator for birth prevalence with low ascertainment bias, large sample size, and low diagnostic variability. NBS datasets thus offer a uniquely robust benchmark to evaluate and improve the accuracy of AR genetic prevalence models. Here we explore the feasibility, utility, and pitfalls of estimating AR birth prevalence using genetic and NBS data. We applied a genetic model to estimate birth prevalence for 28 AR diseases consistently present on NBS panels and benchmarked these against reported NBS birth prevalence in more than 12 million newborns in the United States. We found concordance between the genetic estimate and NBS was impacted by the population database used to derive AF, ancestry-matching methodology, and pathogenic variant inclusion criteria. Incorporating these refinements, we demonstrate that a genetics-first approach can provide order-of-magnitude estimates of AR disease birth prevalence for nearly all tested diseases (25/28; 89%). However, we note a general underestimate of the genetic prevalence, suggesting identifying additional pathogenic variants would improve the concordance with NBS. Further, we also assessed the impact of epidemiological and genetic variables, highlighting diseases where genetic prevalence estimates may not be suitable.